Evaluation of Generation Alternation Models in Evolutionary Robotics

NASA Astrophysics Data System (ADS)

Oiso, Masashi; Matsumura, Yoshiyuki; Yasuda, Toshiyuki; Ohkura, Kazuhiro

For efficient implementation of Evolutionary Algorithms (EA) to a desktop grid computing environment, we propose a new generation alternation model called Grid-Oriented-Deletion (GOD) based on comparison with the conventional techniques. In previous research, generation alternation models are generally evaluated by using test functions. However, their exploration performance on the real problems such as Evolutionary Robotics (ER) has not been made very clear yet. Therefore we investigate the relationship between the exploration performance of EA on an ER problem and its generation alternation model. We applied four generation alternation models to the Evolutionary Multi-Robotics (EMR), which is the package-pushing problem to investigate their exploration performance. The results show that GOD is more effective than the other conventional models.

Adaptive Architectures for Effects Based Operations

DTIC Science & Technology

2006-08-12

laLb c d elfl I A IB Ic d e f Parent 2 Figure 3: One-Point Crossover System Architectures Lab 85 Aug-06 6.4. ECAD -EA Methodology The previous two...that accomplishes this task is termed as ECAD -EA (Effective Courses of Action Determination Using Evolutionary Algorithms). Besides a completely...items are given below followed by their explanations, while Figure 4 shows the inputs and outputs of the ECAD -EA methodology in the form of a block

Enhancements of evolutionary algorithm for the complex requirements of a nurse scheduling problem

NASA Astrophysics Data System (ADS)

Tein, Lim Huai; Ramli, Razamin

2014-12-01

Over the years, nurse scheduling is a noticeable problem that is affected by the global nurse turnover crisis. The more nurses are unsatisfied with their working environment the more severe the condition or implication they tend to leave. Therefore, the current undesirable work schedule is partly due to that working condition. Basically, there is a lack of complimentary requirement between the head nurse's liability and the nurses' need. In particular, subject to highly nurse preferences issue, the sophisticated challenge of doing nurse scheduling is failure to stimulate tolerance behavior between both parties during shifts assignment in real working scenarios. Inevitably, the flexibility in shifts assignment is hard to achieve for the sake of satisfying nurse diverse requests with upholding imperative nurse ward coverage. Hence, Evolutionary Algorithm (EA) is proposed to cater for this complexity in a nurse scheduling problem (NSP). The restriction of EA is discussed and thus, enhancement on the EA operators is suggested so that the EA would have the characteristic of a flexible search. This paper consists of three types of constraints which are the hard, semi-hard and soft constraints that can be handled by the EA with enhanced parent selection and specialized mutation operators. These operators and EA as a whole contribute to the efficiency of constraint handling, fitness computation as well as flexibility in the search, which correspond to the employment of exploration and exploitation principles.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Park, Won-Woong; Kim, Dong-Kyu; Im, Yong-Taek; Bureerat, Sujin; Kwon, Hyuck-Cheol; Chun, Myung-Sik

2015-04-01

This article proposes an efficient metaheuristic based on hybridization of teaching-learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching-learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

NASA Astrophysics Data System (ADS)

Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad

2008-04-01

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology transition goals.

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks.

PubMed

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-08-20

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ , where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority.

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

NASA Astrophysics Data System (ADS)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary-handling techniques, and sampling schemes, and has good potential to be used in Water-Energy system optimal operation and management.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

NASA Astrophysics Data System (ADS)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision variables and more complexity. In terms of computational time, PSO and SIMPSA are the fastest. SCE needs the highest computational time, even up to four times in comparison to the fastest EAs. CACO and PSO can be recommended for application in CGMPs, in terms of both abovementioned criteria.

Applications of Evolutionary Technology to Manufacturing and Logistics Systems : State-of-the Art Survey

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin

Many combinatorial optimization problems from industrial engineering and operations research in real-world are very complex in nature and quite hard to solve them by conventional techniques. Since the 1960s, there has been an increasing interest in imitating living beings to solve such kinds of hard combinatorial optimization problems. Simulating the natural evolutionary process of human beings results in stochastic optimization techniques called evolutionary algorithms (EAs), which can often outperform conventional optimization methods when applied to difficult real-world problems. In this survey paper, we provide a comprehensive survey of the current state-of-the-art in the use of EA in manufacturing and logistics systems. In order to demonstrate the EAs which are powerful and broadly applicable stochastic search and optimization techniques, we deal with the following engineering design problems: transportation planning models, layout design models and two-stage logistics models in logistics systems; job-shop scheduling, resource constrained project scheduling in manufacturing system.

Evolutionary Beamforming Optimization for Radio Frequency Charging in Wireless Rechargeable Sensor Networks

PubMed Central

Yao, Ke-Han; Jiang, Jehn-Ruey; Tsai, Chung-Hsien; Wu, Zong-Syun

2017-01-01

This paper investigates how to efficiently charge sensor nodes in a wireless rechargeable sensor network (WRSN) with radio frequency (RF) chargers to make the network sustainable. An RF charger is assumed to be equipped with a uniform circular array (UCA) of 12 antennas with the radius λ, where λ is the RF wavelength. The UCA can steer most RF energy in a target direction to charge a specific WRSN node by the beamforming technology. Two evolutionary algorithms (EAs) using the evolution strategy (ES), namely the Evolutionary Beamforming Optimization (EBO) algorithm and the Evolutionary Beamforming Optimization Reseeding (EBO-R) algorithm, are proposed to nearly optimize the power ratio of the UCA beamforming peak side lobe (PSL) and the main lobe (ML) aimed at the given target direction. The proposed algorithms are simulated for performance evaluation and are compared with a related algorithm, called Particle Swarm Optimization Gravitational Search Algorithm-Explore (PSOGSA-Explore), to show their superiority. PMID:28825648

Evolutionary optimization of radial basis function classifiers for data mining applications.

PubMed

Buchtala, Oliver; Klimek, Manuel; Sick, Bernhard

2005-10-01

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

NASA Astrophysics Data System (ADS)

Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B.

2018-01-01

Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2 (110 )-(4 ×1 ) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

Using evolutionary algorithms for fitting high-dimensional models to neuronal data.

PubMed

Svensson, Carl-Magnus; Coombes, Stephen; Peirce, Jonathan Westley

2012-04-01

In the study of neurosciences, and of complex biological systems in general, there is frequently a need to fit mathematical models with large numbers of parameters to highly complex datasets. Here we consider algorithms of two different classes, gradient following (GF) methods and evolutionary algorithms (EA) and examine their performance in fitting a 9-parameter model of a filter-based visual neuron to real data recorded from a sample of 107 neurons in macaque primary visual cortex (V1). Although the GF method converged very rapidly on a solution, it was highly susceptible to the effects of local minima in the error surface and produced relatively poor fits unless the initial estimates of the parameters were already very good. Conversely, although the EA required many more iterations of evaluating the model neuron's response to a series of stimuli, it ultimately found better solutions in nearly all cases and its performance was independent of the starting parameters of the model. Thus, although the fitting process was lengthy in terms of processing time, the relative lack of human intervention in the evolutionary algorithm, and its ability ultimately to generate model fits that could be trusted as being close to optimal, made it far superior in this particular application than the gradient following methods. This is likely to be the case in many further complex systems, as are often found in neuroscience.

An improved parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

PubMed

Biswas, Subhodip; Kundu, Souvik; Das, Swagatam

2014-10-01

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

Analysing the Effect of Demand Uncertainty in Dynamic Pricing with EAs

NASA Astrophysics Data System (ADS)

Shakya, Siddhartha; Oliveira, Fernando; Owusu, Gilbert

Dynamic pricing is a pricing strategy where a firm adjust the price for their products and services as a function of its perceived demand at different times. In this paper, we show how Evolutionary algorithms (EA) can be used to analyse the effect of demand uncertainty in dynamic pricing. The experiments are conducted in a range of dynamic pricing problems considering a number of different stochastic scenarios with a number of different EAs. The results are analysed, which suggest that higher demand fluctuation may not have adverse effect to the profit in comparison to the lower demand fluctuation, and that the reliability of EA for finding accurate policy could be higher when there is higher fluctuation then when there is lower fluctuation.

Solving Large-scale Spatial Optimization Problems in Water Resources Management through Spatial Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Wang, J.; Cai, X.

2007-12-01

A water resources system can be defined as a large-scale spatial system, within which distributed ecological system interacts with the stream network and ground water system. Water resources management, the causative factors and hence the solutions to be developed have a significant spatial dimension. This motivates a modeling analysis of water resources management within a spatial analytical framework, where data is usually geo- referenced and in the form of a map. One of the important functions of Geographic information systems (GIS) is to identify spatial patterns of environmental variables. The role of spatial patterns in water resources management has been well established in the literature particularly regarding how to design better spatial patterns for satisfying the designated objectives of water resources management. Evolutionary algorithms (EA) have been demonstrated to be successful in solving complex optimization models for water resources management due to its flexibility to incorporate complex simulation models in the optimal search procedure. The idea of combining GIS and EA motivates the development and application of spatial evolutionary algorithms (SEA). SEA assimilates spatial information into EA, and even changes the representation and operators of EA. In an EA used for water resources management, the mathematical optimization model should be modified to account the spatial patterns; however, spatial patterns are usually implicit, and it is difficult to impose appropriate patterns to spatial data. Also it is difficult to express complex spatial patterns by explicit constraints included in the EA. The GIS can help identify the spatial linkages and correlations based on the spatial knowledge of the problem. These linkages are incorporated in the fitness function for the preference of the compatible vegetation distribution. Unlike a regular GA for spatial models, the SEA employs a special hierarchical hyper-population and spatial genetic operators to represent spatial variables in a more efficient way. The hyper-population consists of a set of populations, which correspond to the spatial distributions of the individual agents (organisms). Furthermore spatial crossover and mutation operators are designed in accordance with the tree representation and then applied to both organisms and populations. This study applies the SEA to a specific problem of water resources management- maximizing the riparian vegetation coverage in accordance with the distributed groundwater system in an arid region. The vegetation coverage is impacted greatly by the nonlinear feedbacks and interactions between vegetation and groundwater and the spatial variability of groundwater. The SEA is applied to search for an optimal vegetation configuration compatible to the groundwater flow. The results from this example demonstrate the effectiveness of the SEA. Extension of the algorithm for other water resources management problems is discussed.

Circuit Design Optimization Using Genetic Algorithm with Parameterized Uniform Crossover

NASA Astrophysics Data System (ADS)

Bao, Zhiguo; Watanabe, Takahiro

Evolvable hardware (EHW) is a new research field about the use of Evolutionary Algorithms (EAs) to construct electronic systems. EHW refers in a narrow sense to use evolutionary mechanisms as the algorithmic drivers for system design, while in a general sense to the capability of the hardware system to develop and to improve itself. Genetic Algorithm (GA) is one of typical EAs. We propose optimal circuit design by using GA with parameterized uniform crossover (GApuc) and with fitness function composed of circuit complexity, power, and signal delay. Parameterized uniform crossover is much more likely to distribute its disruptive trials in an unbiased manner over larger portions of the space, then it has more exploratory power than one and two-point crossover, so we have more chances of finding better solutions. Its effectiveness is shown by experiments. From the results, we can see that the best elite fitness, the average value of fitness of the correct circuits and the number of the correct circuits of GApuc are better than that of GA with one-point crossover or two-point crossover. The best case of optimal circuits generated by GApuc is 10.18% and 6.08% better in evaluating value than that by GA with one-point crossover and two-point crossover, respectively.

Evolutionary algorithms for multi-objective optimization: fuzzy preference aggregation and multisexual EAs

NASA Astrophysics Data System (ADS)

Bonissone, Stefano R.

2001-11-01

There are many approaches to solving multi-objective optimization problems using evolutionary algorithms. We need to select methods for representing and aggregating preferences, as well as choosing strategies for searching in multi-dimensional objective spaces. First we suggest the use of linguistic variables to represent preferences and the use of fuzzy rule systems to implement tradeoff aggregations. After a review of alternatives EA methods for multi-objective optimizations, we explore the use of multi-sexual genetic algorithms (MSGA). In using a MSGA, we need to modify certain parts of the GAs, namely the selection and crossover operations. The selection operator groups solutions according to their gender tag to prepare them for crossover. The crossover is modified by appending a gender tag at the end of the chromosome. We use single and double point crossovers. We determine the gender of the offspring by the amount of genetic material provided by each parent. The parent that contributed the most to the creation of a specific offspring determines the gender that the offspring will inherit. This is still a work in progress, and in the conclusion we examine many future extensions and experiments.

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms.

PubMed

Chan, Wai Sum; Recknagel, Friedrich; Cao, Hongqing; Park, Ho-Dong

2007-05-01

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.

Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification.

PubMed

Wang, Yubo; Veluvolu, Kalyana C

2017-01-01

The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.

Evolving locomotion for a 12-DOF quadruped robot in simulated environments.

PubMed

Klaus, Gordon; Glette, Kyrre; Høvin, Mats

2013-05-01

We demonstrate the power of evolutionary robotics (ER) by comparing to a more traditional approach its performance and cost on the task of simulated robot locomotion. A novel quadruped robot is introduced, the legs of which - each having three non-coplanar degrees of freedom - are very maneuverable. Using a simplistic control architecture and a physics simulation of the robot, gaits are designed both by hand and using a highly parallel evolutionary algorithm (EA). It is found that the EA produces, in a small fraction of the time that takes to design by hand, gaits that travel at two to four times the speed of the hand-designed one. The flexibility of this approach is demonstrated by applying it across a range of differently configured simulators. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

NASA Astrophysics Data System (ADS)

Pirpinia, Kleopatra; Bosman, Peter A. N.; Sonke, Jan-Jakob; van Herk, Marcel; Alderliesten, Tanja

2015-03-01

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

Exploring the Pareto frontier using multisexual evolutionary algorithms: an application to a flexible manufacturing problem

NASA Astrophysics Data System (ADS)

Bonissone, Stefano R.; Subbu, Raj

2002-12-01

In multi-objective optimization (MOO) problems we need to optimize many possibly conflicting objectives. For instance, in manufacturing planning we might want to minimize the cost and production time while maximizing the product's quality. We propose the use of evolutionary algorithms (EAs) to solve these problems. Solutions are represented as individuals in a population and are assigned scores according to a fitness function that determines their relative quality. Strong solutions are selected for reproduction, and pass their genetic material to the next generation. Weak solutions are removed from the population. The fitness function evaluates each solution and returns a related score. In MOO problems, this fitness function is vector-valued, i.e. it returns a value for each objective. Therefore, instead of a global optimum, we try to find the Pareto-optimal or non-dominated frontier. We use multi-sexual EAs with as many genders as optimization criteria. We have created new crossover and gender assignment functions, and experimented with various parameters to determine the best setting (yielding the highest number of non-dominated solutions.) These experiments are conducted using a variety of fitness functions, and the algorithms are later evaluated on a flexible manufacturing problem with total cost and time minimization objectives.

Computational evolution: taking liberties.

PubMed

Correia, Luís

2010-09-01

Evolution has, for a long time, inspired computer scientists to produce computer models mimicking its behavior. Evolutionary algorithm (EA) is one of the areas where this approach has flourished. EAs have been used to model and study evolution, but they have been especially developed for their aptitude as optimization tools for engineering. Developed models are quite simple in comparison with their natural sources of inspiration. However, since EAs run on computers, we have the freedom, especially in optimization models, to test approaches both realistic and outright speculative, from the biological point of view. In this article, we discuss different common evolutionary algorithm models, and then present some alternatives of interest. These include biologically inspired models, such as co-evolution and, in particular, symbiogenetics and outright artificial operators and representations. In each case, the advantages of the modifications to the standard model are identified. The other area of computational evolution, which has allowed us to study basic principles of evolution and ecology dynamics, is the development of artificial life platforms for open-ended evolution of artificial organisms. With these platforms, biologists can test theories by directly manipulating individuals and operators, observing the resulting effects in a realistic way. An overview of the most prominent of such environments is also presented. If instead of artificial platforms we use the real world for evolving artificial life, then we are dealing with evolutionary robotics (ERs). A brief description of this area is presented, analyzing its relations to biology. Finally, we present the conclusions and identify future research avenues in the frontier of computation and biology. Hopefully, this will help to draw the attention of more biologists and computer scientists to the benefits of such interdisciplinary research.

Courses of action for effects based operations using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Haider, Sajjad; Levis, Alexander H.

2006-05-01

This paper presents an Evolutionary Algorithms (EAs) based approach to identify effective courses of action (COAs) in Effects Based Operations. The approach uses Timed Influence Nets (TINs) as the underlying mathematical model to capture a dynamic uncertain situation. TINs provide a concise graph-theoretic probabilistic approach to specify the cause and effect relationships that exist among the variables of interest (actions, desired effects, and other uncertain events) in a problem domain. The purpose of building these TIN models is to identify and analyze several alternative courses of action. The current practice is to use trial and error based techniques which are not only labor intensive but also produce sub-optimal results and are not capable of modeling constraints among actionable events. The EA based approach presented in this paper is aimed to overcome these limitations. The approach generates multiple COAs that are close enough in terms of achieving the desired effect. The purpose of generating multiple COAs is to give several alternatives to a decision maker. Moreover, the alternate COAs could be generalized based on the relationships that exist among the actions and their execution timings. The approach also allows a system analyst to capture certain types of constraints among actionable events.

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

NASA Astrophysics Data System (ADS)

Zecchin, A. C.; Simpson, A. R.; Maier, H. R.; Marchi, A.; Nixon, J. B.

2012-09-01

Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.

GAMBIT: A Parameterless Model-Based Evolutionary Algorithm for Mixed-Integer Problems.

PubMed

Sadowski, Krzysztof L; Thierens, Dirk; Bosman, Peter A N

2018-01-01

Learning and exploiting problem structure is one of the key challenges in optimization. This is especially important for black-box optimization (BBO) where prior structural knowledge of a problem is not available. Existing model-based Evolutionary Algorithms (EAs) are very efficient at learning structure in both the discrete, and in the continuous domain. In this article, discrete and continuous model-building mechanisms are integrated for the Mixed-Integer (MI) domain, comprising discrete and continuous variables. We revisit a recently introduced model-based evolutionary algorithm for the MI domain, the Genetic Algorithm for Model-Based mixed-Integer opTimization (GAMBIT). We extend GAMBIT with a parameterless scheme that allows for practical use of the algorithm without the need to explicitly specify any parameters. We furthermore contrast GAMBIT with other model-based alternatives. The ultimate goal of processing mixed dependences explicitly in GAMBIT is also addressed by introducing a new mechanism for the explicit exploitation of mixed dependences. We find that processing mixed dependences with this novel mechanism allows for more efficient optimization. We further contrast the parameterless GAMBIT with Mixed-Integer Evolution Strategies (MIES) and other state-of-the-art MI optimization algorithms from the General Algebraic Modeling System (GAMS) commercial algorithm suite on problems with and without constraints, and show that GAMBIT is capable of solving problems where variable dependences prevent many algorithms from successfully optimizing them.

A bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environments

NASA Astrophysics Data System (ADS)

Ben-Romdhane, Hajer; Krichen, Saoussen; Alba, Enrique

2017-05-01

Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

PubMed

Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

2015-01-01

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system throughput performance.

Computational approaches for understanding the diagnosis and treatment of Parkinson’s disease

PubMed Central

Smith, Stephen L.; Lones, Michael A.; Bedder, Matthew; Alty, Jane E.; Cosgrove, Jeremy; Maguire, Richard J.; Pownall, Mary Elizabeth; Ivanoiu, Diana; Lyle, Camille; Cording, Amy; Elliott, Christopher J.H.

2015-01-01

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson’s disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson’s by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way. PMID:26577157

Multiobjective Optimization of Rocket Engine Pumps Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2001-01-01

A design optimization method for turbopumps of cryogenic rocket engines has been developed. Multiobjective Evolutionary Algorithm (MOEA) is used for multiobjective pump design optimizations. Performances of design candidates are evaluated by using the meanline pump flow modeling method based on the Euler turbine equation coupled with empirical correlations for rotor efficiency. To demonstrate the feasibility of the present approach, a single stage centrifugal pump design and multistage pump design optimizations are presented. In both cases, the present method obtains very reasonable Pareto-optimal solutions that include some designs outperforming the original design in total head while reducing input power by one percent. Detailed observation of the design results also reveals some important design criteria for turbopumps in cryogenic rocket engines. These results demonstrate the feasibility of the EA-based design optimization method in this field.

Finding Mount Everest and handling voids.

PubMed

Storch, Tobias

2011-01-01

Evolutionary algorithms (EAs) are randomized search heuristics that solve problems successfully in many cases. Their behavior is often described in terms of strategies to find a high location on Earth's surface. Unfortunately, many digital elevation models describing it contain void elements. These are elements not assigned an elevation. Therefore, we design and analyze simple EAs with different strategies to handle such partially defined functions. They are experimentally investigated on a dataset describing the elevation of Earth's surface. The largest value found by an EA within a certain runtime is measured, and the median over a few runs is computed and compared for the different EAs. For the dataset, the distribution of void elements seems to be neither random nor adversarial. They are so-called semirandomly distributed. To deepen our understanding of the behavior of the different EAs, they are theoretically considered on well-known pseudo-Boolean functions transferred to partially defined ones. These modifications are also performed in a semirandom way. The typical runtime until an optimum is found by an EA is analyzed, namely bounded from above and below, and compared for the different EAs. We figure out that for the random model it is a good strategy to assume that a void element has a worse function value than all previous elements. Whereas for the adversary model it is a good strategy to assume that a void element has the best function value of all previous elements.

Evolutionary optimization methods for accelerator design

NASA Astrophysics Data System (ADS)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained optimization test problems for EA with a variety of different configurations and suggest optimal default parameter values based on the results. Then we study the performance of the REPA method on the same set of test problems and compare the obtained results with those of several commonly used constrained optimization methods with EA. Based on the obtained results, particularly on the outstanding performance of REPA on test problem that presents significant difficulty for other reviewed EAs, we conclude that the proposed method is useful and competitive. We discuss REPA parameter tuning for difficult problems and critically review some of the problems from the de-facto standard test problem set for the constrained optimization with EA. In order to demonstrate the practical usefulness of the developed method, we study several problems of accelerator design and demonstrate how they can be solved with EAs. These problems include a simple accelerator design problem (design a quadrupole triplet to be stigmatically imaging, find all possible solutions), a complex real-life accelerator design problem (an optimization of the front end section for the future neutrino factory), and a problem of the normal form defect function optimization which is used to rigorously estimate the stability of the beam dynamics in circular accelerators. The positive results we obtained suggest that the application of EAs to problems from accelerator theory can be very beneficial and has large potential. The developed optimization scenarios and tools can be used to approach similar problems.

Physical Properties and Evolutionary States of EA-type Eclipsing Binaries Observed by LAMOST

NASA Astrophysics Data System (ADS)

Qian, S.-B.; Zhang, J.; He, J.-J.; Zhu, L.-Y.; Zhao, E.-G.; Shi, X.-D.; Zhou, X.; Han, Z.-T.

2018-03-01

About 3196 EA-type binaries (EAs) were observed by LAMOST by 2017 June 16 and their spectral types were derived. Meanwhile, the stellar atmospheric parameters of 2020 EAs were determined. In this paper, those EAs are cataloged and their physical properties and evolutionary states are investigated. The period distribution of EAs suggests that the period limit of tidal locking for the close binaries is about 6 days. It is found that the metallicity of EAs is higher than that of EW-type binaries (EWs), indicating that EAs are generally younger than EWs and they are the progenitors of EWs. The metallicities of long-period EWs (0.4< P< 1 days) are the same as those of EAs with the same periods, while their values of Log (g) are usually smaller than those of EAs. These support the evolutionary process that EAs evolve into long-period EWs through the combination of angular momentum loss (AML) via magnetic braking and case A mass transfer. For short-period EWs, their metallicities are lower than those of EAs, while their gravitational accelerations are higher. These reveal that they may be formed from cool short-period EAs through AML via magnetic braking with little mass transfer. For some EWs with high metallicities, they may be contaminated by material from the evolution of unseen neutron stars and black holes or they have third bodies that may help them to form rapidly through a short timescale of pre-contact evolution. The present investigation suggests that the modern EW populations may have formed through a combination of these mechanisms.

Multi-Objective Control Optimization for Greenhouse Environment Using Evolutionary Algorithms

PubMed Central

Hu, Haigen; Xu, Lihong; Wei, Ruihua; Zhu, Bingkun

2011-01-01

This paper investigates the issue of tuning the Proportional Integral and Derivative (PID) controller parameters for a greenhouse climate control system using an Evolutionary Algorithm (EA) based on multiple performance measures such as good static-dynamic performance specifications and the smooth process of control. A model of nonlinear thermodynamic laws between numerous system variables affecting the greenhouse climate is formulated. The proposed tuning scheme is tested for greenhouse climate control by minimizing the integrated time square error (ITSE) and the control increment or rate in a simulation experiment. The results show that by tuning the gain parameters the controllers can achieve good control performance through step responses such as small overshoot, fast settling time, and less rise time and steady state error. Besides, it can be applied to tuning the system with different properties, such as strong interactions among variables, nonlinearities and conflicting performance criteria. The results implicate that it is a quite effective and promising tuning method using multi-objective optimization algorithms in the complex greenhouse production. PMID:22163927

New phases of osmium carbide from evolutionary algorithm and ab initio computations

NASA Astrophysics Data System (ADS)

Fadda, Alessandro; Fadda, Giuseppe

2017-09-01

New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry by using an ad hoc search space and genetic operators. The new OsC2 and Os2C structures have a lower enthalpy than any known so far. Moreover, the layered pattern of OsC2 serves as a blueprint for building new crystals by adding or removing layers of carbon and/or osmium and generating many other Os  +  C structures like Os2C, OsC, OsC2 and OsC4. These again have a lower enthalpy than all the investigated structures, including those of the present work. The mechanical, vibrational and electronic properties are discussed as well.

Distributed autonomous systems: resource management, planning, and control algorithms

NASA Astrophysics Data System (ADS)

Smith, James F., III; Nguyen, ThanhVu H.

2005-05-01

Distributed autonomous systems, i.e., systems that have separated distributed components, each of which, exhibit some degree of autonomy are increasingly providing solutions to naval and other DoD problems. Recently developed control, planning and resource allocation algorithms for two types of distributed autonomous systems will be discussed. The first distributed autonomous system (DAS) to be discussed consists of a collection of unmanned aerial vehicles (UAVs) that are under fuzzy logic control. The UAVs fly and conduct meteorological sampling in a coordinated fashion determined by their fuzzy logic controllers to determine the atmospheric index of refraction. Once in flight no human intervention is required. A fuzzy planning algorithm determines the optimal trajectory, sampling rate and pattern for the UAVs and an interferometer platform while taking into account risk, reliability, priority for sampling in certain regions, fuel limitations, mission cost, and related uncertainties. The real-time fuzzy control algorithm running on each UAV will give the UAV limited autonomy allowing it to change course immediately without consulting with any commander, request other UAVs to help it, alter its sampling pattern and rate when observing interesting phenomena, or to terminate the mission and return to base. The algorithms developed will be compared to a resource manager (RM) developed for another DAS problem related to electronic attack (EA). This RM is based on fuzzy logic and optimized by evolutionary algorithms. It allows a group of dissimilar platforms to use EA resources distributed throughout the group. For both DAS types significant theoretical and simulation results will be presented.

A New Artificial Neural Network Enhanced by the Shuffled Complex Evolution Optimization with Principal Component Analysis (SP-UCI) for Water Resources Management

NASA Astrophysics Data System (ADS)

Hayatbini, N.; Faridzad, M.; Yang, T.; Akbari Asanjan, A.; Gao, X.; Sorooshian, S.

2016-12-01

The Artificial Neural Networks (ANNs) are useful in many fields, including water resources engineering and management. However, due to the non-linear and chaotic characteristics associated with natural processes and human decision making, the use of ANNs in real-world applications is still limited, and its performance needs to be further improved for a broader practical use. The commonly used Back-Propagation (BP) scheme and gradient-based optimization in training the ANNs have already found to be problematic in some cases. The BP scheme and gradient-based optimization methods are associated with the risk of premature convergence, stuck in local optimums, and the searching is highly dependent on initial conditions. Therefore, as an alternative to BP and gradient-based searching scheme, we propose an effective and efficient global searching method, termed the Shuffled Complex Evolutionary Global optimization algorithm with Principal Component Analysis (SP-UCI), to train the ANN connectivity weights. Large number of real-world datasets are tested with the SP-UCI-based ANN, as well as various popular Evolutionary Algorithms (EAs)-enhanced ANNs, i.e., Particle Swarm Optimization (PSO)-, Genetic Algorithm (GA)-, Simulated Annealing (SA)-, and Differential Evolution (DE)-enhanced ANNs. Results show that SP-UCI-enhanced ANN is generally superior over other EA-enhanced ANNs with regard to the convergence and computational performance. In addition, we carried out a case study for hydropower scheduling in the Trinity Lake in the western U.S. In this case study, multiple climate indices are used as predictors for the SP-UCI-enhanced ANN. The reservoir inflows and hydropower releases are predicted up to sub-seasonal to seasonal scale. Results show that SP-UCI-enhanced ANN is able to achieve better statistics than other EAs-based ANN, which implies the usefulness and powerfulness of proposed SP-UCI-enhanced ANN for reservoir operation, water resources engineering and management. The SP-UCI-enhanced ANN is universally applicable to many other regression and prediction problems, and it has a good potential to be an alternative to the classical BP scheme and gradient-based optimization methods.

Choosing the appropriate forecasting model for predictive parameter control.

PubMed

Aleti, Aldeida; Moser, Irene; Meedeniya, Indika; Grunske, Lars

2014-01-01

All commonly used stochastic optimisation algorithms have to be parameterised to perform effectively. Adaptive parameter control (APC) is an effective method used for this purpose. APC repeatedly adjusts parameter values during the optimisation process for optimal algorithm performance. The assignment of parameter values for a given iteration is based on previously measured performance. In recent research, time series prediction has been proposed as a method of projecting the probabilities to use for parameter value selection. In this work, we examine the suitability of a variety of prediction methods for the projection of future parameter performance based on previous data. All considered prediction methods have assumptions the time series data has to conform to for the prediction method to provide accurate projections. Looking specifically at parameters of evolutionary algorithms (EAs), we find that all standard EA parameters with the exception of population size conform largely to the assumptions made by the considered prediction methods. Evaluating the performance of these prediction methods, we find that linear regression provides the best results by a very small and statistically insignificant margin. Regardless of the prediction method, predictive parameter control outperforms state of the art parameter control methods when the performance data adheres to the assumptions made by the prediction method. When a parameter's performance data does not adhere to the assumptions made by the forecasting method, the use of prediction does not have a notable adverse impact on the algorithm's performance.

Pruning Neural Networks with Distribution Estimation Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less

Phylogenetic diversity anomaly in angiosperms between eastern Asia and eastern North America.

PubMed

Qian, Hong; Jin, Yi; Ricklefs, Robert E

2017-10-24

Although eastern Asia (EAS) and eastern North America (ENA) have similar climates, plant species richness in EAS greatly exceeds that in ENA. The degree to which this diversity difference reflects the ages of the floras or their rates of evolutionary diversification has not been quantified. Measures of species diversity that do not incorporate the ages of lineages disregard the evolutionary distinctiveness of species. In contrast, phylogenetic diversity integrates both the number of species and their history of evolutionary diversification. Here we compared species diversity and phylogenetic diversity in a large number of flowering plant (angiosperm) floras distributed across EAS and ENA, two regions with similar contemporary environments and broadly shared floristic history. After accounting for climate and sample area, we found both species diversity and phylogenetic diversity to be significantly higher in EAS than in ENA. When we controlled the number of species statistically, we found that phylogenetic diversity remained substantially higher in EAS than in ENA, although it tended to converge at high latitude. This pattern held independently for herbs, shrubs, and trees. The anomaly in species and phylogenetic diversity likely resulted from differences in regional processes, related in part to high climatic and topographic heterogeneity, and a strong monsoon climate, in EAS. The broad connection between tropical and temperate floras in southern Asia also might have played a role in creating the phylogenetic diversity anomaly.

YANA – a software tool for analyzing flux modes, gene-expression and enzyme activities

PubMed Central

Schwarz, Roland; Musch, Patrick; von Kamp, Axel; Engels, Bernd; Schirmer, Heiner; Schuster, Stefan; Dandekar, Thomas

2005-01-01

Background A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e.g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at . Conclusion A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts. PMID:15929789

Rapid wide-field Mueller matrix polarimetry imaging based on four photoelastic modulators with no moving parts.

PubMed

Alali, Sanaz; Gribble, Adam; Vitkin, I Alex

2016-03-01

A new polarimetry method is demonstrated to image the entire Mueller matrix of a turbid sample using four photoelastic modulators (PEMs) and a charge coupled device (CCD) camera, with no moving parts. Accurate wide-field imaging is enabled with a field-programmable gate array (FPGA) optical gating technique and an evolutionary algorithm (EA) that optimizes imaging times. This technique accurately and rapidly measured the Mueller matrices of air, polarization elements, and turbid phantoms. The system should prove advantageous for Mueller matrix analysis of turbid samples (e.g., biological tissues) over large fields of view, in less than a second.

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

PubMed Central

Budi, Setia; de Souza, Paulo; Timms, Greg; Malhotra, Vishv; Turner, Paul

2015-01-01

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail. PMID:26633392

On the effect of response transformations in sequential parameter optimization.

PubMed

Wagner, Tobias; Wessing, Simon

2012-01-01

Parameter tuning of evolutionary algorithms (EAs) is attracting more and more interest. In particular, the sequential parameter optimization (SPO) framework for the model-assisted tuning of stochastic optimizers has resulted in established parameter tuning algorithms. In this paper, we enhance the SPO framework by introducing transformation steps before the response aggregation and before the actual modeling. Based on design-of-experiments techniques, we empirically analyze the effect of integrating different transformations. We show that in particular, a rank transformation of the responses provides significant improvements. A deeper analysis of the resulting models and additional experiments with adaptive procedures indicates that the rank and the Box-Cox transformation are able to improve the properties of the resultant distributions with respect to symmetry and normality of the residuals. Moreover, model-based effect plots document a higher discriminatory power obtained by the rank transformation.

Theoretical and Empirical Analysis of a Spatial EA Parallel Boosting Algorithm.

PubMed

Kamath, Uday; Domeniconi, Carlotta; De Jong, Kenneth

2018-01-01

Many real-world problems involve massive amounts of data. Under these circumstances learning algorithms often become prohibitively expensive, making scalability a pressing issue to be addressed. A common approach is to perform sampling to reduce the size of the dataset and enable efficient learning. Alternatively, one customizes learning algorithms to achieve scalability. In either case, the key challenge is to obtain algorithmic efficiency without compromising the quality of the results. In this article we discuss a meta-learning algorithm (PSBML) that combines concepts from spatially structured evolutionary algorithms (SSEAs) with concepts from ensemble and boosting methodologies to achieve the desired scalability property. We present both theoretical and empirical analyses which show that PSBML preserves a critical property of boosting, specifically, convergence to a distribution centered around the margin. We then present additional empirical analyses showing that this meta-level algorithm provides a general and effective framework that can be used in combination with a variety of learning classifiers. We perform extensive experiments to investigate the trade-off achieved between scalability and accuracy, and robustness to noise, on both synthetic and real-world data. These empirical results corroborate our theoretical analysis, and demonstrate the potential of PSBML in achieving scalability without sacrificing accuracy.

Autonomous self-organizing resource manager for multiple networked platforms

NASA Astrophysics Data System (ADS)

Smith, James F., III

2002-08-01

A fuzzy logic based expert system for resource management has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar autonomous naval platforms defending their group against attackers. The platforms can be very general, e.g., ships, planes, robots, land based facilities, etc. Potential foes the platforms deal with can also be general. This paper provides an overview of the resource manager including the four fuzzy decision trees that make up the resource manager; the fuzzy EA model; genetic algorithm based optimization; co-evolutionary data mining through gaming; and mathematical, computational and hardware based validation. Methods of automatically designing new multi-platform EA techniques are considered. The expert system runs on each defending platform rendering it an autonomous system requiring no human intervention. There is no commanding platform. Instead the platforms work cooperatively as a function of battlespace geometry; sensor data such as range, bearing, ID, uncertainty measures for sensor output; intelligence reports; etc. Computational experiments will show the defending networked platform's ability to self- organize. The platforms' ability to self-organize is illustrated through the output of the scenario generator, a software package that automates the underlying data mining problem and creates a computer movie of the platforms' interaction for evaluation.

Multivariable optimization of an auto-thermal ammonia synthesis reactor using genetic algorithm

NASA Astrophysics Data System (ADS)

Anh-Nga, Nguyen T.; Tuan-Anh, Nguyen; Tien-Dung, Vu; Kim-Trung, Nguyen

2017-09-01

The ammonia synthesis system is an important chemical process used in the manufacture of fertilizers, chemicals, explosives, fibers, plastics, refrigeration. In the literature, many works approaching the modeling, simulation and optimization of an auto-thermal ammonia synthesis reactor can be found. However, they just focus on the optimization of the reactor length while keeping the others parameters constant. In this study, the other parameters are also considered in the optimization problem such as the temperature of feed gas enters the catalyst zone. The optimal problem requires the maximization of a multivariable objective function which subjects to a number of equality constraints involving the solution of coupled differential equations and also inequality constraints. The solution of an optimization problem can be found through, among others, deterministic or stochastic approaches. The stochastic methods, such as evolutionary algorithm (EA), which is based on natural phenomenon, can overcome the drawbacks such as the requirement of the derivatives of the objective function and/or constraints, or being not efficient in non-differentiable or discontinuous problems. Genetic algorithm (GA) which is a class of EA, exceptionally simple, robust at numerical optimization and is more likely to find a true global optimum. In this study, the genetic algorithm is employed to find the optimum profit of the process. The inequality constraints were treated using penalty method. The coupled differential equations system was solved using Runge-Kutta 4th order method. The results showed that the presented numerical method could be applied to model the ammonia synthesis reactor. The optimum economic profit obtained from this study are also compared to the results from the literature. It suggests that the process should be operated at higher temperature of feed gas in catalyst zone and the reactor length is slightly longer.

Parallel optimization algorithm for drone inspection in the building industry

NASA Astrophysics Data System (ADS)

Walczyński, Maciej; BoŻejko, Wojciech; Skorupka, Dariusz

2017-07-01

In this paper we present an approach for Vehicle Routing Problem with Drones (VRPD) in case of building inspection from the air. In autonomic inspection process there is a need to determine of the optimal route for inspection drone. This is especially important issue because of the very limited flight time of modern multicopters. The method of determining solutions for Traveling Salesman Problem(TSP), described in this paper bases on Parallel Evolutionary Algorithm (ParEA)with cooperative and independent approach for communication between threads. This method described first by Bożejko and Wodecki [1] bases on the observation that if exists some number of elements on certain positions in a number of permutations which are local minima, then those elements will be in the same position in the optimal solution for TSP problem. Numerical experiments were made on BEM computational cluster with using MPI library.

Assessing and optimising flood control options along the Arachthos river floodplain (Epirus, Greece)

NASA Astrophysics Data System (ADS)

Drosou, Athina; Dimitriadis, Panayiotis; Lykou, Archontia; Kossieris, Panagiotis; Tsoukalas, Ioannis; Efstratiadis, Andreas; Mamassis, Nikos

2015-04-01

We present a multi-criteria simulation-optimization framework for the optimal design and setting of flood protection structures along river banks. The methodology is tested in the lower course of the Arachthos River (Epirus, Greece), downstream of the hydroelectric dam of Pournari. The entire study area is very sensitive, particularly because the river crosses the urban area of Arta, which is located just after the dam. Moreover, extended agricultural areas that are crucial for the local economy are prone to floods. In the proposed methodology we investigate two conflicting criteria, i.e. the minimization of flood hazards (due to damages to urban infrastructures, crops, etc.) and the minimization of construction costs of the essential hydraulic structures (e.g. dikes). For the hydraulic simulation we examine two flood routing models, named 1D HEC-RAS and quasi-2D LISFLOOD, whereas the optimization is carried out through the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the EAS method.

An improved method for nonlinear parameter estimation: a case study of the Rössler model

NASA Astrophysics Data System (ADS)

He, Wen-Ping; Wang, Liu; Jiang, Yun-Di; Wan, Shi-Quan

2016-08-01

Parameter estimation is an important research topic in nonlinear dynamics. Based on the evolutionary algorithm (EA), Wang et al. (2014) present a new scheme for nonlinear parameter estimation and numerical tests indicate that the estimation precision is satisfactory. However, the convergence rate of the EA is relatively slow when multiple unknown parameters in a multidimensional dynamical system are estimated simultaneously. To solve this problem, an improved method for parameter estimation of nonlinear dynamical equations is provided in the present paper. The main idea of the improved scheme is to use all of the known time series for all of the components in some dynamical equations to estimate the parameters in single component one by one, instead of estimating all of the parameters in all of the components simultaneously. Thus, we can estimate all of the parameters stage by stage. The performance of the improved method was tested using a classic chaotic system—Rössler model. The numerical tests show that the amended parameter estimation scheme can greatly improve the searching efficiency and that there is a significant increase in the convergence rate of the EA, particularly for multiparameter estimation in multidimensional dynamical equations. Moreover, the results indicate that the accuracy of parameter estimation and the CPU time consumed by the presented method have no obvious dependence on the sample size.

Applications of Evolutionary Algorithms to Electromagnetic Materials Characterization and Design Problems

NASA Astrophysics Data System (ADS)

Frasch, Jonathan Lemoine

Determining the electrical permittivity and magnetic permeability of materials is an important task in electromagnetics research. The method using reflection and transmission scattering parameters to determine these constants has been widely employed for many years, ever since the work of Nicolson, Ross, and Weir in the 1970's. For general materials that are homogeneous, linear, and isotropic, the method they developed (the NRW method) works very well and provides an analytical solution. For materials which possess a metal backing or are applied as a coating to a metal surface, it can be difficult or even impossible to obtain a transmission measurement, especially when the coating is thin. In such a circumstance, it is common to resort to a method which uses two reflection type measurements. There are several such methods for free-space measurements, using multiple angles or polarizations for example. For waveguide measurements, obtaining two independent sources of information from which to extract two complex parameters can be a challenge. This dissertation covers three different topics. Two of these involve different techniques to characterize conductor-backed materials, and the third proposes a method for designing synthetic validation standards for use with standard NRW measurements. All three of these topics utilize modal expansions of electric and magnetic fields to analyze propagation in stepped rectangular waveguides. Two of the projects utilize evolutionary algorithms (EA) to design waveguide structures. These algorithms were developed specifically for these projects and utilize fairly recent innovations within the optimization community. The first characterization technique uses two different versions of a single vertical step in the waveguide. Samples to be tested lie inside the steps with the conductor reflection plane behind them. If the two reflection measurements are truly independent it should be possible to recover the values of two complex parameters, but success of the technique ultimately depends upon how independent the measurements actually are. Next, a method is demonstrated for developing synthetic verification standards. These standards are created from combinations of vertical steps formed from a single piece of metal or metal coated plastic. These fully insertable structures mimic some of the measurement characteristics of typical lab specimens and thus provide a useful tool for verifying the proper calibration and function of the experimental setup used for NRW characterization. These standards are designed with the use an EA, which compares possible designs based on the quality of the match with target parameter values. Several examples have been fabricated and tested, and the design specifications and results are presented. Finally, a second characterization technique is considered. This method uses multiple vertical steps to construct an error reducing structure within the waveguide, which allows parameters to be reliably extracted using both reflection and transmission measurements. These structures are designed with an EA, measuring fitness by the reduction of error in the extracted parameters. An additional EA is used to assist in the extraction of the material parameters supplying better initial guesses to a secant method solver. This hybrid approach greatly increases the stability of the solver and increases the speed of parameter extractions. Several designs have been identified and are analyzed.

Optimization lighting layout based on gene density improved genetic algorithm for indoor visible light communications

NASA Astrophysics Data System (ADS)

Liu, Huanlin; Wang, Xin; Chen, Yong; Kong, Deqian; Xia, Peijie

2017-05-01

For indoor visible light communication system, the layout of LED lamps affects the uniformity of the received power on communication plane. In order to find an optimized lighting layout that meets both the lighting needs and communication needs, a gene density genetic algorithm (GDGA) is proposed. In GDGA, a gene indicates a pair of abscissa and ordinate of a LED, and an individual represents a LED layout in the room. The segmented crossover operation and gene mutation strategy based on gene density are put forward to make the received power on communication plane more uniform and increase the population's diversity. A weighted differences function between individuals is designed as the fitness function of GDGA for reserving the population having the useful LED layout genetic information and ensuring the global convergence of GDGA. Comparing square layout and circular layout, with the optimized layout achieved by the GDGA, the power uniformity increases by 83.3%, 83.1% and 55.4%, respectively. Furthermore, the convergence of GDGA is verified compared with evolutionary algorithm (EA). Experimental results show that GDGA can quickly find an approximation of optimal layout.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

PubMed

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

PubMed Central

Suraj; Tiwari, Purnendu; Ghosh, Subhojit; Sinha, Rakesh Kumar

2015-01-01

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed. PMID:25972896

Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex.

PubMed

Sloas, David C; Zhuo, Ran; Xue, Hongbo; Chambers, Anna R; Kolaczyk, Eric; Polley, Daniel B; Sen, Kamal

2016-01-01

Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.

Optimizing RF gun cavity geometry within an automated injector design system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alicia Hofler ,Pavel Evtushenko

2011-03-28

RF guns play an integral role in the success of several light sources around the world, and properly designed and optimized cw superconducting RF (SRF) guns can provide a path to higher average brightness. As the need for these guns grows, it is important to have automated optimization software tools that vary the geometry of the gun cavity as part of the injector design process. This will allow designers to improve existing designs for present installations, extend the utility of these guns to other applications, and develop new designs. An evolutionary algorithm (EA) based system can provide this capability becausemore » EAs can search in parallel a large parameter space (often non-linear) and in a relatively short time identify promising regions of the space for more careful consideration. The injector designer can then evaluate more cavity design parameters during the injector optimization process against the beam performance requirements of the injector. This paper will describe an extension to the APISA software that allows the cavity geometry to be modified as part of the injector optimization and provide examples of its application to existing RF and SRF gun designs.« less

The evolutionary sequence of post-starburst galaxies

NASA Astrophysics Data System (ADS)

Wilkinson, C. L.; Pimbblet, K. A.; Stott, J. P.

2017-12-01

There are multiple ways in which to select post-starburst galaxies in the literature. In this work, we present a study into how two well-used selection techniques have consequences on observable post-starburst galaxy parameters, such as colour, morphology and environment, and how this affects interpretations of their role in the galaxy duty cycle. We identify a master sample of H δ strong (EWH δ > 3Å) post-starburst galaxies from the value-added catalogue in the seventh data release of the Sloan Digital Sky Survey (SDSS DR7) over a redshift range 0.01 < z < 0.1. From this sample we select two E+A subsets, both having a very little [O II] emission (EW_[O II] > -2.5 Å) but one having an additional cut on EWHα (>-3 Å). We examine the differences in observables and AGN fractions to see what effect the H α cut has on the properties of post-starburst galaxies and what these differing samples can tell us about the duty cycle of post-starburst galaxies. We find that H δ strong galaxies peak in the 'blue cloud', E+As in the 'green valley' and pure E+As in the 'red sequence'. We also find that pure E+As have a more early-type morphology and a higher fraction in denser environments compared with the H δ strong and E+A galaxies. These results suggest that there is an evolutionary sequence in the post-starburst phase from blue discy galaxies with residual star formation to passive red early-types.

Genetic Insights in Barrett’s Esophagus and Esophageal Adenocarcinoma

PubMed Central

Reid, Brian J.; Paulson, Thomas G.; Li, Xiaohong

2015-01-01

Beginning in the 1980s, an alarming rise in the incidence of esophageal adenocarcinoma (EA) led to screening of patients with reflux to detect Barrett’s esophagus (BE) and surveillance of BE to detect early EA. This strategy, based on linear progression disease models, resulted in selective detection of BE that does not progress to EA over a lifetime (overdiagnosis) and missed BE that rapidly progresses to EA (underdiagnosis). Here we review the historical thought processes that resulted in this undesired outcome and the transformation in our understanding of genetic and evolutionary principles governing neoplastic progression that has come from application of modern genomic technologies to cancers and their precursors. This new synthesis provides improved strategies for prevention and early detection of EA by addressing the environmental and mutational processes that can determine “windows of opportunity” in time to detect rapidly progressing BE and distinguish it from slowly or non-progressing BE. PMID:26208895

Controlling laser driven protons acceleration using a deformable mirror at a high repetition rate

NASA Astrophysics Data System (ADS)

Noaman-ul-Haq, M.; Sokollik, T.; Ahmed, H.; Braenzel, J.; Ehrentraut, L.; Mirzaie, M.; Yu, L.-L.; Sheng, Z. M.; Chen, L. M.; Schnürer, M.; Zhang, J.

2018-03-01

We present results from a proof-of-principle experiment to optimize laser driven protons acceleration by directly feeding back its spectral information to a deformable mirror (DM) controlled by evolutionary algorithms (EAs). By irradiating a stable high-repetition rate tape driven target with ultra-intense pulses of intensities ∼1020 W/ cm2, we optimize the maximum energy of the accelerated protons with a stability of less than ∼5% fluctuations near optimum value. Moreover, due to spatio-temporal development of the sheath field, modulations in the spectrum are also observed. Particularly, a prominent narrow peak is observed with a spread of ∼15% (FWHM) at low energy part of the spectrum. These results are helpful to develop high repetition rate optimization techniques required for laser-driven ion accelerators.

Incorporation of an evolutionary algorithm to estimate transfer-functions for a parameter regionalization scheme of a rainfall-runoff model

NASA Astrophysics Data System (ADS)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2016-04-01

This contribution presents a framework, which enables the use of an Evolutionary Algorithm (EA) for the calibration and regionalization of the hydrological model COSEROreg. COSEROreg uses an updated version of the HBV-type model COSERO (Kling et al. 2014) for the modelling of hydrological processes and is embedded in a parameter regionalization scheme based on Samaniego et al. (2010). The latter uses subscale-information to estimate model via a-priori chosen transfer functions (often derived from pedotransfer functions). However, the transferability of the regionalization scheme to different model-concepts and the integration of new forms of subscale information is not straightforward. (i) The usefulness of (new) single sub-scale information layers is unknown beforehand. (ii) Additionally, the establishment of functional relationships between these (possibly meaningless) sub-scale information layers and the distributed model parameters remain a central challenge in the implementation of a regionalization procedure. The proposed method theoretically provides a framework to overcome this challenge. The implementation of the EA encompasses the following procedure: First, a formal grammar is specified (Ryan et al., 1998). The construction of the grammar thereby defines the set of possible transfer functions and also allows to incorporate hydrological domain knowledge into the search itself. The EA iterates over the given space by combining parameterized basic functions (e.g. linear- or exponential functions) and sub-scale information layers into transfer functions, which are then used in COSEROreg. However, a pre-selection model is applied beforehand to sort out unfeasible proposals by the EA and to reduce the necessary model runs. A second optimization routine is used to optimize the parameters of the transfer functions proposed by the EA. This concept, namely using two nested optimization loops, is inspired by the idea of Lamarckian Evolution and Baldwin Effect (Whitley et al., 1994), which might be understood as the idea that acquired characteristics during the lifetime of an individual can be transferred between generations. A hierarchical objective function is used for the model evaluation. This enables model preemption (Tolson et al., 2010) and reduces the amount of model evaluations in the early stages of optimization. References: • Samaniego, L., Kumar, R., Attinger, S. (2010): Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., doi: 10.1029/2008WR007327 • Kling, H., Stanzel, P., Fuchs, M., and Nachtnebel, H. P. (2014): Performance of the COSERO precipitation-runoff model under non-stationary conditions in basins with different climates, Hydrolog. Sci. J., doi:10.1080/02626667.2014.959956. • C. Ryan, J.J. Collins, Ji, Collins, M. O'Neil (1998): Evolving Programs for an Arbitrary Language, Lecture Notes in Computer Science 1391, Proceedings of the First European Workshop on Genetic Programming. • B.A. Tolson, S. Razavi, L.S. Matott, N.R. Thomson, A. MacLean, F.R. Seglenieks (2010): Reducing the computational cost of automatic calibration through model preemption, Water Resour. Res., 46, W11523, doi:10.1029/2009WR008957. • D. Whitley, S. Gordon, K. Mathias (1994): Lamarckian evolution, the Baldwin effect, and function optimization, in Parallel Problem Solving from Nature (PPSN) III, Y. Davidor, H.-P. Schwefel, and R. Manner, Eds. Berlin: Springer-Verlag, pp. 6-15.

A Note on Evolutionary Algorithms and Its Applications

ERIC Educational Resources Information Center

Bhargava, Shifali

2013-01-01

This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.

FluBreaks: early epidemic detection from Google flu trends.

PubMed

Pervaiz, Fahad; Pervaiz, Mansoor; Abdur Rehman, Nabeel; Saif, Umar

2012-10-04

The Google Flu Trends service was launched in 2008 to track changes in the volume of online search queries related to flu-like symptoms. Over the last few years, the trend data produced by this service has shown a consistent relationship with the actual number of flu reports collected by the US Centers for Disease Control and Prevention (CDC), often identifying increases in flu cases weeks in advance of CDC records. However, contrary to popular belief, Google Flu Trends is not an early epidemic detection system. Instead, it is designed as a baseline indicator of the trend, or changes, in the number of disease cases. To evaluate whether these trends can be used as a basis for an early warning system for epidemics. We present the first detailed algorithmic analysis of how Google Flu Trends can be used as a basis for building a fully automated system for early warning of epidemics in advance of methods used by the CDC. Based on our work, we present a novel early epidemic detection system, called FluBreaks (dritte.org/flubreaks), based on Google Flu Trends data. We compared the accuracy and practicality of three types of algorithms: normal distribution algorithms, Poisson distribution algorithms, and negative binomial distribution algorithms. We explored the relative merits of these methods, and related our findings to changes in Internet penetration and population size for the regions in Google Flu Trends providing data. Across our performance metrics of percentage true-positives (RTP), percentage false-positives (RFP), percentage overlap (OT), and percentage early alarms (EA), Poisson- and negative binomial-based algorithms performed better in all except RFP. Poisson-based algorithms had average values of 99%, 28%, 71%, and 76% for RTP, RFP, OT, and EA, respectively, whereas negative binomial-based algorithms had average values of 97.8%, 17.8%, 60%, and 55% for RTP, RFP, OT, and EA, respectively. Moreover, the EA was also affected by the region's population size. Regions with larger populations (regions 4 and 6) had higher values of EA than region 10 (which had the smallest population) for negative binomial- and Poisson-based algorithms. The difference was 12.5% and 13.5% on average in negative binomial- and Poisson-based algorithms, respectively. We present the first detailed comparative analysis of popular early epidemic detection algorithms on Google Flu Trends data. We note that realizing this opportunity requires moving beyond the cumulative sum and historical limits method-based normal distribution approaches, traditionally employed by the CDC, to negative binomial- and Poisson-based algorithms to deal with potentially noisy search query data from regions with varying population and Internet penetrations. Based on our work, we have developed FluBreaks, an early warning system for flu epidemics using Google Flu Trends.

Combining in silico evolution and nonlinear dimensionality reduction to redesign responses of signaling networks

NASA Astrophysics Data System (ADS)

Prescott, Aaron M.; Abel, Steven M.

2016-12-01

The rational design of network behavior is a central goal of synthetic biology. Here, we combine in silico evolution with nonlinear dimensionality reduction to redesign the responses of fixed-topology signaling networks and to characterize sets of kinetic parameters that underlie various input-output relations. We first consider the earliest part of the T cell receptor (TCR) signaling network and demonstrate that it can produce a variety of input-output relations (quantified as the level of TCR phosphorylation as a function of the characteristic TCR binding time). We utilize an evolutionary algorithm (EA) to identify sets of kinetic parameters that give rise to: (i) sigmoidal responses with the activation threshold varied over 6 orders of magnitude, (ii) a graded response, and (iii) an inverted response in which short TCR binding times lead to activation. We also consider a network with both positive and negative feedback and use the EA to evolve oscillatory responses with different periods in response to a change in input. For each targeted input-output relation, we conduct many independent runs of the EA and use nonlinear dimensionality reduction to embed the resulting data for each network in two dimensions. We then partition the results into groups and characterize constraints placed on the parameters by the different targeted response curves. Our approach provides a way (i) to guide the design of kinetic parameters of fixed-topology networks to generate novel input-output relations and (ii) to constrain ranges of biological parameters using experimental data. In the cases considered, the network topologies exhibit significant flexibility in generating alternative responses, with distinct patterns of kinetic rates emerging for different targeted responses.

Algorithmic Mechanism Design of Evolutionary Computation.

PubMed

Pei, Yan

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Algorithmic Mechanism Design of Evolutionary Computation

PubMed Central

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm. PMID:26257777

Predicting Innovation Acceptance by Simulation in Virtual Environments (Theoretical Foundations)

NASA Astrophysics Data System (ADS)

León, Noel; Duran, Roberto; Aguayo, Humberto; Flores, Myrna

This paper extends the current development of a methodology for Computer Aided Innovation. It begins with a presentation of concepts related to the perceived capabilities of virtual environments in the Innovation Cycle. The main premise establishes that it is possible to predict the acceptance of a new product in a specific market, by releasing an early prototype in a virtual scenario to quantify its general reception and to receive early feedback from potential customers. The paper continues to focus this research on a synergistic extension of techniques that have their origins in optimization and innovation disciplines. TRIZ (Theory of Inventive Problem Solving), extends the generation of variants with Evolutionary Algorithms (EA) and finally to present the designer and the intended customer, creative and innovative alternatives. All of this developed on a virtual software interface (Virtual World). The work continues with a general description of the project as a step forward to improve the overall strategy.

Cooperative combinatorial optimization: evolutionary computation case study.

PubMed

Burgin, Mark; Eberbach, Eugene

2008-01-01

This paper presents a formalization of the notion of cooperation and competition of multiple systems that work toward a common optimization goal of the population using evolutionary computation techniques. It is proved that evolutionary algorithms are more expressive than conventional recursive algorithms, such as Turing machines. Three classes of evolutionary computations are introduced and studied: bounded finite, unbounded finite, and infinite computations. Universal evolutionary algorithms are constructed. Such properties of evolutionary algorithms as completeness, optimality, and search decidability are examined. A natural extension of evolutionary Turing machine (ETM) model is proposed to properly reflect phenomena of cooperation and competition in the whole population.

Comparison of Ergot Alkaloid Biosynthesis Gene Clusters in Claviceps Species Indicates Loss of Late Pathway Steps in Evolution of C. fusiformis▿

PubMed Central

Lorenz, Nicole; Wilson, Ella V.; Machado, Caroline; Schardl, Christopher L.; Tudzynski, Paul

2007-01-01

The grass parasites Claviceps purpurea and Claviceps fusiformis produce ergot alkaloids (EA) in planta and in submerged culture. Whereas EA synthesis (EAS) in C. purpurea proceeds via clavine intermediates to lysergic acid and the complex ergopeptines, C. fusiformis produces only agroclavine and elymoclavine. In C. purpurea the EAS gene (EAS) cluster includes dmaW (encoding the first pathway step), cloA (elymoclavine oxidation to lysergic acid), and the lpsA/lpsB genes (ergopeptine formation). We analyzed the corresponding C. fusiformis EAS cluster to investigate the evolutionary basis for chemotypic differences between the Claviceps species. Other than three peptide synthetase genes (lpsC and the tandem paralogues lpsA1 and lpsA2), homologues of all C. purpurea EAS genes were identified in C. fusiformis, including homologues of lpsB and cloA, which in C. purpurea encode enzymes for steps after clavine synthesis. Rearrangement of the cluster was evident around lpsB, which is truncated in C. fusiformis. This and several frameshift mutations render CflpsB a pseudogene (CflpsBΨ). No obvious inactivating mutation was identified in CfcloA. All C. fusiformis EAS genes, including CflpsBΨ and CfcloA, were expressed in culture. Cross-complementation analyses demonstrated that CfcloA and CflpsBΨ were expressed in C. purpurea but did not encode functional enzymes. In contrast, CpcloA catalyzed lysergic acid biosynthesis in C. fusiformis, indicating that C. fusiformis terminates its EAS pathway at elymoclavine because the cloA gene product is inactive. We propose that the C. fusiformis EAS cluster evolved from a more complete cluster by loss of some lps genes and by rearrangements and mutations inactivating lpsB and cloA. PMID:17720822

Comparison of ergot alkaloid biosynthesis gene clusters in Claviceps species indicates loss of late pathway steps in evolution of C. fusiformis.

PubMed

Lorenz, Nicole; Wilson, Ella V; Machado, Caroline; Schardl, Christopher L; Tudzynski, Paul

2007-11-01

The grass parasites Claviceps purpurea and Claviceps fusiformis produce ergot alkaloids (EA) in planta and in submerged culture. Whereas EA synthesis (EAS) in C. purpurea proceeds via clavine intermediates to lysergic acid and the complex ergopeptines, C. fusiformis produces only agroclavine and elymoclavine. In C. purpurea the EAS gene (EAS) cluster includes dmaW (encoding the first pathway step), cloA (elymoclavine oxidation to lysergic acid), and the lpsA/lpsB genes (ergopeptine formation). We analyzed the corresponding C. fusiformis EAS cluster to investigate the evolutionary basis for chemotypic differences between the Claviceps species. Other than three peptide synthetase genes (lpsC and the tandem paralogues lpsA1 and lpsA2), homologues of all C. purpurea EAS genes were identified in C. fusiformis, including homologues of lpsB and cloA, which in C. purpurea encode enzymes for steps after clavine synthesis. Rearrangement of the cluster was evident around lpsB, which is truncated in C. fusiformis. This and several frameshift mutations render CflpsB a pseudogene (CflpsB(Psi)). No obvious inactivating mutation was identified in CfcloA. All C. fusiformis EAS genes, including CflpsB(Psi) and CfcloA, were expressed in culture. Cross-complementation analyses demonstrated that CfcloA and CflpsB(Psi) were expressed in C. purpurea but did not encode functional enzymes. In contrast, CpcloA catalyzed lysergic acid biosynthesis in C. fusiformis, indicating that C. fusiformis terminates its EAS pathway at elymoclavine because the cloA gene product is inactive. We propose that the C. fusiformis EAS cluster evolved from a more complete cluster by loss of some lps genes and by rearrangements and mutations inactivating lpsB and cloA.

Multicomponent ionic liquid CMC prediction.

PubMed

Kłosowska-Chomiczewska, I E; Artichowicz, W; Preiss, U; Jungnickel, C

2017-09-27

We created a model to predict CMC of ILs based on 704 experimental values published in 43 publications since 2000. Our model was able to predict CMC of variety of ILs in binary or ternary system in a presence of salt or alcohol. The molecular volume of IL (V m ), solvent-accessible surface (Ŝ), solvation enthalpy (Δ solv G ∞ ), concentration of salt (C s ) or alcohol (C a ) and their molecular volumes (V ms and V ma , respectively) were chosen as descriptors, and Kernel Support Vector Machine (KSVM) and Evolutionary Algorithm (EA) as regression methodologies to create the models. Data was split into training and validation set (80/20) and subjected to bootstrap aggregation. KSVM provided better fit with average R 2 of 0.843, and MSE of 0.608, whereas EA resulted in R 2 of 0.794 and MSE of 0.973. From the sensitivity analysis it was shown that V m and Ŝ have the highest impact on ILs micellization in both binary and ternary systems, however surprisingly in the presence of alcohol the V m becomes insignificant/irrelevant. Micelle stabilizing or destabilizing influence of the descriptors depends upon the additives. Previous attempts at modelling the CMC of ILs was generally limited to small number of ILs in simplified (binary) systems. We however showed successful prediction of the CMC over a range of different systems (binary and ternary).

Recent developments of axial flow compressors under transonic flow conditions

NASA Astrophysics Data System (ADS)

Srinivas, G.; Raghunandana, K.; Satish Shenoy, B.

2017-05-01

The objective of this paper is to give a holistic view of the most advanced technology and procedures that are practiced in the field of turbomachinery design. Compressor flow solver is the turbulence model used in the CFD to solve viscous problems. The popular techniques like Jameson’s rotated difference scheme was used to solve potential flow equation in transonic condition for two dimensional aero foils and later three dimensional wings. The gradient base method is also a popular method especially for compressor blade shape optimization. Various other types of optimization techniques available are Evolutionary algorithms (EAs) and Response surface methodology (RSM). It is observed that in order to improve compressor flow solver and to get agreeable results careful attention need to be paid towards viscous relations, grid resolution, turbulent modeling and artificial viscosity, in CFD. The advanced techniques like Jameson’s rotated difference had most substantial impact on wing design and aero foil. For compressor blade shape optimization, Evolutionary algorithm is quite simple than gradient based technique because it can solve the parameters simultaneously by searching from multiple points in the given design space. Response surface methodology (RSM) is a method basically used to design empirical models of the response that were observed and to study systematically the experimental data. This methodology analyses the correct relationship between expected responses (output) and design variables (input). RSM solves the function systematically in a series of mathematical and statistical processes. For turbomachinery blade optimization recently RSM has been implemented successfully. The well-designed high performance axial flow compressors finds its application in any air-breathing jet engines.

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

PubMed

Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

Impact of earliest activation site location in the septal right ventricular outflow tract for identification of left vs right outflow tract origin of idiopathic ventricular arrhythmias.

PubMed

Acosta, Juan; Penela, Diego; Herczku, Csaba; Macías, Yolanda; Andreu, David; Fernández-Armenta, Juan; Cipolletta, Laura; Díaz, Andrés; Korshunov, Viatcheslav; Brugada, Josep; Mont, Lluis; Cabrera, Jose A; Sánchez-Quintana, Damián; Berruezo, Antonio

2015-04-01

The earliest activation site (EAS) location in the septal right ventricular outflow tract (RVOT) could be an additional mapping data predictor of left ventricular outflow tract (LVOT) vs RVOT origin of idiopathic ventricular arrhythmias (VAs). The purpose of this study was to assess the impact of EAS location in predicting LVOT vs RVOT origin. Macroscopic and histologic study was performed in 12 postmortem hearts. Electroanatomic maps (EAMs) from 37 patients with outflow tract (OT) VA with the EAS in the septal RVOT were analyzed. Pulmonary valve (PV) was defined by voltage scanning after validation of voltage thresholds by image integration. EAM measurements were correlated with those of macroscopic/histologic study. A cutoff value of 1.9 mV discriminated between subvalvular and supravalvular positions (90% sensitivity, 96% specificity). EAS ≥1 cm below PV excluded RVOT site of origin (SOO). According to anatomic findings (distance PV-left coronary cusp = 5 ± 3 vs PV-right coronary cusp = 11 ± 5 mm), EAS-PV distance was significantly shorter in VAs arising from left coronary cusp than from the other LVOT locations (4.2 ± 5.4 mm vs 9.2 ± 7 mm; P = .034). The 10-ms isochronal longitudinal/perpendicular diameter ratio was higher in the RVOT vs the LVOT SOO group (1.97 ± 1.2 vs 0.79 ± 0.49; P = .001). An algorithm based on EAS-PV distance and the 10-ms isochronal longitudinal/perpendicular diameter ratio predicted LVOT SOO with 91% sensitivity and 100% specificity. An algorithm based on the EAS-PV distance and the 10-ms isochronal longitudinal/perpendicular diameter ratio accurately predicts LVOT vs RVOT SOO in outflow tract VAs with EAS in the septal RVOT. Copyright © 2015 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.

An Orthogonal Evolutionary Algorithm With Learning Automata for Multiobjective Optimization.

PubMed

Dai, Cai; Wang, Yuping; Ye, Miao; Xue, Xingsi; Liu, Hailin

2016-12-01

Research on multiobjective optimization problems becomes one of the hottest topics of intelligent computation. In order to improve the search efficiency of an evolutionary algorithm and maintain the diversity of solutions, in this paper, the learning automata (LA) is first used for quantization orthogonal crossover (QOX), and a new fitness function based on decomposition is proposed to achieve these two purposes. Based on these, an orthogonal evolutionary algorithm with LA for complex multiobjective optimization problems with continuous variables is proposed. The experimental results show that in continuous states, the proposed algorithm is able to achieve accurate Pareto-optimal sets and wide Pareto-optimal fronts efficiently. Moreover, the comparison with the several existing well-known algorithms: nondominated sorting genetic algorithm II, decomposition-based multiobjective evolutionary algorithm, decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes, multiobjective optimization by LA, and multiobjective immune algorithm with nondominated neighbor-based selection, on 15 multiobjective benchmark problems, shows that the proposed algorithm is able to find more accurate and evenly distributed Pareto-optimal fronts than the compared ones.

An Effective Hybrid Evolutionary Algorithm for Solving the Numerical Optimization Problems

NASA Astrophysics Data System (ADS)

Qian, Xiaohong; Wang, Xumei; Su, Yonghong; He, Liu

2018-04-01

There are many different algorithms for solving complex optimization problems. Each algorithm has been applied successfully in solving some optimization problems, but not efficiently in other problems. In this paper the Cauchy mutation and the multi-parent hybrid operator are combined to propose a hybrid evolutionary algorithm based on the communication (Mixed Evolutionary Algorithm based on Communication), hereinafter referred to as CMEA. The basic idea of the CMEA algorithm is that the initial population is divided into two subpopulations. Cauchy mutation operators and multiple paternal crossover operators are used to perform two subpopulations parallelly to evolve recursively until the downtime conditions are met. While subpopulation is reorganized, the individual is exchanged together with information. The algorithm flow is given and the performance of the algorithm is compared using a number of standard test functions. Simulation results have shown that this algorithm converges significantly faster than FEP (Fast Evolutionary Programming) algorithm, has good performance in global convergence and stability and is superior to other compared algorithms.

Why don’t you use Evolutionary Algorithms in Big Data?

NASA Astrophysics Data System (ADS)

Stanovov, Vladimir; Brester, Christina; Kolehmainen, Mikko; Semenkina, Olga

2017-02-01

In this paper we raise the question of using evolutionary algorithms in the area of Big Data processing. We show that evolutionary algorithms provide evident advantages due to their high scalability and flexibility, their ability to solve global optimization problems and optimize several criteria at the same time for feature selection, instance selection and other data reduction problems. In particular, we consider the usage of evolutionary algorithms with all kinds of machine learning tools, such as neural networks and fuzzy systems. All our examples prove that Evolutionary Machine Learning is becoming more and more important in data analysis and we expect to see the further development of this field especially in respect to Big Data.

Evaluation of epidural analgesia for open major liver resection surgery from a US inpatient sample.

PubMed

Rosero, Eric B; Cheng, Gloria S; Khatri, Kinnari P; Joshi, Girish P

2014-10-01

The aim of this study was to assess the nationwide use of epidural analgesia (EA) and the incidence of postoperative complications in patients undergoing major liver resections (MLR) with and without EA in the United States. The 2001 to 2010 Nationwide Inpatient Sample was queried to identify adult patients undergoing MLR. A 1:1 matched cohort of patients having MLR with and without EA was assembled using propensity-score matching techniques. Differences in the rate of postoperative complications were compared between the matched groups. We identified 68,028 MLR. Overall, 5.9% of patients in the database had procedural codes for postoperative EA. A matched cohort of 802 patients per group was derived from the propensity-matching algorithm. Although use of EA was associated with more blood transfusions (relative risk, 1.36; 95% confidence interval, 1.12-1.65; P = 0.001) and longer hospital stay (median [interquartile range], 6 [5-8] vs 6 [4-8] days), the use of coagulation factors and the incidence of postoperative hemorrhage/hematomas or other postoperative complications were not higher in patients receiving EA. In conclusion, the use of EA for MLR is low, and EA does not seem to influence the incidence of postoperative complications. EA, however, was associated with an increased use of blood transfusions and a longer hospital stay.

Evaluation of epidural analgesia for open major liver resection surgery from a US inpatient sample

PubMed Central

Cheng, Gloria S.; Khatri, Kinnari P.; Joshi, Girish P.

2014-01-01

The aim of this study was to assess the nationwide use of epidural analgesia (EA) and the incidence of postoperative complications in patients undergoing major liver resections (MLR) with and without EA in the United States. The 2001 to 2010 Nationwide Inpatient Sample was queried to identify adult patients undergoing MLR. A 1:1 matched cohort of patients having MLR with and without EA was assembled using propensity-score matching techniques. Differences in the rate of postoperative complications were compared between the matched groups. We identified 68,028 MLR. Overall, 5.9% of patients in the database had procedural codes for postoperative EA. A matched cohort of 802 patients per group was derived from the propensity-matching algorithm. Although use of EA was associated with more blood transfusions (relative risk, 1.36; 95% confidence interval, 1.12–1.65; P = 0.001) and longer hospital stay (median [interquartile range], 6 [5–8] vs 6 [4–8] days), the use of coagulation factors and the incidence of postoperative hemorrhage/hematomas or other postoperative complications were not higher in patients receiving EA. In conclusion, the use of EA for MLR is low, and EA does not seem to influence the incidence of postoperative complications. EA, however, was associated with an increased use of blood transfusions and a longer hospital stay. PMID:25484494

A simulation study of harmonics regeneration in noise reduction for electric and acoustic stimulation.

PubMed

Hu, Yi

2010-05-01

Recent research results show that combined electric and acoustic stimulation (EAS) significantly improves speech recognition in noise, and it is generally established that access to the improved F0 representation of target speech, along with the glimpse cues, provide the EAS benefits. Under noisy listening conditions, noise signals degrade these important cues by introducing undesired temporal-frequency components and corrupting harmonics structure. In this study, the potential of combining noise reduction and harmonics regeneration techniques was investigated to further improve speech intelligibility in noise by providing improved beneficial cues for EAS. Three hypotheses were tested: (1) noise reduction methods can improve speech intelligibility in noise for EAS; (2) harmonics regeneration after noise reduction can further improve speech intelligibility in noise for EAS; and (3) harmonics sideband constraints in frequency domain (or equivalently, amplitude modulation in temporal domain), even deterministic ones, can provide additional benefits. Test results demonstrate that combining noise reduction and harmonics regeneration can significantly improve speech recognition in noise for EAS, and it is also beneficial to preserve the harmonics sidebands under adverse listening conditions. This finding warrants further work into the development of algorithms that regenerate harmonics and the related sidebands for EAS processing under noisy conditions.

Scheduling Earth Observing Satellites with Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna

2003-01-01

We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.

Neural processing of race during imitation: self-similarity versus social status

PubMed Central

Reynolds Losin, Elizabeth A.; Cross, Katy A.; Iacoboni, Marco; Dapretto, Mirella

2017-01-01

People preferentially imitate others who are similar to them or have high social status. Such imitative biases are thought to have evolved because they increase the efficiency of cultural acquisition. Here we focused on distinguishing between self-similarity and social status as two candidate mechanisms underlying neural responses to a person’s race during imitation. We used fMRI to measure neural responses when 20 African American (AA) and 20 European American (EA) young adults imitated AA, EA and Chinese American (CA) models and also passively observed their gestures and faces. We found that both AA and EA participants exhibited more activity in lateral fronto-parietal and visual regions when imitating AAs compared to EAs or CAs. These results suggest that racial self-similarity is not likely to modulate neural responses to race during imitation, in contrast with findings from previous neuroimaging studies of face perception and action observation. Furthermore, AA and EA participants associated AAs with lower social status than EAs or CAs, suggesting that the social status associated with different racial groups may instead modulate neural activity during imitation of individuals from those groups. Taken together, these findings suggest that neural responses to race during imitation are driven by socially-learned associations rather than self-similarity. This may reflect the adaptive role of imitation in social learning, where learning from higher-status models can be more beneficial. This study provides neural evidence consistent with evolutionary theories of cultural acquisition. PMID:23813738

Thought waves remotely affect the performance (output voltage) of photoelectric cells

NASA Astrophysics Data System (ADS)

Cao, Dayong; Cao, Daqing

2012-02-01

In our experiments, thought waves have been shown to be capable of changing (affecting) the output voltage of photovoltaic cells located from as far away as 1-3 meters. There are no wires between brain and photoelectric cell and so it is presumed only the thought waves act on the photoelectric cell. In continual rotations, the experiments tested different solar cells, measuring devices and lamps, and the experiments were done in different labs. The first experiment was conducted on Oct 2002. Tests are ongoing. Conclusions and assumptions include: 1) the slow thought wave has the energy of space-time as defined by C1.00007: The mass, energy, space and time systemic theory- MEST. Every process releases a field effect electrical vibration which be transmitted and focussed in particular paths; 2) the thought wave has the information of the order of tester; 3) the brain (with the physical system of MEST) and consciousness (with the spirit system of the mind, consciousness, emotion and desire-MECD) can produce the information (a part of them as the Genetic code); 4) through some algorithms such as ACO Ant Colony Optimization and EA Evolutionary Algorithm (or genetic algorithm) working in RAM, human can optimize the information. This Optimizational function is the intelligence; 5) In our experiments, not only can thought waves affect the voltage of the output photoelectric signals by its energy, but they can also selectively increase or decrease those photoelectric currents through remote consciousness interface and a conscious-brain information technology.

Evolutionary Multiobjective Design Targeting a Field Programmable Transistor Array

NASA Technical Reports Server (NTRS)

Aguirre, Arturo Hernandez; Zebulum, Ricardo S.; Coello, Carlos Coello

2004-01-01

This paper introduces the ISPAES algorithm for circuit design targeting a Field Programmable Transistor Array (FPTA). The use of evolutionary algorithms is common in circuit design problems, where a single fitness function drives the evolution process. Frequently, the design problem is subject to several goals or operating constraints, thus, designing a suitable fitness function catching all requirements becomes an issue. Such a problem is amenable for multi-objective optimization, however, evolutionary algorithms lack an inherent mechanism for constraint handling. This paper introduces ISPAES, an evolutionary optimization algorithm enhanced with a constraint handling technique. Several design problems targeting a FPTA show the potential of our approach.

Integrated pipeline for inferring the evolutionary history of a gene family embedded in the species tree: a case study on the STIMATE gene family.

PubMed

Song, Jia; Zheng, Sisi; Nguyen, Nhung; Wang, Youjun; Zhou, Yubin; Lin, Kui

2017-10-03

Because phylogenetic inference is an important basis for answering many evolutionary problems, a large number of algorithms have been developed. Some of these algorithms have been improved by integrating gene evolution models with the expectation of accommodating the hierarchy of evolutionary processes. To the best of our knowledge, however, there still is no single unifying model or algorithm that can take all evolutionary processes into account through a stepwise or simultaneous method. On the basis of three existing phylogenetic inference algorithms, we built an integrated pipeline for inferring the evolutionary history of a given gene family; this pipeline can model gene sequence evolution, gene duplication-loss, gene transfer and multispecies coalescent processes. As a case study, we applied this pipeline to the STIMATE (TMEM110) gene family, which has recently been reported to play an important role in store-operated Ca 2+ entry (SOCE) mediated by ORAI and STIM proteins. We inferred their phylogenetic trees in 69 sequenced chordate genomes. By integrating three tree reconstruction algorithms with diverse evolutionary models, a pipeline for inferring the evolutionary history of a gene family was developed, and its application was demonstrated.

Post-Starburst Galaxies At The End of The E+A Phase

NASA Astrophysics Data System (ADS)

Liu, Charles; Marinelli, Mariarosa; Chang, Madeleine; Lyczko, Camilla; Vega Orozco, Cecilia; SDSS-IV Collaboration

2018-06-01

Post-starburst galaxies, once thought to be rare curiosities, are now recognized to represent a key phase in the galaxy evolution. The post-starburst, or E+A phase, should however not be considered as a single, short-lived phenomenon; rather, it is an extended evolutionary process that occurs a galaxy transitions from an actively star-forming system into a quiescent one. We present a study of nearby galaxies at or near the end of the E+A phase, wherein all star formation has been quenched, the fossilized stellar population of the most recent starburst is highly localized, and the remainder of the galaxy's stellar population is old and quiescent. The luminosity and stellar age distribution of these "end-phase E+As" can provide insights into the evolution of galaxies onto and within the red sequence, from active to passive systems. This work is supported by National Science Foundation grants to CUNY College of Staten Island and the American Museum of Natural History; the College of Staten Island Office of Academic Affairs; the Sherman Fairchild Science Pathways Scholars Program (SP^2) at Barnard College; and the Alfred P. Sloan Foundation.

E+A Galaxy Properties and Post-Starburst Galaxy Evolution Data through SDSS-IV MaNGA and Illustris: A Co-Analysis

NASA Astrophysics Data System (ADS)

Ojanen, Winonah; Dudley, Raymond; Edwards, Kay; Gonzalez, Andrea; Johnson, Amalya; Kerrison, Nicole; Marinelli, Mariarosa; Melchert, Nancy; Liu, Charles; Sloan Collaboration, SDSS-IV MaNGA

2018-01-01

E+A galaxies (Elliptical + A-type stars) are post-starburst galaxies that have experienced a sudden quenching phase. Using previous research methods, 39 candidates out of 2,812 galaxies observed, or 1.4%, were selected from the SDSS-IV MaNGA survey. We then identified morphological characteristics of the 39 galaxies including stellar kinematics, Gini coefficient, gas density and distribution and stellar ages. To study the origin of how E+A galaxies evolved to their present state, galaxy simulation data from the Illustris simulation was utilized to identify similar quenched post-starburst candidates. Seven post-starburst candidates were identified through star formation rate histories of Illustris simulated galaxies. The evolution of these galaxies is studied from 0 to 13.8 billion years ago to identify what caused the starburst and quenching of the Illustris candidates. Similar morphological characteristics of Illustris post-starburst candidates are pulled from before, during, and post-starburst and compared to the same morphological characteristics of the E+A galaxies from SDSS-IV MaNGA. The characteristics and properties of the Illustris galaxies are used to identify the possible evolutionary histories of the observed E+A galaxies. This work was supported by grants AST-1460860 from the National Science Foundation and SDSS FAST/SSP-483 from the Alfred P. Sloan Foundation to the CUNY College of Staten Island.

An implementation of differential evolution algorithm for inversion of geoelectrical data

NASA Astrophysics Data System (ADS)

Balkaya, Çağlayan

2013-11-01

Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

Bio-inspired algorithms applied to molecular docking simulations.

PubMed

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

Dual-objective optimization of organic Rankine cycle (ORC) systems using genetic algorithm: a comparison between basic and recuperative cycles

NASA Astrophysics Data System (ADS)

Hayat, Nasir; Ameen, Muhammad Tahir; Tariq, Muhammad Kashif; Shah, Syed Nadeem Abbas; Naveed, Ahmad

2017-08-01

Exploitation of low potential waste thermal energy for useful net power output can be done by manipulating organic Rankine cycle systems. In the current article dual-objectives (η_{th} and SIC) optimization of ORC systems [basic organic Rankine cycle (BORC) and recuperative organic Rankine cycle (RORC)] has been done using non-dominated sorting genetic algorithm (II). Seven organic compounds (R-123, R-1234ze, R-152a, R-21, R-236ea, R-245ca and R-601) have been employed in basic cycle and four dry compounds (R-123, R-236ea, R-245ca and R-601) have been employed in recuperative cycle to investigate the behaviour of two systems and compare their performance. Sensitivity analyses show that recuperation boosts the thermodynamic behaviour of systems but it also raises specific investment cost significantly. R-21, R-245ca and R-601 show attractive performance in BORC whereas R-601 and R-236ea in RORC. RORC, due to higher total investment cost and operation & maintenance costs, has longer payback periods as compared to BORC.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Infrastructure system restoration planning using evolutionary algorithms

USGS Publications Warehouse

Corns, Steven; Long, Suzanna K.; Shoberg, Thomas G.

2016-01-01

This paper presents an evolutionary algorithm to address restoration issues for supply chain interdependent critical infrastructure. Rapid restoration of infrastructure after a large-scale disaster is necessary to sustaining a nation's economy and security, but such long-term restoration has not been investigated as thoroughly as initial rescue and recovery efforts. A model of the Greater Saint Louis Missouri area was created and a disaster scenario simulated. An evolutionary algorithm is used to determine the order in which the bridges should be repaired based on indirect costs. Solutions were evaluated based on the reduction of indirect costs and the restoration of transportation capacity. When compared to a greedy algorithm, the evolutionary algorithm solution reduced indirect costs by approximately 12.4% by restoring automotive travel routes for workers and re-establishing the flow of commodities across the three rivers in the Saint Louis area.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

NASA Astrophysics Data System (ADS)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

Scheduling Earth Observing Fleets Using Evolutionary Algorithms: Problem Description and Approach

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Morris, Robert; Clancy, Daniel (Technical Monitor)

2002-01-01

We describe work in progress concerning multi-instrument, multi-satellite scheduling. Most, although not all, Earth observing instruments currently in orbit are unique. In the relatively near future, however, we expect to see fleets of Earth observing spacecraft, many carrying nearly identical instruments. This presents a substantially new scheduling challenge. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of evolutionary algorithms to solve a set of Earth observing related model problems. Both the model problems and the software are described. Since the larger problems will require substantial computation and evolutionary algorithms are embarrassingly parallel, we discuss our parallelization techniques using dedicated and cycle-scavenged workstations.

System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft

NASA Technical Reports Server (NTRS)

Pullen, Samuel P.; Parkinson, Bradford W.

1994-01-01

This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

PubMed

Semenov, Mikhail A; Terkel, Dmitri A

2003-01-01

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Bell-Curve Based Evolutionary Optimization Algorithm

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.; Laba, K.; Kincaid, R.

1998-01-01

The paper presents an optimization algorithm that falls in the category of genetic, or evolutionary algorithms. While the bit exchange is the basis of most of the Genetic Algorithms (GA) in research and applications in America, some alternatives, also in the category of evolutionary algorithms, but use a direct, geometrical approach have gained popularity in Europe and Asia. The Bell-Curve Based Evolutionary Algorithm (BCB) is in this alternative category and is distinguished by the use of a combination of n-dimensional geometry and the normal distribution, the bell-curve, in the generation of the offspring. The tool for creating a child is a geometrical construct comprising a line connecting two parents and a weighted point on that line. The point that defines the child deviates from the weighted point in two directions: parallel and orthogonal to the connecting line, the deviation in each direction obeying a probabilistic distribution. Tests showed satisfactory performance of BCB. The principal advantage of BCB is its controllability via the normal distribution parameters and the geometrical construct variables.

The ergot alkaloid gene cluster: functional analyses and evolutionary aspects.

PubMed

Lorenz, Nicole; Haarmann, Thomas; Pazoutová, Sylvie; Jung, Manfred; Tudzynski, Paul

2009-01-01

Ergot alkaloids and their derivatives have been traditionally used as therapeutic agents in migraine, blood pressure regulation and help in childbirth and abortion. Their production in submerse culture is a long established biotechnological process. Ergot alkaloids are produced mainly by members of the genus Claviceps, with Claviceps purpurea as best investigated species concerning the biochemistry of ergot alkaloid synthesis (EAS). Genes encoding enzymes involved in EAS have been shown to be clustered; functional analyses of EAS cluster genes have allowed to assign specific functions to several gene products. Various Claviceps species differ with respect to their host specificity and their alkaloid content; comparison of the ergot alkaloid clusters in these species (and of clavine alkaloid clusters in other genera) yields interesting insights into the evolution of cluster structure. This review focuses on recently published and also yet unpublished data on the structure and evolution of the EAS gene cluster and on the function and regulation of cluster genes. These analyses have also significant biotechnological implications: the characterization of non-ribosomal peptide synthetases (NRPS) involved in the synthesis of the peptide moiety of ergopeptines opened interesting perspectives for the synthesis of ergot alkaloids; on the other hand, defined mutants could be generated producing interesting intermediates or only single peptide alkaloids (instead of the alkaloid mixtures usually produced by industrial strains).

Steady-State ALPS for Real-Valued Problems

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.

2009-01-01

The two objectives of this paper are to describe a steady-state version of the Age-Layered Population Structure (ALPS) Evolutionary Algorithm (EA) and to compare it against other GAs on real-valued problems. Motivation for this work comes from our previous success in demonstrating that a generational version of ALPS greatly improves search performance on a Genetic Programming problem. In making steady-state ALPS some modifications were made to the method for calculating age and the method for moving individuals up layers. To demonstrate that ALPS works well on real-valued problems we compare it against CMA-ES and Differential Evolution (DE) on five challenging, real-valued functions and on one real-world problem. While CMA-ES and DE outperform ALPS on the two unimodal test functions, ALPS is much better on the three multimodal test problems and on the real-world problem. Further examination shows that, unlike the other GAs, ALPS maintains a genotypically diverse population throughout the entire search process. These findings strongly suggest that the ALPS paradigm is better able to avoid premature convergence then the other GAs.

Battery parameterisation based on differential evolution via a boundary evolution strategy

NASA Astrophysics Data System (ADS)

Yang, Guangya

2014-01-01

Attention has been given to the battery modelling in the electric engineering field following the current development of renewable energy and electrification of transportation. The establishment of the equivalent circuit model of the battery requires data preparation and parameterisation. Besides, as the equivalent circuit model is an abstract map of the battery electric characteristics, the determination of the possible ranges of parameters can be a challenging task. In this paper, an efficient yet easy to implement method is proposed to parameterise the equivalent circuit model of batteries utilising the advances of evolutionary algorithms (EAs). Differential evolution (DE) is selected and modified to parameterise an equivalent circuit model of lithium-ion batteries. A boundary evolution strategy (BES) is developed and incorporated into the DE to update the parameter boundaries during the parameterisation. The method can parameterise the model without extensive data preparation. In addition, the approach can also estimate the initial SOC and the available capacity. The efficiency of the approach is verified through two battery packs, one is an 8-cell battery module and one from an electrical vehicle.

Practical advantages of evolutionary computation

NASA Astrophysics Data System (ADS)

Fogel, David B.

1997-10-01

Evolutionary computation is becoming a common technique for solving difficult, real-world problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as their ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine.

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

NASA Astrophysics Data System (ADS)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods through the evolutionary algorithms. In addition, experimental and statistical significance tests are carried out to study the applicability and effectiveness of the reviewed methods.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

NASA Astrophysics Data System (ADS)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Inferring epidemiologic dynamics from viral evolution: 2014–2015 Eurasian/North American highly pathogenic avian influenza viruses exceed transmission threshold, R0 = 1, in wild birds and poultry in North America

USGS Publications Warehouse

Grear, Daniel R.; Hall, Jeffrey S.; Dusek, Robert; Ip, Hon S.

2018-01-01

Highly pathogenic avian influenza virus (HPAIV) is a multihost pathogen with lineages that pose health risks for domestic birds, wild birds, and humans. One mechanism of intercontinental HPAIV spread is through wild bird reservoirs, and wild birds were the likely sources of a Eurasian (EA) lineage HPAIV into North America in 2014. The introduction resulted in several reassortment events with North American (NA) lineage low-pathogenic avian influenza viruses and the reassortant EA/NA H5N2 went on to cause one of the largest HPAIV poultry outbreaks in North America. We evaluated three hypotheses about novel HPAIV introduced into wild and domestic bird hosts: (i) transmission of novel HPAIVs in wild birds was restricted by mechanisms associated with highly pathogenic phenotypes; (ii) the HPAIV poultry outbreak was not self-sustaining and required viral input from wild birds; and (iii) reassortment of the EA H5N8 generated reassortant EA/NA AIVs with a fitness advantage over fully Eurasian lineages in North American wild birds. We used a time-rooted phylodynamic model that explicitly incorporated viral population dynamics with evolutionary dynamics to estimate the basic reproductive number (R0) and viral migration among host types in domestic and wild birds, as well as between the EA H5N8 and EA/NA H5N2 in wild birds. We did not find evidence to support hypothesis (i) or (ii) as our estimates of the transmission parameters suggested that the HPAIV outbreak met or exceeded the threshold for persistence in wild birds (R0 > 1) and poultry (R0 ≈ 1) with minimal estimated transmission among host types. There was also no evidence to support hypothesis (iii) because R0 values were similar among EA H5N8 and EA/NA H5N2 in wild birds. Our results suggest that this novel HPAIV and reassortments did not encounter any transmission barriers sufficient to prevent persistence when introduced to wild or domestic birds.

Inferring epidemiologic dynamics from viral evolution: 2014-2015 Eurasian/North American highly pathogenic avian influenza viruses exceed transmission threshold, R0 = 1, in wild birds and poultry in North America.

PubMed

Grear, Daniel A; Hall, Jeffrey S; Dusek, Robert J; Ip, Hon S

2018-04-01

Highly pathogenic avian influenza virus (HPAIV) is a multihost pathogen with lineages that pose health risks for domestic birds, wild birds, and humans. One mechanism of intercontinental HPAIV spread is through wild bird reservoirs, and wild birds were the likely sources of a Eurasian (EA) lineage HPAIV into North America in 2014. The introduction resulted in several reassortment events with North American (NA) lineage low-pathogenic avian influenza viruses and the reassortant EA/NA H5N2 went on to cause one of the largest HPAIV poultry outbreaks in North America. We evaluated three hypotheses about novel HPAIV introduced into wild and domestic bird hosts: (i) transmission of novel HPAIVs in wild birds was restricted by mechanisms associated with highly pathogenic phenotypes; (ii) the HPAIV poultry outbreak was not self-sustaining and required viral input from wild birds; and (iii) reassortment of the EA H5N8 generated reassortant EA/NA AIVs with a fitness advantage over fully Eurasian lineages in North American wild birds. We used a time-rooted phylodynamic model that explicitly incorporated viral population dynamics with evolutionary dynamics to estimate the basic reproductive number ( R 0 ) and viral migration among host types in domestic and wild birds, as well as between the EA H5N8 and EA/NA H5N2 in wild birds. We did not find evidence to support hypothesis (i) or (ii) as our estimates of the transmission parameters suggested that the HPAIV outbreak met or exceeded the threshold for persistence in wild birds ( R 0  > 1) and poultry ( R 0  ≈ 1) with minimal estimated transmission among host types. There was also no evidence to support hypothesis (iii) because R 0 values were similar among EA H5N8 and EA/NA H5N2 in wild birds. Our results suggest that this novel HPAIV and reassortments did not encounter any transmission barriers sufficient to prevent persistence when introduced to wild or domestic birds.

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

An evolutionary algorithm that constructs recurrent neural networks.

PubMed

Angeline, P J; Saunders, G M; Pollack, J B

1994-01-01

Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods.

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Development of antibiotic regimens using graph based evolutionary algorithms.

PubMed

Corns, Steven M; Ashlock, Daniel A; Bryden, Kenneth M

2013-12-01

This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A review of estimation of distribution algorithms in bioinformatics

PubMed Central

Armañanzas, Rubén; Inza, Iñaki; Santana, Roberto; Saeys, Yvan; Flores, Jose Luis; Lozano, Jose Antonio; Peer, Yves Van de; Blanco, Rosa; Robles, Víctor; Bielza, Concha; Larrañaga, Pedro

2008-01-01

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain. PMID:18822112

XtalOpt  version r9: An open-source evolutionary algorithm for crystal structure prediction

DOE PAGES

Falls, Zackary; Lonie, David C.; Avery, Patrick; ...

2015-10-23

This is a new version of XtalOpt, an evolutionary algorithm for crystal structure prediction available for download from the CPC library or the XtalOpt website, http://xtalopt.github.io. XtalOpt is published under the Gnu Public License (GPL), which is an open source license that is recognized by the Open Source Initiative. We have detailed the new version incorporates many bug-fixes and new features here and predict the crystal structure of a system from its stoichiometry alone, using evolutionary algorithms.

Association between polymorphisms in cancer-related genes and early onset of esophageal adenocarcinoma.

PubMed

Wu, I-Chen; Zhao, Yang; Zhai, Rihong; Liu, Geoffrey; Ter-Minassian, Monica; Asomaning, Kofi; Su, Li; Liu, Chen-Yu; Chen, Feng; Kulke, Matthew H; Heist, Rebecca S; Christiani, David C

2011-04-01

There is an increasing incidence of esophageal adenocarcinoma (EA) among younger people in the western populations. However, the association between genetic polymorphisms and the age of EA onset is unclear. In this study, 1330 functional/tagging single-nucleotide polymorphisms (SNPs) from 354 cancer-related genes were genotyped in 335 white EA patients. Twenty important SNPs that have the highest importance scores and lowest classification error rate were identified by the random forest algorithm to be associated with early onset of EA (age ≤ 55 years). Subsequent logistic regression analysis indicated that 10 SNPs (rs2070744 of NOS3, rs720321 of BCL2, rs17757541 of BCL2, rs11775256 of TNFRSF10A, rs1035142 of CASP8, rs2236302 of MMP14, rs4740363 of ABL1, rs696217 of GHRL, rs2445762 of CYP19A1, and rs11941492 of VEGFR2/KDR) were significantly associated with early onset of EA (≤55 vs >55 years, all P < .05 after adjusting for co-variates and false discovery rate). Among them, five SNPs in the NOS3, BCL2, TNFRSF10A, and CASP8 genes were known to be involved in apoptosis processes. In Kaplan-Meier analyses, rs2070744 of NOS3, rs720321 of BCL2, and rs1035142 of CASP8 were also significantly associated with early onset of EA. Moreover, there was a higher risk of developing EA at a younger age when one had more risk genotypes. In conclusion, polymorphisms in cancer-related genes, especially those in the apoptotic pathway, play an important role in the development of younger-aged EA in a dose-response manner.

Packets Distributing Evolutionary Algorithm Based on PSO for Ad Hoc Network

NASA Astrophysics Data System (ADS)

Xu, Xiao-Feng

2018-03-01

Wireless communication network has such features as limited bandwidth, changeful channel and dynamic topology, etc. Ad hoc network has lots of difficulties in accessing control, bandwidth distribution, resource assign and congestion control. Therefore, a wireless packets distributing Evolutionary algorithm based on PSO (DPSO)for Ad Hoc Network is proposed. Firstly, parameters impact on performance of network are analyzed and researched to obtain network performance effective function. Secondly, the improved PSO Evolutionary Algorithm is used to solve the optimization problem from local to global in the process of network packets distributing. The simulation results show that the algorithm can ensure fairness and timeliness of network transmission, as well as improve ad hoc network resource integrated utilization efficiency.

Estimating the ratios of the stationary distribution values for Markov chains modeling evolutionary algorithms.

PubMed

Mitavskiy, Boris; Cannings, Chris

2009-01-01

The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

PubMed

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case-based reasoning) obtaining with ENORA a classification rate of 0.9298, specificity of 0.9385, and sensitivity of 0.9364, with 14.2 interpretable fuzzy rules on average. Our proposal improves the accuracy and interpretability of the classifiers, compared with other non-evolutionary techniques. We also conclude that ENORA outperforms niched pre-selection and NSGA-II algorithms. Moreover, given that our multi-objective evolutionary methodology is non-combinational based on real parameter optimization, the time cost is significantly reduced compared with other evolutionary approaches existing in literature based on combinational optimization. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparison of some evolutionary algorithms for optimization of the path synthesis problem

NASA Astrophysics Data System (ADS)

Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

2018-01-01

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2001-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity. However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold. One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumbersome binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back and Dasgupta and Michalesicz. We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

Comparison of evolutionary algorithms for LPDA antenna optimization

NASA Astrophysics Data System (ADS)

Lazaridis, Pavlos I.; Tziris, Emmanouil N.; Zaharis, Zaharias D.; Xenos, Thomas D.; Cosmas, John P.; Gallion, Philippe B.; Holmes, Violeta; Glover, Ian A.

2016-08-01

A novel approach to broadband log-periodic antenna design is presented, where some of the most powerful evolutionary algorithms are applied and compared for the optimal design of wire log-periodic dipole arrays (LPDA) using Numerical Electromagnetics Code. The target is to achieve an optimal antenna design with respect to maximum gain, gain flatness, front-to-rear ratio (F/R) and standing wave ratio. The parameters of the LPDA optimized are the dipole lengths, the spacing between the dipoles, and the dipole wire diameters. The evolutionary algorithms compared are the Differential Evolution (DE), Particle Swarm (PSO), Taguchi, Invasive Weed (IWO), and Adaptive Invasive Weed Optimization (ADIWO). Superior performance is achieved by the IWO (best results) and PSO (fast convergence) algorithms.

Computational intelligence techniques in bioinformatics.

PubMed

Hassanien, Aboul Ella; Al-Shammari, Eiman Tamah; Ghali, Neveen I

2013-12-01

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included. Copyright © 2013 Elsevier Ltd. All rights reserved.

Reconciliation of Gene and Species Trees

PubMed Central

Rusin, L. Y.; Lyubetskaya, E. V.; Gorbunov, K. Y.; Lyubetsky, V. A.

2014-01-01

The first part of the paper briefly overviews the problem of gene and species trees reconciliation with the focus on defining and algorithmic construction of the evolutionary scenario. Basic ideas are discussed for the aspects of mapping definitions, costs of the mapping and evolutionary scenario, imposing time scales on a scenario, incorporating horizontal gene transfers, binarization and reconciliation of polytomous trees, and construction of species trees and scenarios. The review does not intend to cover the vast diversity of literature published on these subjects. Instead, the authors strived to overview the problem of the evolutionary scenario as a central concept in many areas of evolutionary research. The second part provides detailed mathematical proofs for the solutions of two problems: (i) inferring a gene evolution along a species tree accounting for various types of evolutionary events and (ii) trees reconciliation into a single species tree when only gene duplications and losses are allowed. All proposed algorithms have a cubic time complexity and are mathematically proved to find exact solutions. Solving algorithms for problem (ii) can be naturally extended to incorporate horizontal transfers, other evolutionary events, and time scales on the species tree. PMID:24800245

Evolving cell models for systems and synthetic biology.

PubMed

Cao, Hongqing; Romero-Campero, Francisco J; Heeb, Stephan; Cámara, Miguel; Krasnogor, Natalio

2010-03-01

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm's results as well as of the resulting evolved cell models.

AI-BL1.0: a program for automatic on-line beamline optimization using the evolutionary algorithm.

PubMed

Xi, Shibo; Borgna, Lucas Santiago; Zheng, Lirong; Du, Yonghua; Hu, Tiandou

2017-01-01

In this report, AI-BL1.0, an open-source Labview-based program for automatic on-line beamline optimization, is presented. The optimization algorithms used in the program are Genetic Algorithm and Differential Evolution. Efficiency was improved by use of a strategy known as Observer Mode for Evolutionary Algorithm. The program was constructed and validated at the XAFCA beamline of the Singapore Synchrotron Light Source and 1W1B beamline of the Beijing Synchrotron Radiation Facility.

The application of artificial intelligence in the optimal design of mechanical systems

NASA Astrophysics Data System (ADS)

Poteralski, A.; Szczepanik, M.

2016-11-01

The paper is devoted to new computational techniques in mechanical optimization where one tries to study, model, analyze and optimize very complex phenomena, for which more precise scientific tools of the past were incapable of giving low cost and complete solution. Soft computing methods differ from conventional (hard) computing in that, unlike hard computing, they are tolerant of imprecision, uncertainty, partial truth and approximation. The paper deals with an application of the bio-inspired methods, like the evolutionary algorithms (EA), the artificial immune systems (AIS) and the particle swarm optimizers (PSO) to optimization problems. Structures considered in this work are analyzed by the finite element method (FEM), the boundary element method (BEM) and by the method of fundamental solutions (MFS). The bio-inspired methods are applied to optimize shape, topology and material properties of 2D, 3D and coupled 2D/3D structures, to optimize the termomechanical structures, to optimize parameters of composites structures modeled by the FEM, to optimize the elastic vibrating systems to identify the material constants for piezoelectric materials modeled by the BEM and to identify parameters in acoustics problem modeled by the MFS.

Genetic algorithm for investigating flight MH370 in Indian Ocean using remotely sensed data

NASA Astrophysics Data System (ADS)

Marghany, Maged; Mansor, Shattri; Shariff, Abdul Rashid Bin Mohamed

2016-06-01

This study utilized Genetic algorithm (GA) for automatic detection and simulation trajectory movements of flight MH370 debris. In doing so, the Ocean Surface Topography Mission(OSTM) on the Jason- 2 satellite have been used within 1 and half year covers data to simulate the pattern of Flight MH370 debris movements across the southern Indian Ocean. Further, multi-objectives evolutionary algorithm also used to discriminate uncertainty of flight MH370 imagined and detection. The study shows that the ocean surface current speed is 0.5 m/s. This current patterns have developed a large anticlockwise gyre over a water depth of 8,000 m. The multi-objectives evolutionary algorithm suggested that objects are existed on satellite data are not flight MH370 debris. In addition, multiobjectives evolutionary algorithm suggested that the difficulties to acquire the exact location of flight MH370 due to complicated hydrodynamic movements across the southern Indian Ocean.

Experiments with a Parallel Multi-Objective Evolutionary Algorithm for Scheduling

NASA Technical Reports Server (NTRS)

Brown, Matthew; Johnston, Mark D.

2013-01-01

Evolutionary multi-objective algorithms have great potential for scheduling in those situations where tradeoffs among competing objectives represent a key requirement. One challenge, however, is runtime performance, as a consequence of evolving not just a single schedule, but an entire population, while attempting to sample the Pareto frontier as accurately and uniformly as possible. The growing availability of multi-core processors in end user workstations, and even laptops, has raised the question of the extent to which such hardware can be used to speed up evolutionary algorithms. In this paper we report on early experiments in parallelizing a Generalized Differential Evolution (GDE) algorithm for scheduling long-range activities on NASA's Deep Space Network. Initial results show that significant speedups can be achieved, but that performance does not necessarily improve as more cores are utilized. We describe our preliminary results and some initial suggestions from parallelizing the GDE algorithm. Directions for future work are outlined.

Linear antenna array optimization using flower pollination algorithm.

PubMed

Saxena, Prerna; Kothari, Ashwin

2016-01-01

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.

Bell-Curve Based Evolutionary Strategies for Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

2000-01-01

Evolutionary methods are exceedingly popular with practitioners of many fields; more so than perhaps any optimization tool in existence. Historically Genetic Algorithms (GAs) led the way in practitioner popularity (Reeves 1997). However, in the last ten years Evolutionary Strategies (ESs) and Evolutionary Programs (EPS) have gained a significant foothold (Glover 1998). One partial explanation for this shift is the interest in using GAs to solve continuous optimization problems. The typical GA relies upon a cumber-some binary representation of the design variables. An ES or EP, however, works directly with the real-valued design variables. For detailed references on evolutionary methods in general and ES or EP in specific see Back (1996) and Dasgupta and Michalesicz (1997). We call our evolutionary algorithm BCB (bell curve based) since it is based upon two normal distributions.

From evolutionary computation to the evolution of things.

PubMed

Eiben, Agoston E; Smith, Jim

2015-05-28

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.

A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm

PubMed Central

Shi, Jiao; Gong, Maoguo; Ma, Wenping; Jiao, Licheng

2014-01-01

How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems. PMID:24672330

Non-Evolutionary Algorithms for Scheduling Dependent Tasks in Distributed Heterogeneous Computing Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wayne F. Boyer; Gurdeep S. Hura

2005-09-01

The Problem of obtaining an optimal matching and scheduling of interdependent tasks in distributed heterogeneous computing (DHC) environments is well known to be an NP-hard problem. In a DHC system, task execution time is dependent on the machine to which it is assigned and task precedence constraints are represented by a directed acyclic graph. Recent research in evolutionary techniques has shown that genetic algorithms usually obtain more efficient schedules that other known algorithms. We propose a non-evolutionary random scheduling (RS) algorithm for efficient matching and scheduling of inter-dependent tasks in a DHC system. RS is a succession of randomized taskmore » orderings and a heuristic mapping from task order to schedule. Randomized task ordering is effectively a topological sort where the outcome may be any possible task order for which the task precedent constraints are maintained. A detailed comparison to existing evolutionary techniques (GA and PSGA) shows the proposed algorithm is less complex than evolutionary techniques, computes schedules in less time, requires less memory and fewer tuning parameters. Simulation results show that the average schedules produced by RS are approximately as efficient as PSGA schedules for all cases studied and clearly more efficient than PSGA for certain cases. The standard formulation for the scheduling problem addressed in this paper is Rm|prec|Cmax.,« less

Using modified fruit fly optimisation algorithm to perform the function test and case studies

NASA Astrophysics Data System (ADS)

Pan, Wen-Tsao

2013-06-01

Evolutionary computation is a computing mode established by practically simulating natural evolutionary processes based on the concept of Darwinian Theory, and it is a common research method. The main contribution of this paper was to reinforce the function of searching for the optimised solution using the fruit fly optimization algorithm (FOA), in order to avoid the acquisition of local extremum solutions. The evolutionary computation has grown to include the concepts of animal foraging behaviour and group behaviour. This study discussed three common evolutionary computation methods and compared them with the modified fruit fly optimization algorithm (MFOA). It further investigated the ability of the three mathematical functions in computing extreme values, as well as the algorithm execution speed and the forecast ability of the forecasting model built using the optimised general regression neural network (GRNN) parameters. The findings indicated that there was no obvious difference between particle swarm optimization and the MFOA in regards to the ability to compute extreme values; however, they were both better than the artificial fish swarm algorithm and FOA. In addition, the MFOA performed better than the particle swarm optimization in regards to the algorithm execution speed, and the forecast ability of the forecasting model built using the MFOA's GRNN parameters was better than that of the other three forecasting models.

Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem.

PubMed

Pourhassan, Mojgan; Neumann, Frank

2018-06-22

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which meta-heuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a Cluster-Based approach and a Node-Based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this paper, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the Node-Based approach solves the hard instance of the Cluster-Based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the Node-Based approach for a class of Euclidean instances.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks. PMID:24428926

Development of an Evolutionary Algorithm for the ab Initio Discovery of Two-Dimensional Materials

NASA Astrophysics Data System (ADS)

Revard, Benjamin Charles

Crystal structure prediction is an important first step on the path toward computational materials design. Increasingly robust methods have become available in recent years for computing many materials properties, but because properties are largely a function of crystal structure, the structure must be known before these methods can be brought to bear. In addition, structure prediction is particularly useful for identifying low-energy structures of subperiodic materials, such as two-dimensional (2D) materials, which may adopt unexpected structures that differ from those of the corresponding bulk phases. Evolutionary algorithms, which are heuristics for global optimization inspired by biological evolution, have proven to be a fruitful approach for tackling the problem of crystal structure prediction. This thesis describes the development of an improved evolutionary algorithm for structure prediction and several applications of the algorithm to predict the structures of novel low-energy 2D materials. The first part of this thesis contains an overview of evolutionary algorithms for crystal structure prediction and presents our implementation, including details of extending the algorithm to search for clusters, wires, and 2D materials, improvements to efficiency when running in parallel, improved composition space sampling, and the ability to search for partial phase diagrams. We then present several applications of the evolutionary algorithm to 2D systems, including InP, the C-Si and Sn-S phase diagrams, and several group-IV dioxides. This thesis makes use of the Cornell graduate school's "papers" option. Chapters 1 and 3 correspond to the first-author publications of Refs. [131] and [132], respectively, and chapter 2 will soon be submitted as a first-author publication. The material in chapter 4 is taken from Ref. [144], in which I share joint first-authorship. In this case I have included only my own contributions.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

A Strategic Approach to Joint Officer Management: Analysis and Modeling Results

DTIC Science & Technology

2009-01-01

rules. 5 Johnson and Wichern, 2002, p. 643. 6 Sullivan and Perry, 2004, p. 370. 7 Francesco Mola and Raffaele Miele, “Evolutionary Algorithms for...in Military Affairs, Newport, R.I.: Center for Naval Warfare Studies, 2003. Mola , Francesco, and Raffaele Miele, “Evolutionary Algorithms for

Knowledge Guided Evolutionary Algorithms in Financial Investing

ERIC Educational Resources Information Center

Wimmer, Hayden

2013-01-01

A large body of literature exists on evolutionary computing, genetic algorithms, decision trees, codified knowledge, and knowledge management systems; however, the intersection of these computing topics has not been widely researched. Moving through the set of all possible solutions--or traversing the search space--at random exhibits no control…

Optimal Wavelengths Selection Using Hierarchical Evolutionary Algorithm for Prediction of Firmness and Soluble Solids Content in Apples

USDA-ARS?s Scientific Manuscript database

Hyperspectral scattering is a promising technique for rapid and noninvasive measurement of multiple quality attributes of apple fruit. A hierarchical evolutionary algorithm (HEA) approach, in combination with subspace decomposition and partial least squares (PLS) regression, was proposed to select o...

A real negative selection algorithm with evolutionary preference for anomaly detection

NASA Astrophysics Data System (ADS)

Yang, Tao; Chen, Wen; Li, Tao

2017-04-01

Traditional real negative selection algorithms (RNSAs) adopt the estimated coverage (c0) as the algorithm termination threshold, and generate detectors randomly. With increasing dimensions, the data samples could reside in the low-dimensional subspace, so that the traditional detectors cannot effectively distinguish these samples. Furthermore, in high-dimensional feature space, c0 cannot exactly reflect the detectors set coverage rate for the nonself space, and it could lead the algorithm to be terminated unexpectedly when the number of detectors is insufficient. These shortcomings make the traditional RNSAs to perform poorly in high-dimensional feature space. Based upon "evolutionary preference" theory in immunology, this paper presents a real negative selection algorithm with evolutionary preference (RNSAP). RNSAP utilizes the "unknown nonself space", "low-dimensional target subspace" and "known nonself feature" as the evolutionary preference to guide the generation of detectors, thus ensuring the detectors can cover the nonself space more effectively. Besides, RNSAP uses redundancy to replace c0 as the termination threshold, in this way RNSAP can generate adequate detectors under a proper convergence rate. The theoretical analysis and experimental result demonstrate that, compared to the classical RNSA (V-detector), RNSAP can achieve a higher detection rate, but with less detectors and computing cost.

Comparing Evolutionary Programs and Evolutionary Pattern Search Algorithms: A Drug Docking Application

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.

1999-02-10

Evolutionary programs (EPs) and evolutionary pattern search algorithms (EPSAS) are two general classes of evolutionary methods for optimizing on continuous domains. The relative performance of these methods has been evaluated on standard global optimization test functions, and these results suggest that EPSAs more robustly converge to near-optimal solutions than EPs. In this paper we evaluate the relative performance of EPSAs and EPs on a real-world application: flexible ligand binding in the Autodock docking software. We compare the performance of these methods on a suite of docking test problems. Our results confirm that EPSAs and EPs have comparable performance, and theymore » suggest that EPSAs may be more robust on larger, more complex problems.« less

Genetic signature of Last Glacial Maximum regional refugia in a circum-Antarctic sea spider

PubMed Central

Soler-Membrives, Anna; Linse, Katrin; Miller, Karen J.

2017-01-01

The evolutionary history of Antarctic organisms is becoming increasingly important to understand and manage population trajectories under rapid environmental change. The Antarctic sea spider Nymphon australe, with an apparently large population size compared with other sea spider species, is an ideal target to look for molecular signatures of past climatic events. We analysed mitochondrial DNA of specimens collected from the Antarctic continent and two Antarctic islands (AI) to infer past population processes and understand current genetic structure. Demographic history analyses suggest populations survived in refugia during the Last Glacial Maximum. The high genetic diversity found in the Antarctic Peninsula and East Antarctic (EA) seems related to multiple demographic contraction–expansion events associated with deep-sea refugia, while the low genetic diversity in the Weddell Sea points to a more recent expansion from a shelf refugium. We suggest the genetic structure of N. australe from AI reflects recent colonization from the continent. At a local level, EA populations reveal generally low genetic differentiation, geographically and bathymetrically, suggesting limited restrictions to dispersal. Results highlight regional differences in demographic histories and how these relate to the variation in intensity of glaciation–deglaciation events around Antarctica, critical for the study of local evolutionary processes. These are valuable data for understanding the remarkable success of Antarctic pycnogonids, and how environmental changes have shaped the evolution and diversification of Southern Ocean benthic biodiversity. PMID:29134072

Genetic signature of Last Glacial Maximum regional refugia in a circum-Antarctic sea spider

NASA Astrophysics Data System (ADS)

Soler-Membrives, Anna; Linse, Katrin; Miller, Karen J.; Arango, Claudia P.

2017-10-01

The evolutionary history of Antarctic organisms is becoming increasingly important to understand and manage population trajectories under rapid environmental change. The Antarctic sea spider Nymphon australe, with an apparently large population size compared with other sea spider species, is an ideal target to look for molecular signatures of past climatic events. We analysed mitochondrial DNA of specimens collected from the Antarctic continent and two Antarctic islands (AI) to infer past population processes and understand current genetic structure. Demographic history analyses suggest populations survived in refugia during the Last Glacial Maximum. The high genetic diversity found in the Antarctic Peninsula and East Antarctic (EA) seems related to multiple demographic contraction-expansion events associated with deep-sea refugia, while the low genetic diversity in the Weddell Sea points to a more recent expansion from a shelf refugium. We suggest the genetic structure of N. australe from AI reflects recent colonization from the continent. At a local level, EA populations reveal generally low genetic differentiation, geographically and bathymetrically, suggesting limited restrictions to dispersal. Results highlight regional differences in demographic histories and how these relate to the variation in intensity of glaciation-deglaciation events around Antarctica, critical for the study of local evolutionary processes. These are valuable data for understanding the remarkable success of Antarctic pycnogonids, and how environmental changes have shaped the evolution and diversification of Southern Ocean benthic biodiversity.

International Conference on Artificial Immune Systems (1st) ICARIS 2002, held on 9, 10, and 11 September 2002

DTIC Science & Technology

2002-03-07

Michalewicz, Eds., Evolutionary Computation 1: Basic Algorithms and Operators, Institute of Physics, Bristol (UK), 2000. [3] David A. Van Veldhuizen ...2000. [4] Carlos A. Coello Coello, David A. Van Veldhuizen , and Gary B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer...Academic Publishers, 233 Spring St., New York, NY 10013, 2002. [5] David A. Van Veldhuizen , Multiobjective Evolution- ary Algorithms: Classifications

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

NASA Astrophysics Data System (ADS)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this application of the proposed algorithm, TSEA, with several state-of-the-art multiobjective optimization algorithms reveals that TSEA outperforms these algorithms by providing retrofit solutions with greater reliability for the same costs (i.e., closer to the Pareto-optimal front) after the algorithms are executed for the same number of generations. This research also demonstrates that TSEA competes with and, in some situations, outperforms state-of-the-art multiobjective optimization algorithms such as NSGA II and SPEA 2 when applied to classic bicriteria test problems in the technical literature and other complex, sizable real-world applications. The successful implementation of TSEA contributes to the safety of aeronautical structures by providing a systematic way to guide aircraft structural retrofitting efforts, as well as a potentially useful algorithm for a wide range of multiobjective optimization problems in engineering and other fields.

Evolutionary design optimization of traffic signals applied to Quito city.

PubMed

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

Evolutionary design optimization of traffic signals applied to Quito city

PubMed Central

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process. PMID:29236733

Controlling Tensegrity Robots Through Evolution

NASA Technical Reports Server (NTRS)

Iscen, Atil; Agogino, Adrian; SunSpiral, Vytas; Tumer, Kagan

2013-01-01

Tensegrity structures (built from interconnected rods and cables) have the potential to offer a revolutionary new robotic design that is light-weight, energy-efficient, robust to failures, capable of unique modes of locomotion, impact tolerant, and compliant (reducing damage between the robot and its environment). Unfortunately robots built from tensegrity structures are difficult to control with traditional methods due to their oscillatory nature, nonlinear coupling between components and overall complexity. Fortunately this formidable control challenge can be overcome through the use of evolutionary algorithms. In this paper we show that evolutionary algorithms can be used to efficiently control a ball-shaped tensegrity robot. Experimental results performed with a variety of evolutionary algorithms in a detailed soft-body physics simulator show that a centralized evolutionary algorithm performs 400 percent better than a hand-coded solution, while the multi-agent evolution performs 800 percent better. In addition, evolution is able to discover diverse control solutions (both crawling and rolling) that are robust against structural failures and can be adapted to a wide range of energy and actuation constraints. These successful controls will form the basis for building high-performance tensegrity robots in the near future.

Toward a unifying framework for evolutionary processes.

PubMed

Paixão, Tiago; Badkobeh, Golnaz; Barton, Nick; Çörüş, Doğan; Dang, Duc-Cuong; Friedrich, Tobias; Lehre, Per Kristian; Sudholt, Dirk; Sutton, Andrew M; Trubenová, Barbora

2015-10-21

The theory of population genetics and evolutionary computation have been evolving separately for nearly 30 years. Many results have been independently obtained in both fields and many others are unique to its respective field. We aim to bridge this gap by developing a unifying framework for evolutionary processes that allows both evolutionary algorithms and population genetics models to be cast in the same formal framework. The framework we present here decomposes the evolutionary process into its several components in order to facilitate the identification of similarities between different models. In particular, we propose a classification of evolutionary operators based on the defining properties of the different components. We cast several commonly used operators from both fields into this common framework. Using this, we map different evolutionary and genetic algorithms to different evolutionary regimes and identify candidates with the most potential for the translation of results between the fields. This provides a unified description of evolutionary processes and represents a stepping stone towards new tools and results to both fields. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

POCO-MOEA: Using Evolutionary Algorithms to Solve the Controller Placement Problem

DTIC Science & Technology

2016-03-24

to gather data on POCO-MOEA performance to a series of iv model networks. The algorithm’s behavior is then evaluated and compared to ex- haustive... evaluation of a third heuristic based on a Multi 3 Objective Evolutionary Algorithm (MOEA). This heuristic is modeled after one of the most well known MOEAs...researchers to extend into more realistic evaluations of the performance characteristics of SDN controllers, such as the use of simulators or live

Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

PubMed

Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

2012-06-07

We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.

PubMed

Błażej, Paweł; Wnȩtrzak, Małgorzata; Mackiewicz, Paweł

2016-12-01

One of theories explaining the present structure of canonical genetic code assumes that it was optimized to minimize harmful effects of amino acid replacements resulting from nucleotide substitutions and translational errors. A way to testify this concept is to find the optimal code under given criteria and compare it with the canonical genetic code. Unfortunately, the huge number of possible alternatives makes it impossible to find the optimal code using exhaustive methods in sensible time. Therefore, heuristic methods should be applied to search the space of possible solutions. Evolutionary algorithms (EA) seem to be ones of such promising approaches. This class of methods is founded both on mutation and crossover operators, which are responsible for creating and maintaining the diversity of candidate solutions. These operators possess dissimilar characteristics and consequently play different roles in the process of finding the best solutions under given criteria. Therefore, the effective searching for the potential solutions can be improved by applying both of them, especially when these operators are devised specifically for a given problem. To study this subject, we analyze the effectiveness of algorithms for various combinations of mutation and crossover probabilities under three models of the genetic code assuming different restrictions on its structure. To achieve that, we adapt the position based crossover operator for the most restricted model and develop a new type of crossover operator for the more general models. The applied fitness function describes costs of amino acid replacement regarding their polarity. Our results indicate that the usage of crossover operators can significantly improve the quality of the solutions. Moreover, the simulations with the crossover operator optimize the fitness function in the smaller number of generations than simulations without this operator. The optimal genetic codes without restrictions on their structure minimize the costs about 2.7 times better than the canonical genetic code. Interestingly, the optimal codes are dominated by amino acids characterized by polarity close to its average value for all amino acids. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Protein Structure Prediction with Evolutionary Algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hart, W.E.; Krasnogor, N.; Pelta, D.A.

1999-02-08

Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.

Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems.

PubMed

Fiuzy, Mohammad; Haddadnia, Javad; Mollania, Nasrin; Hashemian, Maryam; Hassanpour, Kazem

2012-01-01

Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA", which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA). We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer. According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%" on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients. Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples.

SiSeRHMap v1.0: a simulator for mapped seismic response using a hybrid model

NASA Astrophysics Data System (ADS)

Grelle, G.; Bonito, L.; Lampasi, A.; Revellino, P.; Guerriero, L.; Sappa, G.; Guadagno, F. M.

2015-06-01

SiSeRHMap is a computerized methodology capable of drawing up prediction maps of seismic response. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches and models are organized in a code-architecture composed of five interdependent modules. A GIS (Geographic Information System) Cubic Model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A metamodeling process confers a hybrid nature to the methodology. In this process, the one-dimensional linear equivalent analysis produces acceleration response spectra of shear wave velocity-thickness profiles, defined as trainers, which are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated Evolutionary Algorithm (EA) and the Levenberg-Marquardt Algorithm (LMA) as the final optimizer. In the final step, the GCM Maps Executor module produces a serial map-set of a stratigraphic seismic response at different periods, grid-solving the calibrated Spectra model. In addition, the spectra topographic amplification is also computed by means of a numerical prediction model. This latter is built to match the results of the numerical simulations related to isolate reliefs using GIS topographic attributes. In this way, different sets of seismic response maps are developed, on which, also maps of seismic design response spectra are defined by means of an enveloping technique.

Trajectory optimization of spacecraft high-thrust orbit transfer using a modified evolutionary algorithm

NASA Astrophysics Data System (ADS)

Shirazi, Abolfazl

2016-10-01

This article introduces a new method to optimize finite-burn orbital manoeuvres based on a modified evolutionary algorithm. Optimization is carried out based on conversion of the orbital manoeuvre into a parameter optimization problem by assigning inverse tangential functions to the changes in direction angles of the thrust vector. The problem is analysed using boundary delimitation in a common optimization algorithm. A method is introduced to achieve acceptable values for optimization variables using nonlinear simulation, which results in an enlarged convergence domain. The presented algorithm benefits from high optimality and fast convergence time. A numerical example of a three-dimensional optimal orbital transfer is presented and the accuracy of the proposed algorithm is shown.

Towards unbiased benchmarking of evolutionary and hybrid algorithms for real-valued optimisation

NASA Astrophysics Data System (ADS)

MacNish, Cara

2007-12-01

Randomised population-based algorithms, such as evolutionary, genetic and swarm-based algorithms, and their hybrids with traditional search techniques, have proven successful and robust on many difficult real-valued optimisation problems. This success, along with the readily applicable nature of these techniques, has led to an explosion in the number of algorithms and variants proposed. In order for the field to advance it is necessary to carry out effective comparative evaluations of these algorithms, and thereby better identify and understand those properties that lead to better performance. This paper discusses the difficulties of providing benchmarking of evolutionary and allied algorithms that is both meaningful and logistically viable. To be meaningful the benchmarking test must give a fair comparison that is free, as far as possible, from biases that favour one style of algorithm over another. To be logistically viable it must overcome the need for pairwise comparison between all the proposed algorithms. To address the first problem, we begin by attempting to identify the biases that are inherent in commonly used benchmarking functions. We then describe a suite of test problems, generated recursively as self-similar or fractal landscapes, designed to overcome these biases. For the second, we describe a server that uses web services to allow researchers to 'plug in' their algorithms, running on their local machines, to a central benchmarking repository.

Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

NASA Astrophysics Data System (ADS)

Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

2016-07-01

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

Tools for Accurate and Efficient Analysis of Complex Evolutionary Mechanisms in Microbial Genomes. Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakhleh, Luay

I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbialmore » genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.« less

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

PubMed

Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

2016-01-01

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG.

PubMed

Ullah, Khalil; Cescon, Corrado; Afsharipour, Babak; Merletti, Roberto

2014-12-01

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left-right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Expert-guided evolutionary algorithm for layout design of complex space stations

NASA Astrophysics Data System (ADS)

Qian, Zhiqin; Bi, Zhuming; Cao, Qun; Ju, Weiguo; Teng, Hongfei; Zheng, Yang; Zheng, Siyu

2017-08-01

The layout of a space station should be designed in such a way that different equipment and instruments are placed for the station as a whole to achieve the best overall performance. The station layout design is a typical nondeterministic polynomial problem. In particular, how to manage the design complexity to achieve an acceptable solution within a reasonable timeframe poses a great challenge. In this article, a new evolutionary algorithm has been proposed to meet such a challenge. It is called as the expert-guided evolutionary algorithm with a tree-like structure decomposition (EGEA-TSD). Two innovations in EGEA-TSD are (i) to deal with the design complexity, the entire design space is divided into subspaces with a tree-like structure; it reduces the computation and facilitates experts' involvement in the solving process. (ii) A human-intervention interface is developed to allow experts' involvement in avoiding local optimums and accelerating convergence. To validate the proposed algorithm, the layout design of one-space station is formulated as a multi-disciplinary design problem, the developed algorithm is programmed and executed, and the result is compared with those from other two algorithms; it has illustrated the superior performance of the proposed EGEA-TSD.

Multiobjective optimisation design for enterprise system operation in the case of scheduling problem with deteriorating jobs

NASA Astrophysics Data System (ADS)

Wang, Hongfeng; Fu, Yaping; Huang, Min; Wang, Junwei

2016-03-01

The operation process design is one of the key issues in the manufacturing and service sectors. As a typical operation process, the scheduling with consideration of the deteriorating effect has been widely studied; however, the current literature only studied single function requirement and rarely considered the multiple function requirements which are critical for a real-world scheduling process. In this article, two function requirements are involved in the design of a scheduling process with consideration of the deteriorating effect and then formulated into two objectives of a mathematical programming model. A novel multiobjective evolutionary algorithm is proposed to solve this model with combination of three strategies, i.e. a multiple population scheme, a rule-based local search method and an elitist preserve strategy. To validate the proposed model and algorithm, a series of randomly-generated instances are tested and the experimental results indicate that the model is effective and the proposed algorithm can achieve the satisfactory performance which outperforms the other state-of-the-art multiobjective evolutionary algorithms, such as nondominated sorting genetic algorithm II and multiobjective evolutionary algorithm based on decomposition, on all the test instances.

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

NASA Astrophysics Data System (ADS)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

Stochastic Evolutionary Algorithms for Planning Robot Paths

NASA Technical Reports Server (NTRS)

Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

2006-01-01

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

PubMed Central

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. PMID:24701158

An adaptive evolutionary algorithm for traveling salesman problem with precedence constraints.

PubMed

Sung, Jinmo; Jeong, Bongju

2014-01-01

Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

An Evolutionary Algorithm to Generate Ellipsoid Detectors for Negative Selection

DTIC Science & Technology

2005-03-21

of Congress on Evolutionary Computation. Honolulu,. 58. Lamont, Gary B., Robert E. Marmelstein, and David A. Van Veldhuizen . A Distributed Architecture...antibody and an antigen is a function of several processes including electrostatic interactions, hydrogen bonding, van der Waals interaction, and others [20...Kelly, Patrick M., Don R. Hush, and James M. White. “An Adaptive Algorithm for Modifying Hyperellipsoidal Decision Surfaces”. Journal of Artificial

Automated Antenna Design with Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Hornby, Gregory S.; Globus, Al; Linden, Derek S.; Lohn, Jason D.

2006-01-01

Current methods of designing and optimizing antennas by hand are time and labor intensive, and limit complexity. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions. In recent years, evolutionary algorithms have shown great promise in finding practical solutions in large, poorly understood design spaces. In particular, spacecraft antenna design has proven tractable to evolutionary design techniques. Researchers have been investigating evolutionary antenna design and optimization since the early 1990s, and the field has grown in recent years as computer speed has increased and electromagnetic simulators have improved. Two requirements-compliant antennas, one for ST5 and another for TDRS-C, have been automatically designed by evolutionary algorithms. The ST5 antenna is slated to fly this year, and a TDRS-C phased array element has been fabricated and tested. Such automated evolutionary design is enabled by medium-to-high quality simulators and fast modern computers to evaluate computer-generated designs. Evolutionary algorithms automate cut-and-try engineering, substituting automated search though millions of potential designs for intelligent search by engineers through a much smaller number of designs. For evolutionary design, the engineer chooses the evolutionary technique, parameters and the basic form of the antenna, e.g., single wire for ST5 and crossed-element Yagi for TDRS-C. Evolutionary algorithms then search for optimal configurations in the space defined by the engineer. NASA's Space Technology 5 (ST5) mission will launch three small spacecraft to test innovative concepts and technologies. Advanced evolutionary algorithms were used to automatically design antennas for ST5. The combination of wide beamwidth for a circularly-polarized wave and wide impedance bandwidth made for a challenging antenna design problem. From past experience in designing wire antennas, we chose to constrain the evolutionary design to a monopole wire antenna. The results of the runs produced requirements-compliant antennas that were subsequently fabricated and tested. The evolved antenna has a number of advantages with regard to power consumption, fabrication time and complexity, and performance. Lower power requirements result from achieving high gain across a wider range of elevation angles, thus allowing a broader range of angles over which maximum data throughput can be achieved. Since the evolved antenna does not require a phasing circuit, less design and fabrication work is required. In terms of overall work, the evolved antenna required approximately three person-months to design and fabricate whereas the conventional antenna required about five. Furthermore, when the mission was modified and new orbital parameters selected, a redesign of the antenna to new requirements was required. The evolutionary system was rapidly modified and a new antenna evolved in a few weeks. The evolved antenna was shown to be compliant to the ST5 mission requirements. It has an unusual organic looking structure, one that expert antenna designers would not likely produce. This antenna has been tested, baselined and is scheduled to fly this year. In addition to the ST5 antenna, our laboratory has evolved an S-band phased array antenna element design that meets the requirements for NASA's TDRS-C communications satellite scheduled for launch early next decade. A combination of fairly broad bandwidth, high efficiency and circular polarization at high gain made for another challenging design problem. We chose to constrain the evolutionary design to a crossed-element Yagi antenna. The specification called for two types of elements, one for receive only and one for transmit/receive. We were able to evolve a single element design that meets both specifications thereby simplifying the antenna and reducing testing and integration costs. The highest performance antenna found using a getic algorithm and stochastic hill-climbing has been fabricated and tested. Laboratory results correspond well with simulation. Aerospace component design is an expensive and important step in space development. Evolutionary design can make a significant contribution wherever sufficiently fast, accurate and capable software simulators are available. We have demonstrated successful real-world design in the spacecraft antenna domain; and there is good reason to believe that these results could be replicated in other design spaces.

Optimising operational amplifiers by evolutionary algorithms and gm/Id method

NASA Astrophysics Data System (ADS)

Tlelo-Cuautle, E.; Sanabria-Borbon, A. C.

2016-10-01

The evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is applied herein in the optimisation of operational transconductance amplifiers. NSGA-II is accelerated by applying the gm/Id method to estimate reduced search spaces associated to widths (W) and lengths (L) of the metal-oxide-semiconductor field-effect-transistor (MOSFETs), and to guarantee their appropriate bias levels conditions. In addition, we introduce an integer encoding for the W/L sizes of the MOSFETs to avoid a post-processing step for rounding-off their values to be multiples of the integrated circuit fabrication technology. Finally, from the feasible solutions generated by NSGA-II, we introduce a second optimisation stage to guarantee that the final feasible W/L sizes solutions support process, voltage and temperature (PVT) variations. The optimisation results lead us to conclude that the gm/Id method and integer encoding are quite useful to accelerate the convergence of the evolutionary algorithm NSGA-II, while the second optimisation stage guarantees robustness of the feasible solutions to PVT variations.

Multi-objective optimization in spatial planning: Improving the effectiveness of multi-objective evolutionary algorithms (non-dominated sorting genetic algorithm II)

NASA Astrophysics Data System (ADS)

Karakostas, Spiros

2015-05-01

The multi-objective nature of most spatial planning initiatives and the numerous constraints that are introduced in the planning process by decision makers, stakeholders, etc., synthesize a complex spatial planning context in which the concept of solid and meaningful optimization is a unique challenge. This article investigates new approaches to enhance the effectiveness of multi-objective evolutionary algorithms (MOEAs) via the adoption of a well-known metaheuristic: the non-dominated sorting genetic algorithm II (NSGA-II). In particular, the contribution of a sophisticated crossover operator coupled with an enhanced initialization heuristic is evaluated against a series of metrics measuring the effectiveness of MOEAs. Encouraging results emerge for both the convergence rate of the evolutionary optimization process and the occupation of valuable regions of the objective space by non-dominated solutions, facilitating the work of spatial planners and decision makers. Based on the promising behaviour of both heuristics, topics for further research are proposed to improve their effectiveness.

Research on Novel Algorithms for Smart Grid Reliability Assessment and Economic Dispatch

NASA Astrophysics Data System (ADS)

Luo, Wenjin

In this dissertation, several studies of electric power system reliability and economy assessment methods are presented. To be more precise, several algorithms in evaluating power system reliability and economy are studied. Furthermore, two novel algorithms are applied to this field and their simulation results are compared with conventional results. As the electrical power system develops towards extra high voltage, remote distance, large capacity and regional networking, the application of a number of new technique equipments and the electric market system have be gradually established, and the results caused by power cut has become more and more serious. The electrical power system needs the highest possible reliability due to its complication and security. In this dissertation the Boolean logic Driven Markov Process (BDMP) method is studied and applied to evaluate power system reliability. This approach has several benefits. It allows complex dynamic models to be defined, while maintaining its easy readability as conventional methods. This method has been applied to evaluate IEEE reliability test system. The simulation results obtained are close to IEEE experimental data which means that it could be used for future study of the system reliability. Besides reliability, modern power system is expected to be more economic. This dissertation presents a novel evolutionary algorithm named as quantum evolutionary membrane algorithm (QEPS), which combines the concept and theory of quantum-inspired evolutionary algorithm and membrane computation, to solve the economic dispatch problem in renewable power system with on land and offshore wind farms. The case derived from real data is used for simulation tests. Another conventional evolutionary algorithm is also used to solve the same problem for comparison. The experimental results show that the proposed method is quick and accurate to obtain the optimal solution which is the minimum cost for electricity supplied by wind farm system.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tumuluru, Jaya Shankar; McCulloch, Richard Chet James

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

PubMed Central

Cao, Leilei; Xu, Lihong; Goodman, Erik D.

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

Cao, Leilei; Xu, Lihong; Goodman, Erik D

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

A global optimization algorithm inspired in the behavior of selfish herds.

PubMed

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparison of multiobjective evolutionary algorithms: empirical results.

PubMed

Zitzler, E; Deb, K; Thiele, L

2000-01-01

In this paper, we provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions. Each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front (e.g., multimodality and deception). By investigating these different problem features separately, it is possible to predict the kind of problems to which a certain technique is or is not well suited. However, in contrast to what was suspected beforehand, the experimental results indicate a hierarchy of the algorithms under consideration. Furthermore, the emerging effects are evidence that the suggested test functions provide sufficient complexity to compare multiobjective optimizers. Finally, elitism is shown to be an important factor for improving evolutionary multiobjective search.

A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw

2001-01-01

An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).

Multi-Objective and Multidisciplinary Design Optimisation (MDO) of UAV Systems using Hierarchical Asynchronous Parallel Evolutionary Algorithms

DTIC Science & Technology

2007-09-17

been proposed; these include a combination of variable fidelity models, parallelisation strategies and hybridisation techniques (Coello, Veldhuizen et...Coello et al (Coello, Veldhuizen et al. 2002). 4.4.2 HIERARCHICAL POPULATION TOPOLOGY A hierarchical population topology, when integrated into...to hybrid parallel Multi-Objective Evolutionary Algorithms (pMOEA) (Cantu-Paz 2000; Veldhuizen , Zydallis et al. 2003); it uses a master slave

Evolvable Hardware for Space Applications

NASA Technical Reports Server (NTRS)

Lohn, Jason; Globus, Al; Hornby, Gregory; Larchev, Gregory; Kraus, William

2004-01-01

This article surveys the research of the Evolvable Systems Group at NASA Ames Research Center. Over the past few years, our group has developed the ability to use evolutionary algorithms in a variety of NASA applications ranging from spacecraft antenna design, fault tolerance for programmable logic chips, atomic force field parameter fitting, analog circuit design, and earth observing satellite scheduling. In some of these applications, evolutionary algorithms match or improve on human performance.

An evolutionary algorithm for large traveling salesman problems.

PubMed

Tsai, Huai-Kuang; Yang, Jinn-Moon; Tsai, Yuan-Fang; Kao, Cheng-Yan

2004-08-01

This work proposes an evolutionary algorithm, called the heterogeneous selection evolutionary algorithm (HeSEA), for solving large traveling salesman problems (TSP). The strengths and limitations of numerous well-known genetic operators are first analyzed, along with local search methods for TSPs from their solution qualities and mechanisms for preserving and adding edges. Based on this analysis, a new approach, HeSEA is proposed which integrates edge assembly crossover (EAX) and Lin-Kernighan (LK) local search, through family competition and heterogeneous pairing selection. This study demonstrates experimentally that EAX and LK can compensate for each other's disadvantages. Family competition and heterogeneous pairing selections are used to maintain the diversity of the population, which is especially useful for evolutionary algorithms in solving large TSPs. The proposed method was evaluated on 16 well-known TSPs in which the numbers of cities range from 318 to 13509. Experimental results indicate that HeSEA performs well and is very competitive with other approaches. The proposed method can determine the optimum path when the number of cities is under 10,000 and the mean solution quality is within 0.0074% above the optimum for each test problem. These findings imply that the proposed method can find tours robustly with a fixed small population and a limited family competition length in reasonable time, when used to solve large TSPs.

Differential Natural Selection of Human Zinc Transporter Genes between African and Non-African Populations

PubMed Central

Zhang, Chao; Li, Jing; Tian, Lei; Lu, Dongsheng; Yuan, Kai; Yuan, Yuan; Xu, Shuhua

2015-01-01

Zinc transporters play important roles in all eukaryotes by maintaining the rational zinc concentration in cells. However, the diversity of zinc transporter genes (ZTGs) remains poorly studied. Here, we investigated the genetic diversity of 24 human ZTGs based on the 1000 Genomes data. Some ZTGs show small population differences, such as SLC30A6 with a weighted-average FST (WA-FST = 0.015), while other ZTGs exhibit considerably large population differences, such as SLC30A9 (WA-FST = 0.284). Overall, ZTGs harbor many more highly population-differentiated variants compared with random genes. Intriguingly, we found that SLC30A9 was underlying natural selection in both East Asians (EAS) and Africans (AFR) but in different directions. Notably, a non-synonymous variant (rs1047626) in SLC30A9 is almost fixed with 96.4% A in EAS and 92% G in AFR, respectively. Consequently, there are two different functional haplotypes exhibiting dominant abundance in AFR and EAS, respectively. Furthermore, a strong correlation was observed between the haplotype frequencies of SLC30A9 and distributions of zinc contents in soils or crops. We speculate that the genetic differentiation of ZTGs could directly contribute to population heterogeneity in zinc transporting capabilities and local adaptations of human populations in regard to the local zinc state or diets, which have both evolutionary and medical implications. PMID:25927708

An Energy-Aware Trajectory Optimization Layer for sUAS

NASA Astrophysics Data System (ADS)

Silva, William A.

The focus of this work is the implementation of an energy-aware trajectory optimization algorithm that enables small unmanned aircraft systems (sUAS) to operate in unknown, dynamic severe weather environments. The software is designed as a component of an Energy-Aware Dynamic Data Driven Application System (EA-DDDAS) for sUAS. This work addresses the challenges of integrating and executing an online trajectory optimization algorithm during mission operations in the field. Using simplified aircraft kinematics, the energy-aware algorithm enables extraction of kinetic energy from measured winds to optimize thrust use and endurance during flight. The optimization layer, based upon a nonlinear program formulation, extracts energy by exploiting strong wind velocity gradients in the wind field, a process known as dynamic soaring. The trajectory optimization layer extends the energy-aware path planner developed by Wenceslao Shaw-Cortez te{Shaw-cortez2013} to include additional mission configurations, simulations with a 6-DOF model, and validation of the system with flight testing in June 2015 in Lubbock, Texas. The trajectory optimization layer interfaces with several components within the EA-DDDAS to provide an sUAS with optimal flight trajectories in real-time during severe weather. As a result, execution timing, data transfer, and scalability are considered in the design of the software. Severe weather also poses a measure of unpredictability to the system with respect to communication between systems and available data resources during mission operations. A heuristic mission tree with different cost functions and constraints is implemented to provide a level of adaptability to the optimization layer. Simulations and flight experiments are performed to assess the efficacy of the trajectory optimization layer. The results are used to assess the feasibility of flying dynamic soaring trajectories with existing controllers as well as to verify the interconnections between EA-DDDAS components. Results also demonstrate the usage of the trajectory optimization layer in conjunction with a lattice-based path planner as a method of guiding the optimization layer and stitching together subsequent trajectories.

A controllable sensor management algorithm capable of learning

NASA Astrophysics Data System (ADS)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

PubMed

Capitanescu, F; Rege, S; Marvuglia, A; Benetto, E; Ahmadi, A; Gutiérrez, T Navarrete; Tiruta-Barna, L

2016-07-15

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

PubMed

Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

PubMed Central

Deng, Qianwang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed. PMID:28458687

Implementation and comparative analysis of the optimisations produced by evolutionary algorithms for the parameter extraction of PSP MOSFET model

NASA Astrophysics Data System (ADS)

Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.

2016-05-01

The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.

Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Jirapong, Peeraool; Ongsakul, Weerakorn

2008-10-01

This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.

An Analytical Framework for Runtime of a Class of Continuous Evolutionary Algorithms.

PubMed

Zhang, Yushan; Hu, Guiwu

2015-01-01

Although there have been many studies on the runtime of evolutionary algorithms in discrete optimization, relatively few theoretical results have been proposed on continuous optimization, such as evolutionary programming (EP). This paper proposes an analysis of the runtime of two EP algorithms based on Gaussian and Cauchy mutations, using an absorbing Markov chain. Given a constant variation, we calculate the runtime upper bound of special Gaussian mutation EP and Cauchy mutation EP. Our analysis reveals that the upper bounds are impacted by individual number, problem dimension number n, searching range, and the Lebesgue measure of the optimal neighborhood. Furthermore, we provide conditions whereby the average runtime of the considered EP can be no more than a polynomial of n. The condition is that the Lebesgue measure of the optimal neighborhood is larger than a combinatorial calculation of an exponential and the given polynomial of n.

From prompt gamma distribution to dose: a novel approach combining an evolutionary algorithm and filtering based on Gaussian-powerlaw convolutions.

PubMed

Schumann, A; Priegnitz, M; Schoene, S; Enghardt, W; Rohling, H; Fiedler, F

2016-10-07

Range verification and dose monitoring in proton therapy is considered as highly desirable. Different methods have been developed worldwide, like particle therapy positron emission tomography (PT-PET) and prompt gamma imaging (PGI). In general, these methods allow for a verification of the proton range. However, quantification of the dose from these measurements remains challenging. For the first time, we present an approach for estimating the dose from prompt γ-ray emission profiles. It combines a filtering procedure based on Gaussian-powerlaw convolution with an evolutionary algorithm. By means of convolving depth dose profiles with an appropriate filter kernel, prompt γ-ray depth profiles are obtained. In order to reverse this step, the evolutionary algorithm is applied. The feasibility of this approach is demonstrated for a spread-out Bragg-peak in a water target.

SiSeRHMap v1.0: a simulator for mapped seismic response using a hybrid model

NASA Astrophysics Data System (ADS)

Grelle, Gerardo; Bonito, Laura; Lampasi, Alessandro; Revellino, Paola; Guerriero, Luigi; Sappa, Giuseppe; Guadagno, Francesco Maria

2016-04-01

The SiSeRHMap (simulator for mapped seismic response using a hybrid model) is a computerized methodology capable of elaborating prediction maps of seismic response in terms of acceleration spectra. It was realized on the basis of a hybrid model which combines different approaches and models in a new and non-conventional way. These approaches and models are organized in a code architecture composed of five interdependent modules. A GIS (geographic information system) cubic model (GCM), which is a layered computational structure based on the concept of lithodynamic units and zones, aims at reproducing a parameterized layered subsoil model. A meta-modelling process confers a hybrid nature to the methodology. In this process, the one-dimensional (1-D) linear equivalent analysis produces acceleration response spectra for a specified number of site profiles using one or more input motions. The shear wave velocity-thickness profiles, defined as trainers, are randomly selected in each zone. Subsequently, a numerical adaptive simulation model (Emul-spectra) is optimized on the above trainer acceleration response spectra by means of a dedicated evolutionary algorithm (EA) and the Levenberg-Marquardt algorithm (LMA) as the final optimizer. In the final step, the GCM maps executor module produces a serial map set of a stratigraphic seismic response at different periods, grid solving the calibrated Emul-spectra model. In addition, the spectra topographic amplification is also computed by means of a 3-D validated numerical prediction model. This model is built to match the results of the numerical simulations related to isolate reliefs using GIS morphometric data. In this way, different sets of seismic response maps are developed on which maps of design acceleration response spectra are also defined by means of an enveloping technique.

A comparative study of corrugated horn design by evolutionary techniques

NASA Technical Reports Server (NTRS)

Hoorfar, A.

2003-01-01

Here an evolutionary programming algorithm is used to optimize the pattern of a corrugated circular horn subject to various constraints on return loss, antenna beamwidth, pattern circularity, and low cross polarization.

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

PubMed

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Fast stochastic algorithm for simulating evolutionary population dynamics

NASA Astrophysics Data System (ADS)

Tsimring, Lev; Hasty, Jeff; Mather, William

2012-02-01

Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

Multiobjective optimization of temporal processes.

PubMed

Song, Zhe; Kusiak, Andrew

2010-06-01

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.

Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel

PubMed Central

Akbari, Mohsen; Manesh, Mohsen Riahi

2014-01-01

In diversity combining at the receiver, the output signal-to-noise ratio (SNR) is often maximized by using the maximal ratio combining (MRC) provided that the channel is perfectly estimated at the receiver. However, channel estimation is rarely perfect in practice, which results in deteriorating the system performance. In this paper, an imperialistic competitive algorithm (ICA) is proposed and compared with two other evolutionary based algorithms, namely, particle swarm optimization (PSO) and genetic algorithm (GA), for diversity combining of signals travelling across the imperfect channels. The proposed algorithm adjusts the combiner weights of the received signal components in such a way that maximizes the SNR and minimizes the bit error rate (BER). The results indicate that the proposed method eliminates the need of channel estimation and can outperform the conventional diversity combining methods. PMID:25045725

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

ERIC Educational Resources Information Center

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

NASA Astrophysics Data System (ADS)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi

In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

Introduction of a New Diagnostic Method for Breast Cancer Based on Fine Needle Aspiration (FNA) Test Data and Combining Intelligent Systems

PubMed Central

Fiuzy, Mohammad; Haddadnia, Javad; Mollania, Nasrin; Hashemian, Maryam; Hassanpour, Kazem

2012-01-01

Background Accurate Diagnosis of Breast Cancer is of prime importance. Fine Needle Aspiration test or "FNA”, which has been used for several years in Europe, is a simple, inexpensive, noninvasive and accurate technique for detecting breast cancer. Expending the suitable features of the Fine Needle Aspiration results is the most important diagnostic problem in early stages of breast cancer. In this study, we introduced a new algorithm that can detect breast cancer based on combining artificial intelligent system and Fine Needle Aspiration (FNA). Methods We studied the Features of Wisconsin Data Base Cancer which contained about 569 FNA test samples (212 patient samples (malignant) and 357 healthy samples (benign)). In this research, we combined Artificial Intelligence Approaches, such as Evolutionary Algorithm (EA) with Genetic Algorithm (GA), and also used Exact Classifier Systems (here by Fuzzy C-Means (FCM)) to separate malignant from benign samples. Furthermore, we examined artificial Neural Networks (NN) to identify the model and structure. This research proposed a new algorithm for an accurate diagnosis of breast cancer. Results According to Wisconsin Data Base Cancer (WDBC) data base, 62.75% of samples were benign, and 37.25% were malignant. After applying the proposed algorithm, we achieved high detection accuracy of about "96.579%” on 205 patients who were diagnosed as having breast cancer. It was found that the method had 93% sensitivity, 73% specialty, 65% positive predictive value, and 95% negative predictive value, respectively. If done by experts, Fine Needle Aspiration (FNA) can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. FNA can be the first line of diagnosis in women with breast masses, at least in deprived regions, and may increase health standards and clinical supervision of patients. Conclusion Such a smart, economical, non-invasive, rapid and accurate system can be introduced as a useful diagnostic system for comprehensive treatment of breast cancer. Another advantage of this method is the possibility of diagnosing breast abnormalities. If done by experts, FNA can be a reliable replacement for open biopsy in palpable breast masses. Evaluation of FNA samples during aspiration can decrease insufficient samples. PMID:25352966

Models of performance of evolutionary program induction algorithms based on indicators of problem difficulty.

PubMed

Graff, Mario; Poli, Riccardo; Flores, Juan J

2013-01-01

Modeling the behavior of algorithms is the realm of evolutionary algorithm theory. From a practitioner's point of view, theory must provide some guidelines regarding which algorithm/parameters to use in order to solve a particular problem. Unfortunately, most theoretical models of evolutionary algorithms are difficult to apply to realistic situations. However, in recent work (Graff and Poli, 2008, 2010), where we developed a method to practically estimate the performance of evolutionary program-induction algorithms (EPAs), we started addressing this issue. The method was quite general; however, it suffered from some limitations: it required the identification of a set of reference problems, it required hand picking a distance measure in each particular domain, and the resulting models were opaque, typically being linear combinations of 100 features or more. In this paper, we propose a significant improvement of this technique that overcomes the three limitations of our previous method. We achieve this through the use of a novel set of features for assessing problem difficulty for EPAs which are very general, essentially based on the notion of finite difference. To show the capabilities or our technique and to compare it with our previous performance models, we create models for the same two important classes of problems-symbolic regression on rational functions and Boolean function induction-used in our previous work. We model a variety of EPAs. The comparison showed that for the majority of the algorithms and problem classes, the new method produced much simpler and more accurate models than before. To further illustrate the practicality of the technique and its generality (beyond EPAs), we have also used it to predict the performance of both autoregressive models and EPAs on the problem of wind speed forecasting, obtaining simpler and more accurate models that outperform in all cases our previous performance models.

The Electrolyte Genome project: A big data approach in battery materials discovery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qu, Xiaohui; Jain, Anubhav; Rajput, Nav Nidhi

2015-06-01

We present a high-throughput infrastructure for the automated calculation of molecular properties with a focus on battery electrolytes. The infrastructure is largely open-source and handles both practical aspects (input file generation, output file parsing, and information management) as well as more complex problems (structure matching, salt complex generation, and failure recovery). Using this infrastructure, we have computed the ionization potential (IP) and electron affinities (EA) of 4830 molecules relevant to battery electrolytes (encompassing almost 55,000 quantum mechanics calculations) at the B3LYP/6-31+G(*) level. We describe automated workflows for computing redox potential, dissociation constant, and salt-molecule binding complex structure generation. We presentmore » routines for automatic recovery from calculation errors, which brings the failure rate from 9.2% to 0.8% for the QChem DFT code. Automated algorithms to check duplication between two arbitrary molecules and structures are described. We present benchmark data on basis sets and functionals on the G2-97 test set; one finding is that a IP/EA calculation method that combines PBE geometry optimization and B3LYP energy evaluation requires less computational cost and yields nearly identical results as compared to a full B3LYP calculation, and could be suitable for the calculation of large molecules. Our data indicates that among the 8 functionals tested, XYGJ-OS and B3LYP are the two best functionals to predict IP/EA with an RMSE of 0.12 and 0.27 eV, respectively. Application of our automated workflow to a large set of quinoxaline derivative molecules shows that functional group effect and substitution position effect can be separated for IP/EA of quinoxaline derivatives, and the most sensitive position is different for IP and EA. Published by Elsevier B.V« less

Prevalence of E/A wave fusion and A wave truncation in DDD pacemaker patients with complete AV block under nominal AV intervals.

PubMed

Poller, Wolfram C; Dreger, Henryk; Schwerg, Marius; Melzer, Christoph

2015-01-01

Optimization of the AV-interval (AVI) in DDD pacemakers improves cardiac hemodynamics and reduces pacemaker syndromes. Manual optimization is typically not performed in clinical routine. In the present study we analyze the prevalence of E/A wave fusion and A wave truncation under resting conditions in 160 patients with complete AV block (AVB) under the pre-programmed AVI. We manually optimized sub-optimal AVI. We analyzed 160 pacemaker patients with complete AVB, both in sinus rhythm (AV-sense; n = 129) and under atrial pacing (AV-pace; n = 31). Using Doppler analyses of the transmitral inflow we classified the nominal AVI as: a) normal, b) too long (E/A wave fusion) or c) too short (A wave truncation). In patients with a sub-optimal AVI, we performed manual optimization according to the recommendations of the American Society of Echocardiography. All AVB patients with atrial pacing exhibited a normal transmitral inflow under the nominal AV-pace intervals (100%). In contrast, 25 AVB patients in sinus rhythm showed E/A wave fusion under the pre-programmed AV-sense intervals (19.4%; 95% confidence interval (CI): 12.6-26.2%). A wave truncations were not observed in any patient. All patients with a complete E/A wave fusion achieved a normal transmitral inflow after AV-sense interval reduction (mean optimized AVI: 79.4 ± 13.6 ms). Given the rate of 19.4% (CI 12.6-26.2%) of patients with a too long nominal AV-sense interval, automatic algorithms may prove useful in improving cardiac hemodynamics, especially in the subgroup of atrially triggered pacemaker patients with AV node diseases.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm

NASA Astrophysics Data System (ADS)

Craig, Sam; While, Lyndon; Barone, Luigi

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

NASA Astrophysics Data System (ADS)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Evolutionary Optimization of a Quadrifilar Helical Antenna

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Clancy, Daniel (Technical Monitor)

2002-01-01

Automated antenna synthesis via evolutionary design has recently garnered much attention in the research literature. Evolutionary algorithms show promise because, among search algorithms, they are able to effectively search large, unknown design spaces. NASA's Mars Odyssey spacecraft is due to reach final Martian orbit insertion in January, 2002. Onboard the spacecraft is a quadrifilar helical antenna that provides telecommunications in the UHF band with landed assets, such as robotic rovers. Each helix is driven by the same signal which is phase-delayed in 90 deg increments. A small ground plane is provided at the base. It is designed to operate in the frequency band of 400-438 MHz. Based on encouraging previous results in automated antenna design using evolutionary search, we wanted to see whether such techniques could improve upon Mars Odyssey antenna design. Specifically, a co-evolutionary genetic algorithm is applied to optimize the gain and size of the quadrifilar helical antenna. The optimization was performed in-situ in the presence of a neighboring spacecraft structure. On the spacecraft, a large aluminum fuel tank is adjacent to the antenna. Since this fuel tank can dramatically affect the antenna's performance, we leave it to the evolutionary process to see if it can exploit the fuel tank's properties advantageously. Optimizing in the presence of surrounding structures would be quite difficult for human antenna designers, and thus the actual antenna was designed for free space (with a small ground plane). In fact, when flying on the spacecraft, surrounding structures that are moveable (e.g., solar panels) may be moved during the mission in order to improve the antenna's performance.

Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

PubMed

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

Decomposition Products of Phosphine Under Pressure: PH 2 Stable and Superconducting?

DOE PAGES

Shamp, Andrew; Terpstra, Tyson; Bi, Tiange; ...

2016-02-17

Evolutionary algorithms (EA) coupled with Density Functional Theory (DFT) calculations have been used to predict the most stable hydrides of phosphorous (PH n, n = 1 - 6) at 100, 150 and 200 GPa. At these pressures phosphine is unstable with respect to decomposition into the elemental phases, as well as PH 2 and H 2. Three metallic PH 2 phases were found to be dynamically stable and superconducting between 100-200 GPa. One of these contains five formula units in the primitive cell and has C2=m symmetry (5FU-C2=m). It is comprised of 1D periodic PH 3-PH-PH 2-PH-PH 3 oligomers. Twomore » structurally related phases consisting of phosphorous atoms that are octahedrally coordinated by four phosphorous atoms in the equatorial positions and two hydrogen atoms in the axial positions (I4=mmm and 2FU-C 2=m) were the most stable phases between 160-200 GPa. Their superconducting critical temperatures (Tc) were computed as being 70 and 76 K, respectively, via the Allen-Dynes modified McMillan formula and using a value of 0.1 for the Coulomb pseudopotential, . Our results suggest that the superconductivity recently observed by Drozdov, Eremets and Troyan when phosphine was subject to pressures of 207 GPa in a diamond anvil cell may result from these, and other, decomposition products of phosphine.« less

Decomposition Products of Phosphine Under Pressure: PH 2 Stable and Superconducting?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shamp, Andrew; Terpstra, Tyson; Bi, Tiange

Evolutionary algorithms (EA) coupled with Density Functional Theory (DFT) calculations have been used to predict the most stable hydrides of phosphorous (PH n, n = 1 - 6) at 100, 150 and 200 GPa. At these pressures phosphine is unstable with respect to decomposition into the elemental phases, as well as PH 2 and H 2. Three metallic PH 2 phases were found to be dynamically stable and superconducting between 100-200 GPa. One of these contains five formula units in the primitive cell and has C2=m symmetry (5FU-C2=m). It is comprised of 1D periodic PH 3-PH-PH 2-PH-PH 3 oligomers. Twomore » structurally related phases consisting of phosphorous atoms that are octahedrally coordinated by four phosphorous atoms in the equatorial positions and two hydrogen atoms in the axial positions (I4=mmm and 2FU-C 2=m) were the most stable phases between 160-200 GPa. Their superconducting critical temperatures (Tc) were computed as being 70 and 76 K, respectively, via the Allen-Dynes modified McMillan formula and using a value of 0.1 for the Coulomb pseudopotential, . Our results suggest that the superconductivity recently observed by Drozdov, Eremets and Troyan when phosphine was subject to pressures of 207 GPa in a diamond anvil cell may result from these, and other, decomposition products of phosphine.« less

Evolutionary Fuzzy Block-Matching-Based Camera Raw Image Denoising.

PubMed

Yang, Chin-Chang; Guo, Shu-Mei; Tsai, Jason Sheng-Hong

2017-09-01

An evolutionary fuzzy block-matching-based image denoising algorithm is proposed to remove noise from a camera raw image. Recently, a variance stabilization transform is widely used to stabilize the noise variance, so that a Gaussian denoising algorithm can be used to remove the signal-dependent noise in camera sensors. However, in the stabilized domain, the existed denoising algorithm may blur too much detail. To provide a better estimate of the noise-free signal, a new block-matching approach is proposed to find similar blocks by the use of a type-2 fuzzy logic system (FLS). Then, these similar blocks are averaged with the weightings which are determined by the FLS. Finally, an efficient differential evolution is used to further improve the performance of the proposed denoising algorithm. The experimental results show that the proposed denoising algorithm effectively improves the performance of image denoising. Furthermore, the average performance of the proposed method is better than those of two state-of-the-art image denoising algorithms in subjective and objective measures.

Automated discovery of local search heuristics for satisfiability testing.

PubMed

Fukunaga, Alex S

2008-01-01

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

PubMed

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms

NASA Astrophysics Data System (ADS)

Wang, Rui; Mansor, Maszatul M.; Purshouse, Robin C.; Fleming, Peter J.

2015-10-01

Many-objective optimisation problems remain challenging for many state-of-the-art multi-objective evolutionary algorithms. Preference-inspired co-evolutionary algorithms (PICEAs) which co-evolve the usual population of candidate solutions with a family of decision-maker preferences during the search have been demonstrated to be effective on such problems. However, it is unknown whether PICEAs are robust with respect to the parameter settings. This study aims to address this question. First, a global sensitivity analysis method - the Sobol' variance decomposition method - is employed to determine the relative importance of the parameters controlling the performance of PICEAs. Experimental results show that the performance of PICEAs is controlled for the most part by the number of function evaluations. Next, we investigate the effect of key parameters identified from the Sobol' test and the genetic operators employed in PICEAs. Experimental results show improved performance of the PICEAs as more preferences are co-evolved. Additionally, some suggestions for genetic operator settings are provided for non-expert users.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Application of evolutionary computation in ECAD problems

NASA Astrophysics Data System (ADS)

Lee, Dae-Hyun; Hwang, Seung H.

1998-10-01

Design of modern electronic system is a complicated task which demands the use of computer- aided design (CAD) tools. Since a lot of problems in ECAD are combinatorial optimization problems, evolutionary computations such as genetic algorithms and evolutionary programming have been widely employed to solve those problems. We have applied evolutionary computation techniques to solve ECAD problems such as technology mapping, microcode-bit optimization, data path ordering and peak power estimation, where their benefits are well observed. This paper presents experiences and discusses issues in those applications.

Computer-Automated Evolution of Spacecraft X-Band Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Homby, Gregory S.; Linden, Derek S.

2010-01-01

A document discusses the use of computer- aided evolution in arriving at a design for X-band communication antennas for NASA s three Space Technology 5 (ST5) satellites, which were launched on March 22, 2006. Two evolutionary algorithms, incorporating different representations of the antenna design and different fitness functions, were used to automatically design and optimize an X-band antenna design. A set of antenna designs satisfying initial ST5 mission requirements was evolved by use these algorithms. The two best antennas - one from each evolutionary algorithm - were built. During flight-qualification testing of these antennas, the mission requirements were changed. After minimal changes in the evolutionary algorithms - mostly in the fitness functions - new antenna designs satisfying the changed mission requirements were evolved and within one month of this change, two new antennas were designed and prototypes of the antennas were built and tested. One of these newly evolved antennas was approved for deployment on the ST5 mission, and flight-qualified versions of this design were built and installed on the spacecraft. At the time of writing the document, these antennas were the first computer-evolved hardware in outer space.

Artificial evolution by viability rather than competition.

PubMed

Maesani, Andrea; Fernando, Pradeep Ruben; Floreano, Dario

2014-01-01

Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.

Fuzzy multi objective transportation problem – evolutionary algorithm approach

NASA Astrophysics Data System (ADS)

Karthy, T.; Ganesan, K.

2018-04-01

This paper deals with fuzzy multi objective transportation problem. An fuzzy optimal compromise solution is obtained by using Fuzzy Genetic Algorithm. A numerical example is provided to illustrate the methodology.

An efficient non-dominated sorting method for evolutionary algorithms.

PubMed

Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F

2008-01-01

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.

PubMed

Nallaperuma, Samadhi; Neumann, Frank; Sudholt, Dirk

2017-01-01

Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to our theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed-time budget. We follow this approach and present a fixed-budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson Problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed-time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1+1) EA and (1+[Formula: see text]) EA algorithms for the TSP in a smoothed complexity setting, and derive the lower bounds of the expected fitness gain for a specified number of generations.

Comparative Analysis of Models of the Earth's Gravity: 3. Accuracy of Predicting EAS Motion

NASA Astrophysics Data System (ADS)

Kuznetsov, E. D.; Berland, V. E.; Wiebe, Yu. S.; Glamazda, D. V.; Kajzer, G. T.; Kolesnikov, V. I.; Khremli, G. P.

2002-05-01

This paper continues a comparative analysis of modern satellite models of the Earth's gravity which we started in [6, 7]. In the cited works, the uniform norms of spherical functions were compared with their gradients for individual harmonics of the geopotential expansion [6] and the potential differences were compared with the gravitational accelerations obtained in various models of the Earth's gravity [7]. In practice, it is important to know how consistently the EAS motion is represented by various geopotential models. Unless otherwise stated, a model version in which the equations of motion are written using the classical Encke scheme and integrated together with the variation equations by the implicit one-step Everhart's algorithm [1] was used. When calculating coordinates and velocities on the integration step (at given instants of time), the approximate Everhart formula was employed.

Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

NASA Astrophysics Data System (ADS)

Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

2018-03-01

This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

Enhancement of DNaseI Salt Tolerance by Mimicking the Domain Structure of DNase from an Extremely Halotolerant Bacterium Thioalkalivibrio sp. K90mix

PubMed Central

Alzbutas, Gediminas; Kaniusaite, Milda; Lagunavicius, Arunas

2016-01-01

In our previous work we showed that DNaseI-like protein from an extremely halotolerant bacterium Thioalkalivibrio sp. K90mix retained its activity at salt concentrations as high as 4 M NaCl and the key factor allowing this was the C-terminal DNA-binding domain, which comprised two HhH (helix-hairpin-helix) motifs. The further investigations revealed that this domain originated from proteins related to bacterial competence ComEA/ComE proteins. It is likely that in the course of evolution the DNA-binding domain from these proteins was fused to a metallo-β-lactamase superfamily domain. Very likely such domain organization having proteins subsequently “donated” the DNA-binding domain to bacterial DNases. In this study we have mimicked this evolutionary step by fusing bovine DNaseI and DNA-binding domains. We have created two fusions: one harboring the DNA-binding domain of DNaseI-like protein from Thioalkalivibrio sp. K90mix and the second one harboring the DNA-binding domain of bacterial competence protein ComEA from Bacillus subtilis. Both domains enhanced salt tolerance of DNaseI, albeit to different extent. Molecular modeling revealed the essential differences between their interaction with DNA shedding some light on the differences in salt tolerance. In this study we have enhanced salt tolerance of bovine DNaseI; thus, we successfully mimicked the Nature’s evolutionary engineering that created the extremely halotolerant bacterial DNase. We have demonstrated that the newly engineered DNaseI variants can be successfully used in applications where activity of the wild type bovine DNaseI is impeded by buffers used. PMID:26939122

Image-Guided Rendering with an Evolutionary Algorithm Based on Cloud Model

PubMed Central

2018-01-01

The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford's law, fractal dimension, global contrast factor, and Shannon's entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art. PMID:29805440

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

DOEpatents

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

Coevolving memetic algorithms: a review and progress report.

PubMed

Smith, Jim E

2007-02-01

Coevolving memetic algorithms are a family of metaheuristic search algorithms in which a rule-based representation of local search (LS) is coadapted alongside candidate solutions within a hybrid evolutionary system. Simple versions of these systems have been shown to outperform other nonadaptive memetic and evolutionary algorithms on a range of problems. This paper presents a rationale for such systems and places them in the context of other recent work on adaptive memetic algorithms. It then proposes a general structure within which a population of LS algorithms can be evolved in tandem with the solutions to which they are applied. Previous research started with a simple self-adaptive system before moving on to more complex models. Results showed that the algorithm was able to discover and exploit certain forms of structure and regularities within the problems. This "metalearning" of problem features provided a means of creating highly scalable algorithms. This work is briefly reviewed to highlight some of the important findings and behaviors exhibited. Based on this analysis, new results are then presented from systems with more flexible representations, which, again, show significant improvements. Finally, the current state of, and future directions for, research in this area is discussed.

A new evolutionary system for evolving artificial neural networks.

PubMed

Yao, X; Liu, Y

1997-01-01

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

The ConSurf-DB: pre-calculated evolutionary conservation profiles of protein structures.

PubMed

Goldenberg, Ofir; Erez, Elana; Nimrod, Guy; Ben-Tal, Nir

2009-01-01

ConSurf-DB is a repository for evolutionary conservation analysis of the proteins of known structures in the Protein Data Bank (PDB). Sequence homologues of each of the PDB entries were collected and aligned using standard methods. The evolutionary conservation of each amino acid position in the alignment was calculated using the Rate4Site algorithm, implemented in the ConSurf web server. The algorithm takes into account the phylogenetic relations between the aligned proteins and the stochastic nature of the evolutionary process explicitly. Rate4Site assigns a conservation level for each position in the multiple sequence alignment using an empirical Bayesian inference. Visual inspection of the conservation patterns on the 3D structure often enables the identification of key residues that comprise the functionally important regions of the protein. The repository is updated with the latest PDB entries on a monthly basis and will be rebuilt annually. ConSurf-DB is available online at http://consurfdb.tau.ac.il/

The ConSurf-DB: pre-calculated evolutionary conservation profiles of protein structures

PubMed Central

Goldenberg, Ofir; Erez, Elana; Nimrod, Guy; Ben-Tal, Nir

2009-01-01

ConSurf-DB is a repository for evolutionary conservation analysis of the proteins of known structures in the Protein Data Bank (PDB). Sequence homologues of each of the PDB entries were collected and aligned using standard methods. The evolutionary conservation of each amino acid position in the alignment was calculated using the Rate4Site algorithm, implemented in the ConSurf web server. The algorithm takes into account the phylogenetic relations between the aligned proteins and the stochastic nature of the evolutionary process explicitly. Rate4Site assigns a conservation level for each position in the multiple sequence alignment using an empirical Bayesian inference. Visual inspection of the conservation patterns on the 3D structure often enables the identification of key residues that comprise the functionally important regions of the protein. The repository is updated with the latest PDB entries on a monthly basis and will be rebuilt annually. ConSurf-DB is available online at http://consurfdb.tau.ac.il/ PMID:18971256

Enhancing the performance of MOEAs: an experimental presentation of a new fitness guided mutation operator

NASA Astrophysics Data System (ADS)

Liagkouras, K.; Metaxiotis, K.

2017-01-01

Multi-objective evolutionary algorithms (MOEAs) are currently a dynamic field of research that has attracted considerable attention. Mutation operators have been utilized by MOEAs as variation mechanisms. In particular, polynomial mutation (PLM) is one of the most popular variation mechanisms and has been utilized by many well-known MOEAs. In this paper, we revisit the PLM operator and we propose a fitness-guided version of the PLM. Experimental results obtained by non-dominated sorting genetic algorithm II and strength Pareto evolutionary algorithm 2 show that the proposed fitness-guided mutation operator outperforms the classical PLM operator, based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it.

Comparison of multiobjective evolutionary algorithms for operations scheduling under machine availability constraints.

PubMed

Frutos, M; Méndez, M; Tohmé, F; Broz, D

2013-01-01

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

NASA Astrophysics Data System (ADS)

Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

2016-01-01

A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems

NASA Astrophysics Data System (ADS)

Guo, Weian; Li, Wuzhao; Zhang, Qun; Wang, Lei; Wu, Qidi; Ren, Hongliang

2014-11-01

In evolutionary algorithms, elites are crucial to maintain good features in solutions. However, too many elites can make the evolutionary process stagnate and cannot enhance the performance. This article employs particle swarm optimization (PSO) and biogeography-based optimization (BBO) to propose a hybrid algorithm termed biogeography-based particle swarm optimization (BPSO) which could make a large number of elites effective in searching optima. In this algorithm, the whole population is split into several subgroups; BBO is employed to search within each subgroup and PSO for the global search. Since not all the population is used in PSO, this structure overcomes the premature convergence in the original PSO. Time complexity analysis shows that the novel algorithm does not increase the time consumption. Fourteen numerical benchmarks and four engineering problems with constraints are used to test the BPSO. To better deal with constraints, a fuzzy strategy for the number of elites is investigated. The simulation results validate the feasibility and effectiveness of the proposed algorithm.

Evaluation of an Agricultural Meteorological Disaster Based on Multiple Criterion Decision Making and Evolutionary Algorithm

PubMed Central

Yu, Xiaobing; Yu, Xianrui; Lu, Yiqun

2018-01-01

The evaluation of a meteorological disaster can be regarded as a multiple-criteria decision making problem because it involves many indexes. Firstly, a comprehensive indexing system for an agricultural meteorological disaster is proposed, which includes the disaster rate, the inundated rate, and the complete loss rate. Following this, the relative weights of the three criteria are acquired using a novel proposed evolutionary algorithm. The proposed algorithm consists of a differential evolution algorithm and an evolution strategy. Finally, a novel evaluation model, based on the proposed algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), is presented to estimate the agricultural meteorological disaster of 2008 in China. The geographic information system (GIS) technique is employed to depict the disaster. The experimental results demonstrated that the agricultural meteorological disaster of 2008 was very serious, especially in Hunan and Hubei provinces. Some useful suggestions are provided to relieve agriculture meteorological disasters. PMID:29597243

Mathematical model and metaheuristics for simultaneous balancing and sequencing of a robotic mixed-model assembly line

NASA Astrophysics Data System (ADS)

Li, Zixiang; Janardhanan, Mukund Nilakantan; Tang, Qiuhua; Nielsen, Peter

2018-05-01

This article presents the first method to simultaneously balance and sequence robotic mixed-model assembly lines (RMALB/S), which involves three sub-problems: task assignment, model sequencing and robot allocation. A new mixed-integer programming model is developed to minimize makespan and, using CPLEX solver, small-size problems are solved for optimality. Two metaheuristics, the restarted simulated annealing algorithm and co-evolutionary algorithm, are developed and improved to address this NP-hard problem. The restarted simulated annealing method replaces the current temperature with a new temperature to restart the search process. The co-evolutionary method uses a restart mechanism to generate a new population by modifying several vectors simultaneously. The proposed algorithms are tested on a set of benchmark problems and compared with five other high-performing metaheuristics. The proposed algorithms outperform their original editions and the benchmarked methods. The proposed algorithms are able to solve the balancing and sequencing problem of a robotic mixed-model assembly line effectively and efficiently.

Multi-objective optimisation and decision-making of space station logistics strategies

NASA Astrophysics Data System (ADS)

Zhu, Yue-he; Luo, Ya-zhong

2016-10-01

Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers' preferences.

Resource-constrained scheduling with hard due windows and rejection penalties

NASA Astrophysics Data System (ADS)

Garcia, Christopher

2016-09-01

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.

Co-evolution for Problem Simplification

NASA Technical Reports Server (NTRS)

Haith, Gary L.; Lohn, Jason D.; Cplombano, Silvano P.; Stassinopoulos, Dimitris

1999-01-01

This paper explores a co-evolutionary approach applicable to difficult problems with limited failure/success performance feedback. Like familiar "predator-prey" frameworks this algorithm evolves two populations of individuals - the solutions (predators) and the problems (prey). The approach extends previous work by rewarding only the problems that match their difficulty to the level of solut,ion competence. In complex problem domains with limited feedback, this "tractability constraint" helps provide an adaptive fitness gradient that, effectively differentiates the candidate solutions. The algorithm generates selective pressure toward the evolution of increasingly competent solutions by rewarding solution generality and uniqueness and problem tractability and difficulty. Relative (inverse-fitness) and absolute (static objective function) approaches to evaluating problem difficulty are explored and discussed. On a simple control task, this co-evolutionary algorithm was found to have significant advantages over a genetic algorithm with either a static fitness function or a fitness function that changes on a hand-tuned schedule.

Design and Optimization of Low-thrust Orbit Transfers Using Q-law and Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Lee, Seungwon; vonAllmen, Paul; Fink, Wolfgang; Petropoulos, Anastassios; Terrile, Richard

2005-01-01

Future space missions will depend more on low-thrust propulsion (such as ion engines) thanks to its high specific impulse. Yet, the design of low-thrust trajectories is complex and challenging. Third-body perturbations often dominate the thrust, and a significant change to the orbit requires a long duration of thrust. In order to guide the early design phases, we have developed an efficient and efficacious method to obtain approximate propellant and flight-time requirements (i.e., the Pareto front) for orbit transfers. A search for the Pareto-optimal trajectories is done in two levels: optimal thrust angles and locations are determined by Q-law, while the Q-law is optimized with two evolutionary algorithms: a genetic algorithm and a simulated-annealing-related algorithm. The examples considered are several types of orbit transfers around the Earth and the asteroid Vesta.

Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

PubMed Central

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

Evolving a Behavioral Repertoire for a Walking Robot.

PubMed

Cully, A; Mouret, J-B

2016-01-01

Numerous algorithms have been proposed to allow legged robots to learn to walk. However, most of these algorithms are devised to learn walking in a straight line, which is not sufficient to accomplish any real-world mission. Here we introduce the Transferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), a novel evolutionary algorithm that simultaneously discovers several hundreds of simple walking controllers, one for each possible direction. By taking advantage of solutions that are usually discarded by evolutionary processes, TBR-Evolution is substantially faster than independently evolving each controller. Our technique relies on two methods: (1) novelty search with local competition, which searches for both high-performing and diverse solutions, and (2) the transferability approach, which combines simulations and real tests to evolve controllers for a physical robot. We evaluate this new technique on a hexapod robot. Results show that with only a few dozen short experiments performed on the robot, the algorithm learns a repertoire of controllers that allows the robot to reach every point in its reachable space. Overall, TBR-Evolution introduced a new kind of learning algorithm that simultaneously optimizes all the achievable behaviors of a robot.

Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

PubMed

Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

Enhancement of A5/1 encryption algorithm

NASA Astrophysics Data System (ADS)

Thomas, Ria Elin; Chandhiny, G.; Sharma, Katyayani; Santhi, H.; Gayathri, P.

2017-11-01

Mobiles have become an integral part of today’s world. Various standards have been proposed for the mobile communication, one of them being GSM. With the rising increase of mobile-based crimes, it is necessary to improve the security of the information passed in the form of voice or data. GSM uses A5/1 for its encryption. It is known that various attacks have been implemented, exploiting the vulnerabilities present within the A5/1 algorithm. Thus, in this paper, we proceed to look at what these vulnerabilities are, and propose the enhanced A5/1 (E-A5/1) where, we try to improve the security provided by the A5/1 algorithm by XORing the key stream generated with a pseudo random number, without increasing the time complexity. We need to study what the vulnerabilities of the base algorithm (A5/1) is, and try to improve upon its security. This will help in the future releases of the A5 family of algorithms.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

NASA Astrophysics Data System (ADS)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

Reverse engineering a gene network using an asynchronous parallel evolution strategy

PubMed Central

2010-01-01

Background The use of reverse engineering methods to infer gene regulatory networks by fitting mathematical models to gene expression data is becoming increasingly popular and successful. However, increasing model complexity means that more powerful global optimisation techniques are required for model fitting. The parallel Lam Simulated Annealing (pLSA) algorithm has been used in such approaches, but recent research has shown that island Evolutionary Strategies can produce faster, more reliable results. However, no parallel island Evolutionary Strategy (piES) has yet been demonstrated to be effective for this task. Results Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested. Conclusions Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures. PMID:20196855

Genesis and genetic constellations of swine influenza viruses in Thailand.

PubMed

Poonsuk, Sukontip; Sangthong, Pradit; Petcharat, Nantawan; Lekcharoensuk, Porntippa

2013-12-27

Swine influenza virus (SIV) is one of the most important zoonotic agents and the origin of the most recent pandemic virus. Asia is considered to be the epicenter for genetic exchanging of influenza A viruses and Southeast Asia including Thailand serves as a reservoir to maintain the persistence of the viruses for seeding other regions. Therefore, searching for new reassortants in this area has been routinely required. Although SIVs in Thailand have been characterized, collective information regarding their genetic evolution and gene constellations is limited. In this study, whole genomes of 30 SIVs isolated during clinical target surveillance plus all available sequences of past and currently circulating Thai SIVs were genetically characterized based on their evolutionary relationships. All genetic pools of Thai SIVs are comprised of four lineages including classical swine (CS), Eurasian swine (EAs), Triple reassortants (TRIG) and Seasonal human (Shs). Out of 84 isolates, nine H1N1, six H3N2 and one H1N2 strains were identified. Gene constellations of SIVs in Thailand are highly complex resulting from multiple reassortments among concurrently circulating SIVs and temporally introduced foreign genes. Most strains contain gene segments from both EAs and CS lineages and appeared transiently. TRIG lineage has been recently introduced into Thai SIV gene pools. The existence of EAs and TRIG lineages in this region may increase rates of genetic exchange and diversity while Southeast Asia is a persistent reservoir for influenza A viruses. Continual monitoring of SIV evolution in this region is crucial in searching for the next potential pandemic viruses. Copyright © 2013 Elsevier B.V. All rights reserved.

A New Automated Design Method Based on Machine Learning for CMOS Analog Circuits

NASA Astrophysics Data System (ADS)

Moradi, Behzad; Mirzaei, Abdolreza

2016-11-01

A new simulation based automated CMOS analog circuit design method which applies a multi-objective non-Darwinian-type evolutionary algorithm based on Learnable Evolution Model (LEM) is proposed in this article. The multi-objective property of this automated design of CMOS analog circuits is governed by a modified Strength Pareto Evolutionary Algorithm (SPEA) incorporated in the LEM algorithm presented here. LEM includes a machine learning method such as the decision trees that makes a distinction between high- and low-fitness areas in the design space. The learning process can detect the right directions of the evolution and lead to high steps in the evolution of the individuals. The learning phase shortens the evolution process and makes remarkable reduction in the number of individual evaluations. The expert designer's knowledge on circuit is applied in the design process in order to reduce the design space as well as the design time. The circuit evaluation is made by HSPICE simulator. In order to improve the design accuracy, bsim3v3 CMOS transistor model is adopted in this proposed design method. This proposed design method is tested on three different operational amplifier circuits. The performance of this proposed design method is verified by comparing it with the evolutionary strategy algorithm and other similar methods.

A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry

PubMed Central

Bertamini, Marco; Jones, Andrew; Holmes, Tim; Zanker, Johannes M.

2016-01-01

Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation–symmetry, DS gene) and orientation (0° to 90°, orientation, ORI gene). An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference. PMID:27433324

A standard deviation selection in evolutionary algorithm for grouper fish feed formulation

NASA Astrophysics Data System (ADS)

Cai-Juan, Soong; Ramli, Razamin; Rahman, Rosshairy Abdul

2016-10-01

Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment. Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price. However, the demand of grouper fish is still insufficient from the wild catch. Therefore, there is a need to farm grouper fish to cater to the market demand. In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available. Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm. Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation. Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. The feasible and low fitness, quick solution can be obtained. These fitness can be further predicted to minimize cost in farming grouper fish.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

PubMed

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

PubMed Central

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N. V.

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test. PMID:23469172

Evolutionary Optimization of Quadrifilar Helical and Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Stoica, Adrian; Clancy, Daniel (Technical Monitor)

2002-01-01

We present optimization results obtained for two type of antennas using evolutionary algorithms. A quadrifilar helical UHF antenna is currently flying aboard NASA's Mars Odyssey spacecraft and is due to reach final Martian orbit insertion in January, 2002. Using this antenna as a benchmark, we ran experiments employing a coevolutionary genetic algorithm to evolve a quadrifilar helical design in-situ - i.e., in the presence of a surrounding structure. Results show a 93% improvement at 400 MHz and a 48% improvement at 438 MHz in the average gain. The evolved antenna is also one-fourth the size. Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain and the inclusion of numerous parasitic elements. Our fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem.

PubMed

Zamdborg, Leonid; Holloway, David M; Merelo, Juan J; Levchenko, Vladimir F; Spirov, Alexander V

2015-06-10

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of "genomic parasites", such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.

Forced evolution in silico by artificial transposons and their genetic operators: The ant navigation problem

PubMed Central

Zamdborg, Leonid; Holloway, David M.; Merelo, Juan J.; Levchenko, Vladimir F.; Spirov, Alexander V.

2015-01-01

Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of “genomic parasites”, such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts. PMID:25767296

PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

NASA Astrophysics Data System (ADS)

Luo, Bin; Lin, Lin; Zhong, ShiSheng

2018-02-01

In this research, we propose a preference-guided optimisation algorithm for multi-criteria decision-making (MCDM) problems with interval-valued fuzzy preferences. The interval-valued fuzzy preferences are decomposed into a series of precise and evenly distributed preference-vectors (reference directions) regarding the objectives to be optimised on the basis of uniform design strategy firstly. Then the preference information is further incorporated into the preference-vectors based on the boundary intersection approach, meanwhile, the MCDM problem with interval-valued fuzzy preferences is reformulated into a series of single-objective optimisation sub-problems (each sub-problem corresponds to a decomposed preference-vector). Finally, a preference-guided optimisation algorithm based on MOEA/D (multi-objective evolutionary algorithm based on decomposition) is proposed to solve the sub-problems in a single run. The proposed algorithm incorporates the preference-vectors within the optimisation process for guiding the search procedure towards a more promising subset of the efficient solutions matching the interval-valued fuzzy preferences. In particular, lots of test instances and an engineering application are employed to validate the performance of the proposed algorithm, and the results demonstrate the effectiveness and feasibility of the algorithm.

The Proposal of a Evolutionary Strategy Generating the Data Structures Based on a Horizontal Tree for the Tests

NASA Astrophysics Data System (ADS)

Żukowicz, Marek; Markiewicz, Michał

2016-09-01

The aim of the article is to present a mathematical definition of the object model, that is known in computer science as TreeList and to show application of this model for design evolutionary algorithm, that purpose is to generate structures based on this object. The first chapter introduces the reader to the problem of presenting data using the TreeList object. The second chapter describes the problem of testing data structures based on TreeList. The third one shows a mathematical model of the object TreeList and the parameters, used in determining the utility of structures created through this model and in evolutionary strategy, that generates these structures for testing purposes. The last chapter provides a brief summary and plans for future research related to the algorithm presented in the article.

The wind power prediction research based on mind evolutionary algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Restart Operator Meta-heuristics for a Problem-Oriented Evolutionary Strategies Algorithm in Inverse Mathematical MISO Modelling Problem Solving

NASA Astrophysics Data System (ADS)

Ryzhikov, I. S.; Semenkin, E. S.

2017-02-01

This study is focused on solving an inverse mathematical modelling problem for dynamical systems based on observation data and control inputs. The mathematical model is being searched in the form of a linear differential equation, which determines the system with multiple inputs and a single output, and a vector of the initial point coordinates. The described problem is complex and multimodal and for this reason the proposed evolutionary-based optimization technique, which is oriented on a dynamical system identification problem, was applied. To improve its performance an algorithm restart operator was implemented.

Configurable pattern-based evolutionary biclustering of gene expression data

PubMed Central

2013-01-01

Background Biclustering algorithms for microarray data aim at discovering functionally related gene sets under different subsets of experimental conditions. Due to the problem complexity and the characteristics of microarray datasets, heuristic searches are usually used instead of exhaustive algorithms. Also, the comparison among different techniques is still a challenge. The obtained results vary in relevant features such as the number of genes or conditions, which makes it difficult to carry out a fair comparison. Moreover, existing approaches do not allow the user to specify any preferences on these properties. Results Here, we present the first biclustering algorithm in which it is possible to particularize several biclusters features in terms of different objectives. This can be done by tuning the specified features in the algorithm or also by incorporating new objectives into the search. Furthermore, our approach bases the bicluster evaluation in the use of expression patterns, being able to recognize both shifting and scaling patterns either simultaneously or not. Evolutionary computation has been chosen as the search strategy, naming thus our proposal Evo-Bexpa (Evolutionary Biclustering based in Expression Patterns). Conclusions We have conducted experiments on both synthetic and real datasets demonstrating Evo-Bexpa abilities to obtain meaningful biclusters. Synthetic experiments have been designed in order to compare Evo-Bexpa performance with other approaches when looking for perfect patterns. Experiments with four different real datasets also confirm the proper performing of our algorithm, whose results have been biologically validated through Gene Ontology. PMID:23433178

Algorithm to find distant repeats in a single protein sequence

PubMed Central

Banerjee, Nirjhar; Sarani, Rangarajan; Ranjani, Chellamuthu Vasuki; Sowmiya, Govindaraj; Michael, Daliah; Balakrishnan, Narayanasamy; Sekar, Kanagaraj

2008-01-01

Distant repeats in protein sequence play an important role in various aspects of protein analysis. A keen analysis of the distant repeats would enable to establish a firm relation of the repeats with respect to their function and three-dimensional structure during the evolutionary process. Further, it enlightens the diversity of duplication during the evolution. To this end, an algorithm has been developed to find all distant repeats in a protein sequence. The scores from Point Accepted Mutation (PAM) matrix has been deployed for the identification of amino acid substitutions while detecting the distant repeats. Due to the biological importance of distant repeats, the proposed algorithm will be of importance to structural biologists, molecular biologists, biochemists and researchers involved in phylogenetic and evolutionary studies. PMID:19052663

Evolutionary Optimization of Yagi-Uda Antennas

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Kraus, William F.; Linden, Derek S.; Colombano, Silvano P.

2001-01-01

Yagi-Uda antennas are known to be difficult to design and optimize due to their sensitivity at high gain, and the inclusion of numerous parasitic elements. We present a genetic algorithm-based automated antenna optimization system that uses a fixed Yagi-Uda topology and a byte-encoded antenna representation. The fitness calculation allows the implicit relationship between power gain and sidelobe/backlobe loss to emerge naturally, a technique that is less complex than previous approaches. The genetic operators used are also simpler. Our results include Yagi-Uda antennas that have excellent bandwidth and gain properties with very good impedance characteristics. Results exceeded previous Yagi-Uda antennas produced via evolutionary algorithms by at least 7.8% in mainlobe gain. We also present encouraging preliminary results where a coevolutionary genetic algorithm is used.

Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

NASA Astrophysics Data System (ADS)

Wang, Chun; Ji, Zhicheng; Wang, Yan

2017-07-01

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

PubMed Central

Frutos, M.; Méndez, M.; Tohmé, F.; Broz, D.

2013-01-01

Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs) for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier. PMID:24489502

Multiobjective Optimization Using a Pareto Differential Evolution Approach

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

Differential Evolution is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. In this paper, the Differential Evolution algorithm is extended to multiobjective optimization problems by using a Pareto-based approach. The algorithm performs well when applied to several test optimization problems from the literature.

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

2008-01-01

other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP 󈨦), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third

77 FR 31342 - Application To Export Electric Energy; Emera Energy Services Subsidiaries

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-25

... DEPARTMENT OF ENERGY [OE Docket No. EA-321-A, EA-322-A, EA-323-A, EA-324-A and EA-325-A...)). On April 19, 2007, the Department of Energy (DOE) issued Order No. EA-321, EA-322, EA-323, EA-324 and... Canada as power marketers for a five-year term. The current export authorities in Order No EA-321, EA-322...

Development of X-TOOLSS: Preliminary Design of Space Systems Using Evolutionary Computation

NASA Technical Reports Server (NTRS)

Schnell, Andrew R.; Hull, Patrick V.; Turner, Mike L.; Dozier, Gerry; Alverson, Lauren; Garrett, Aaron; Reneau, Jarred

2008-01-01

Evolutionary computational (EC) techniques such as genetic algorithms (GA) have been identified as promising methods to explore the design space of mechanical and electrical systems at the earliest stages of design. In this paper the authors summarize their research in the use of evolutionary computation to develop preliminary designs for various space systems. An evolutionary computational solver developed over the course of the research, X-TOOLSS (Exploration Toolset for the Optimization of Launch and Space Systems) is discussed. With the success of early, low-fidelity example problems, an outline of work involving more computationally complex models is discussed.

Evolutionary tree reconstruction

NASA Technical Reports Server (NTRS)

Cheeseman, Peter; Kanefsky, Bob

1990-01-01

It is described how Minimum Description Length (MDL) can be applied to the problem of DNA and protein evolutionary tree reconstruction. If there is a set of mutations that transform a common ancestor into a set of the known sequences, and this description is shorter than the information to encode the known sequences directly, then strong evidence for an evolutionary relationship has been found. A heuristic algorithm is described that searches for the simplest tree (smallest MDL) that finds close to optimal trees on the test data. Various ways of extending the MDL theory to more complex evolutionary relationships are discussed.

XTALOPT: An open-source evolutionary algorithm for crystal structure prediction

NASA Astrophysics Data System (ADS)

Lonie, David C.; Zurek, Eva

2011-02-01

The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely available to the scientific community for use and collaboration under the GNU Public License. Running time: User dependent. The program runs until stopped by the user.

Improved Cloud Detection Utilizing Defense Meteorological Satellite Program near Infrared Measurements

DTIC Science & Technology

1982-01-27

Visible 3. 3 Ea r th Location, Colocation, and Normalization 4. IMAGE ANALYSIS 4. 1 Interactive Capabilities 4.2 Examples 5. AUTOMATED CLOUD...computer Interactive Data Access System (McIDAS) before image analysis and algorithm development were done. Earth-location is an automated procedure to...the factor l / s in (SSE) toward the gain settings given in Table 5. 4. IMAGE ANALYSIS 4.1 Interactive Capabilities The development of automated

Explicit Building-Block Multiobjective Genetic Algorithms: Theory, Analysis, and Development

DTIC Science & Technology

2003-03-01

Member Date Dr. (Maj) David A. Van Veldhuizen Committee Member Date Dr. Richard F. Deckro Dean’s Representative Date Accepted: Robert A. Calico, Jr...results in increased throughput and vice-versa. Van Veldhuizen validated the concept that BBs exist and are useful in the multiob- jective domain [184...extended to multiobjective functions. Van Veldhuizen states that BBs are not handled differently by MOEAs as compared to EAs. Even though an MOEA

Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

PubMed

López-Ibáñez, Manuel; Prasad, T Devi; Paechter, Ben

2011-01-01

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels; or explicitly, by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain fewer than the maximum number of switches. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.

Memetic Algorithms, Domain Knowledge, and Financial Investing

ERIC Educational Resources Information Center

Du, Jie

2012-01-01

While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial…

Decentralized diagnostics based on a distributed micro-genetic algorithm for transducer networks monitoring large experimental systems.

PubMed

Arpaia, P; Cimmino, P; Girone, M; La Commara, G; Maisto, D; Manna, C; Pezzetti, M

2014-09-01

Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.

Integrating instance selection, instance weighting, and feature weighting for nearest neighbor classifiers by coevolutionary algorithms.

PubMed

Derrac, Joaquín; Triguero, Isaac; Garcia, Salvador; Herrera, Francisco

2012-10-01

Cooperative coevolution is a successful trend of evolutionary computation which allows us to define partitions of the domain of a given problem, or to integrate several related techniques into one, by the use of evolutionary algorithms. It is possible to apply it to the development of advanced classification methods, which integrate several machine learning techniques into a single proposal. A novel approach integrating instance selection, instance weighting, and feature weighting into the framework of a coevolutionary model is presented in this paper. We compare it with a wide range of evolutionary and nonevolutionary related methods, in order to show the benefits of the employment of coevolution to apply the techniques considered simultaneously. The results obtained, contrasted through nonparametric statistical tests, show that our proposal outperforms other methods in the comparison, thus becoming a suitable tool in the task of enhancing the nearest neighbor classifier.

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Aybar-Ruíz, A.; Camacho-Gómez, C.; Pereira, E.

2018-03-01

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

PubMed Central

Liu, Min-Yin; Huang, Adam; Huang, Norden E.

2017-01-01

Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz) measured by electroencephalography (EEG) mostly during non-rapid eye movement (NREM) stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1) the lack of common benchmark databases, and (2) the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA), the Strength Pareto Evolutionary Algorithm (SPEA2), to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT), and two Hilbert-Huang transform (HHT) based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737. PMID:28572762

A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

PubMed Central

Song, Zhiming; Wang, Maocai; Dai, Guangming; Vasile, Massimiliano

2015-01-01

As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m − 1)-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA) is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m − 1)-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper. PMID:25874246

Inferring duplications, losses, transfers and incomplete lineage sorting with nonbinary species trees.

PubMed

Stolzer, Maureen; Lai, Han; Xu, Minli; Sathaye, Deepa; Vernot, Benjamin; Durand, Dannie

2012-09-15

Gene duplication (D), transfer (T), loss (L) and incomplete lineage sorting (I) are crucial to the evolution of gene families and the emergence of novel functions. The history of these events can be inferred via comparison of gene and species trees, a process called reconciliation, yet current reconciliation algorithms model only a subset of these evolutionary processes. We present an algorithm to reconcile a binary gene tree with a nonbinary species tree under a DTLI parsimony criterion. This is the first reconciliation algorithm to capture all four evolutionary processes driving tree incongruence and the first to reconcile non-binary species trees with a transfer model. Our algorithm infers all optimal solutions and reports complete, temporally feasible event histories, giving the gene and species lineages in which each event occurred. It is fixed-parameter tractable, with polytime complexity when the maximum species outdegree is fixed. Application of our algorithms to prokaryotic and eukaryotic data show that use of an incomplete event model has substantial impact on the events inferred and resulting biological conclusions. Our algorithms have been implemented in Notung, a freely available phylogenetic reconciliation software package, available at http://www.cs.cmu.edu/~durand/Notung. mstolzer@andrew.cmu.edu.

Hidden long evolutionary memory in a model biochemical network

NASA Astrophysics Data System (ADS)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Computer-automated evolution of an X-band antenna for NASA's Space Technology 5 mission.

PubMed

Hornby, Gregory S; Lohn, Jason D; Linden, Derek S

2011-01-01

Whereas the current practice of designing antennas by hand is severely limited because it is both time and labor intensive and requires a significant amount of domain knowledge, evolutionary algorithms can be used to search the design space and automatically find novel antenna designs that are more effective than would otherwise be developed. Here we present our work in using evolutionary algorithms to automatically design an X-band antenna for NASA's Space Technology 5 (ST5) spacecraft. Two evolutionary algorithms were used: the first uses a vector of real-valued parameters and the second uses a tree-structured generative representation for constructing the antenna. The highest-performance antennas from both algorithms were fabricated and tested and both outperformed a hand-designed antenna produced by the antenna contractor for the mission. Subsequent changes to the spacecraft orbit resulted in a change in requirements for the spacecraft antenna. By adjusting our fitness function we were able to rapidly evolve a new set of antennas for this mission in less than a month. One of these new antenna designs was built, tested, and approved for deployment on the three ST5 spacecraft, which were successfully launched into space on March 22, 2006. This evolved antenna design is the first computer-evolved antenna to be deployed for any application and is the first computer-evolved hardware in space.

How evolutionary crystal structure prediction works--and why.

PubMed

Oganov, Artem R; Lyakhov, Andriy O; Valle, Mario

2011-03-15

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties. © 2011 American Chemical Society

Statistical intercomparison and validation of multisensory aerosol optical depth retrievals over three AERONET sites in Kenya, East Africa

NASA Astrophysics Data System (ADS)

Boiyo, Richard; Kumar, K. Raghavendra; Zhao, Tianliang

2017-11-01

Over the last two decades, a number of space-borne sensors have been used to retrieve aerosol optical depth (AOD). The reliability of these datasets over East Africa (EA), however, is an important issue in the interpretation of regional aerosol variability. This study provides an intercomparison and validation of AOD retrievals from the MODIS-Terra (DT and DB), MISR and OMI sensors against ground-based measurements from the AERONET over three sites (CRPSM_Malindi, Nairobi, and ICIPE_Mbita) in Kenya, EA during the periods 2008-2013, 2005-2009 and 2006-2015, respectively. The analysis revealed that MISR performed better over the three sites with about 82.5% of paired AOD data falling within the error envelope (EE). MODIS-DT showed good agreement against AERONET with 59.05% of paired AOD falling within the sensor EE over terrestrial surfaces with relatively high vegetation cover. The comparison between MODIS-DB and AERONET revealed an overall lower performance with lower Gfraction (48.93%) and lower correlation r = 0.58; while AOD retrieved from OMI showed less correspondence with AERONET data with lower Gfraction (68.89%) and lowest correlation r = 0.31. The monthly evaluation of AODs retrieved from the sensors against AERONET AOD indicates that MODIS-DT has the best performance over the three sites with highest correlation (0.71-0.84), lowest RMSE and spread closer to the AERONET. Regarding seasonal analysis, MISR performed well during most seasons over Nairobi and Mbita; while MODIS-DT performed better than all other sensors during most seasons over Malindi. Furthermore, the best seasonal performance of most sensors relative to AERONET data occurred during June-August (JJA) attributed to modulations induced by a precipitation-vegetation factor to AOD satellite retrieval algorithms. The study revealed the strength and weakness of each of the retrieval algorithm and forms the basis for further research on the validation of satellite retrieved aerosol products over EA.

Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

NASA Astrophysics Data System (ADS)

Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

2018-03-01

In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

Classification of adaptive memetic algorithms: a comparative study.

PubMed

Ong, Yew-Soon; Lim, Meng-Hiot; Zhu, Ning; Wong, Kok-Wai

2006-02-01

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

NASA Astrophysics Data System (ADS)

Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi

2017-11-01

Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the "curse of dimensionality" has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

PubMed

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

NASA Astrophysics Data System (ADS)

Abdul Rani, Khairul Najmy; Abdulmalek, Mohamedfareq; A. Rahim, Hasliza; Siew Chin, Neoh; Abd Wahab, Alawiyah

2017-04-01

This research proposes the various versions of modified cuckoo search (MCS) metaheuristic algorithm deploying the strength Pareto evolutionary algorithm (SPEA) multiobjective (MO) optimization technique in rectangular array geometry synthesis. Precisely, the MCS algorithm is proposed by incorporating the Roulette wheel selection operator to choose the initial host nests (individuals) that give better results, adaptive inertia weight to control the positions exploration of the potential best host nests (solutions), and dynamic discovery rate to manage the fraction probability of finding the best host nests in 3-dimensional search space. In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively. All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler-Deb-Thiele’s (ZDT’s) test functions. Pareto optimum trade-offs are done to generate a set of three non-dominated solutions, which are locations, excitation amplitudes, and excitation phases of array elements, respectively. Overall, simulations demonstrates that the proposed MCSPSOSPEA outperforms other compatible competitors, in gaining a high antenna directivity, small half-power beamwidth (HPBW), low average side lobe level (SLL) suppression, and/or significant predefined nulls mitigation, simultaneously.

Parameter meta-optimization of metaheuristics of solving specific NP-hard facility location problem

NASA Astrophysics Data System (ADS)

Skakov, E. S.; Malysh, V. N.

2018-03-01

The aim of the work is to create an evolutionary method for optimizing the values of the control parameters of metaheuristics of solving the NP-hard facility location problem. A system analysis of the tuning process of optimization algorithms parameters is carried out. The problem of finding the parameters of a metaheuristic algorithm is formulated as a meta-optimization problem. Evolutionary metaheuristic has been chosen to perform the task of meta-optimization. Thus, the approach proposed in this work can be called “meta-metaheuristic”. Computational experiment proving the effectiveness of the procedure of tuning the control parameters of metaheuristics has been performed.

Multidimensional scaling for evolutionary algorithms--visualization of the path through search space and solution space using Sammon mapping.

PubMed

Pohlheim, Hartmut

2006-01-01

Multidimensional scaling as a technique for the presentation of high-dimensional data with standard visualization techniques is presented. The technique used is often known as Sammon mapping. We explain the mathematical foundations of multidimensional scaling and its robust calculation. We also demonstrate the use of this technique in the area of evolutionary algorithms. First, we present the visualization of the path through the search space of the best individuals during an optimization run. We then apply multidimensional scaling to the comparison of multiple runs regarding the variables of individuals and multi-criteria objective values (path through the solution space).

The Induction of Growth Inhibition and Apoptosis in HeLa and MCF-7 Cells by Teucrium sandrasicum, Having Effective Antioxidant Properties.

PubMed

Tarhan, Leman; Nakipoğlu, Mahmure; Kavakcıoğlu, Berna; Tongul, Burcu; Nalbantsoy, Ayşe

2016-03-01

The hidromethanolic (Met/W), ethyl acetate (EA(EA/W)), and water (W(EA/W)) extracts from Teucrium sandrasicum leaves (L) and flowers (F) were investigated for antioxidant properties and antiproliferative effects on HeLa, MCF-7, and L929. The highest DPPH scavenging, metal chelating capacities, and total phenolic and flavonoid contents were observed in Met/WL. The highest hydroxyl scavenging and reducing power capacities were found in EA(EA/W)L. Met/WL, EA(EA/W)L and EA(EA/W)F inhibited cancer cell growths, while they did not show significant cytotoxicity on L929. While the reactive oxygen species (ROS) levels were generally close to controls in HeLa, they were induced in MCF-7 with the treatment of Met/WL, EA(EA/W)L, and EA(EA/W)F and acted as antioxidant for L929. The highest apoptosis inductions were observed in Met/WL-treated HeLa and EA(EA/W)L-treated MCF-7, which were supported with the changes in mitochondrial membrane potentials. The highest caspase-9 activities were found in Met/WL-treated HeLa and EA(EA/W)F-treated MCF-7. Caspase-3 activity was only induced in EA(EA/W)F-treated HeLa.

The Hispanic Paradox and Predictors of Mortality in an Aging Bi-ethnic Cohort of Mexican Americans and European Americans: The San Antonio Longitudinal Study of Aging

PubMed Central

Espinoza, Sara E.; Jung, Inkyung; Hazuda, Helen

2013-01-01

OBJECTIVES To examine predictors of mortality in aging Mexican Americans (MAs) and European Americans (EAs). DESIGN Longitudinal, observational cohort study. SETTING Socioeconomically diverse neighborhoods in San Antonio, Texas. PARTICIPANTS Three hundred and ninety-four MA and 355 EA community-dwelling older adults (65+) who completed the baseline examination (1992–96) of the San Antonio Longitudinal Study of Aging (SALSA) and for whom vital status was ascertained over an average 8.2 years of follow-up. MEASUREMENTS Ethnic group was classified using a validated algorithm. Hazards ratios (HR) for mortality were estimated using Cox proportional hazards models with age, sex, ethnic group, education, income, frailty, diabetes with and without complications, comorbidity, cognition, depressive symptoms, and body mass index included as predictors in sequential models. RESULTS At baseline, MAs had higher prevalence of diabetes and frailty and lower socioeconomic status (SES) compared to EAs. The age- and sex-adjusted ethnic HR (MA vs. EA) for mortality was 1.54 (95% CI: 1.17–2.03). After adjusting for SES, the ethnic HR was no longer significant (HR = 1.16, 95% CI: 0.83–1.61). In the final model, comorbidity, diabetes with complications, depressive symptoms, and cognitive impairment were significant independent risk factors for mortality. CONCLUSION Contrary to the Hispanic paradox, MAs were at increased risk of mortality. Moreover, this ethnic disparity was largely explained by SES differences. Significant independent predictors of mortality, regardless of ethnic group, included diabetes with complications, comorbidity, depressive symptoms and cognitive impairment. Mortality reduction in older MAs requires attention to both socioeconomic disparities and disease factors. PMID:24000922

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Biology Needs Evolutionary Software Tools: Let’s Build Them Right

PubMed Central

Team, Galaxy; Goecks, Jeremy; Taylor, James

2018-01-01

Abstract Research in population genetics and evolutionary biology has always provided a computational backbone for life sciences as a whole. Today evolutionary and population biology reasoning are essential for interpretation of large complex datasets that are characteristic of all domains of today’s life sciences ranging from cancer biology to microbial ecology. This situation makes algorithms and software tools developed by our community more important than ever before. This means that we, developers of software tool for molecular evolutionary analyses, now have a shared responsibility to make these tools accessible using modern technological developments as well as provide adequate documentation and training. PMID:29688462

Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

PubMed

Suchow, Jordan W; Bourgin, David D; Griffiths, Thomas L

2017-07-01

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Rainfall estimation for real time flood monitoring using geostationary meteorological satellite data

NASA Astrophysics Data System (ADS)

Veerakachen, Watcharee; Raksapatcharawong, Mongkol

2015-09-01

Rainfall estimation by geostationary meteorological satellite data provides good spatial and temporal resolutions. This is advantageous for real time flood monitoring and warning systems. However, a rainfall estimation algorithm developed in one region needs to be adjusted for another climatic region. This work proposes computationally-efficient rainfall estimation algorithms based on an Infrared Threshold Rainfall (ITR) method calibrated with regional ground truth. Hourly rain gauge data collected from 70 stations around the Chao-Phraya river basin were used for calibration and validation of the algorithms. The algorithm inputs were derived from FY-2E satellite observations consisting of infrared and water vapor imagery. The results were compared with the Global Satellite Mapping of Precipitation (GSMaP) near real time product (GSMaP_NRT) using the probability of detection (POD), root mean square error (RMSE) and linear correlation coefficient (CC) as performance indices. Comparison with the GSMaP_NRT product for real time monitoring purpose shows that hourly rain estimates from the proposed algorithm with the error adjustment technique (ITR_EA) offers higher POD and approximately the same RMSE and CC with less data latency.

Initial Results from an Energy-Aware Airborne Dynamic, Data-Driven Application System Performing Sampling in Coherent Boundary-Layer Structures

NASA Astrophysics Data System (ADS)

Frew, E.; Argrow, B. M.; Houston, A. L.; Weiss, C.

2014-12-01

The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. Implementation details of a complete EA-DDDAS will be provided, along with preliminary flight test results targeting coherent boundary-layer structures.

Educational Tool for Optimal Controller Tuning Using Evolutionary Strategies

ERIC Educational Resources Information Center

Carmona Morales, D.; Jimenez-Hornero, J. E.; Vazquez, F.; Morilla, F.

2012-01-01

In this paper, an optimal tuning tool is presented for control structures based on multivariable proportional-integral-derivative (PID) control, using genetic algorithms as an alternative to traditional optimization algorithms. From an educational point of view, this tool provides students with the necessary means to consolidate their knowledge on…

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

PubMed

Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

2012-09-01

In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

A hybrid multi-objective evolutionary algorithm for wind-turbine blade optimization

NASA Astrophysics Data System (ADS)

Sessarego, M.; Dixon, K. R.; Rival, D. E.; Wood, D. H.

2015-08-01

A concurrent-hybrid non-dominated sorting genetic algorithm (hybrid NSGA-II) has been developed and applied to the simultaneous optimization of the annual energy production, flapwise root-bending moment and mass of the NREL 5 MW wind-turbine blade. By hybridizing a multi-objective evolutionary algorithm (MOEA) with gradient-based local search, it is believed that the optimal set of blade designs could be achieved in lower computational cost than for a conventional MOEA. To measure the convergence between the hybrid and non-hybrid NSGA-II on a wind-turbine blade optimization problem, a computationally intensive case was performed using the non-hybrid NSGA-II. From this particular case, a three-dimensional surface representing the optimal trade-off between the annual energy production, flapwise root-bending moment and blade mass was achieved. The inclusion of local gradients in the blade optimization, however, shows no improvement in the convergence for this three-objective problem.

Honey bee-inspired algorithms for SNP haplotype reconstruction problem

NASA Astrophysics Data System (ADS)

PourkamaliAnaraki, Maryam; Sadeghi, Mehdi

2016-03-01

Reconstructing haplotypes from SNP fragments is an important problem in computational biology. There have been a lot of interests in this field because haplotypes have been shown to contain promising data for disease association research. It is proved that haplotype reconstruction in Minimum Error Correction model is an NP-hard problem. Therefore, several methods such as clustering techniques, evolutionary algorithms, neural networks and swarm intelligence approaches have been proposed in order to solve this problem in appropriate time. In this paper, we have focused on various evolutionary clustering techniques and try to find an efficient technique for solving haplotype reconstruction problem. It can be referred from our experiments that the clustering methods relying on the behaviour of honey bee colony in nature, specifically bees algorithm and artificial bee colony methods, are expected to result in more efficient solutions. An application program of the methods is available at the following link. http://www.bioinf.cs.ipm.ir/software/haprs/

A Method to Search for Correlations of Ultra-high Energy Cosmic-Ray Masses with the Large-scale Structures in the Local Galaxy Density Field

NASA Astrophysics Data System (ADS)

Ivanov, A. A.

2013-02-01

One of the main goals of investigations using present and future giant extensive air shower (EAS) arrays is the mass composition of ultra-high energy cosmic rays (UHECRs). A new approach to the problem is presented, combining the analysis of arrival directions with the statistical test of the paired EAS samples. One of the ideas of the method is to search for possible correlations between UHECR masses and their separate sources; for instance, if there are two sources in different areas of the celestial sphere injecting different nuclei, but the fluxes are comparable so that arrival directions are isotropic, then the aim is to reveal a difference in the mass composition of cosmic-ray fluxes. The method is based on a non-parametric statistical test—the Wilcoxon signed-rank routine—which does not depend on the populations fitting any parameterized distributions. Two particular algorithms are proposed: first, using measurements of the depth of the EAS maximum position in the atmosphere; and second, relying on the age variance of air showers initiated by different primary particles. The formulated method is applied to the Yakutsk array data, in order to demonstrate the possibility of searching for a difference in average mass composition between the two UHECR sets, arriving particularly from the supergalactic plane and a complementary region.

Improved Evolutionary Programming with Various Crossover Techniques for Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Tangpatiphan, Kritsana; Yokoyama, Akihiko

This paper presents an Improved Evolutionary Programming (IEP) for solving the Optimal Power Flow (OPF) problem, which is considered as a non-linear, non-smooth, and multimodal optimization problem in power system operation. The total generator fuel cost is regarded as an objective function to be minimized. The proposed method is an Evolutionary Programming (EP)-based algorithm with making use of various crossover techniques, normally applied in Real Coded Genetic Algorithm (RCGA). The effectiveness of the proposed approach is investigated on the IEEE 30-bus system with three different types of fuel cost functions; namely the quadratic cost curve, the piecewise quadratic cost curve, and the quadratic cost curve superimposed by sine component. These three cost curves represent the generator fuel cost functions with a simplified model and more accurate models of a combined-cycle generating unit and a thermal unit with value-point loading effect respectively. The OPF solutions by the proposed method and Pure Evolutionary Programming (PEP) are observed and compared. The simulation results indicate that IEP requires less computing time than PEP with better solutions in some cases. Moreover, the influences of important IEP parameters on the OPF solution are described in details.

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Design Mining Interacting Wind Turbines.

PubMed

Preen, Richard J; Bull, Larry

2016-01-01

An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions were made. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Applications of genetic programming in cancer research.

PubMed

Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

2009-02-01

The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

Design and implementation of EP-based PID controller for chaos synchronization of Rikitake circuit systems.

PubMed

Hou, Yi-You

2017-09-01

This article addresses an evolutionary programming (EP) algorithm technique-based and proportional-integral-derivative (PID) control methods are established to guarantee synchronization of the master and slave Rikitake chaotic systems. For PID synchronous control, the evolutionary programming (EP) algorithm is used to find the optimal PID controller parameters k p , k i , k d by integrated absolute error (IAE) method for the convergence conditions. In order to verify the system performance, the basic electronic components containing operational amplifiers (OPAs), resistors, and capacitors are used to implement the proposed chaotic Rikitake systems. Finally, the experimental results validate the proposed Rikitake chaotic synchronization approach. Copyright © 2017. Published by Elsevier Ltd.

Performance optimization of the power user electric energy data acquire system based on MOEA/D evolutionary algorithm

NASA Astrophysics Data System (ADS)

Ding, Zhongan; Gao, Chen; Yan, Shengteng; Yang, Canrong

2017-10-01

The power user electric energy data acquire system (PUEEDAS) is an important part of smart grid. This paper builds a multi-objective optimization model for the performance of the PUEEADS from the point of view of the combination of the comprehensive benefits and cost. Meanwhile, the Chebyshev decomposition approach is used to decompose the multi-objective optimization problem. We design a MOEA/D evolutionary algorithm to solve the problem. By analyzing the Pareto optimal solution set of multi-objective optimization problem and comparing it with the monitoring value to grasp the direction of optimizing the performance of the PUEEDAS. Finally, an example is designed for specific analysis.

XTALOPT version r11: An open-source evolutionary algorithm for crystal structure prediction

NASA Astrophysics Data System (ADS)

Avery, Patrick; Falls, Zackary; Zurek, Eva

2018-01-01

Version 11 of XTALOPT, an evolutionary algorithm for crystal structure prediction, has now been made available for download from the CPC library or the XTALOPT website, http://xtalopt.github.io. Whereas the previous versions of XTALOPT were published under the Gnu Public License (GPL), the current version is made available under the 3-Clause BSD License, which is an open source license that is recognized by the Open Source Initiative. Importantly, the new version can be executed via a command line interface (i.e., it does not require the use of a Graphical User Interface). Moreover, the new version is written as a stand-alone program, rather than an extension to AVOGADRO.

Android malware detection based on evolutionary super-network

NASA Astrophysics Data System (ADS)

Yan, Haisheng; Peng, Lingling

2018-04-01

In the paper, an android malware detection method based on evolutionary super-network is proposed in order to improve the precision of android malware detection. Chi square statistics method is used for selecting characteristics on the basis of analyzing android authority. Boolean weighting is utilized for calculating characteristic weight. Processed characteristic vector is regarded as the system training set and test set; hyper edge alternative strategy is used for training super-network classification model, thereby classifying test set characteristic vectors, and it is compared with traditional classification algorithm. The results show that the detection method proposed in the paper is close to or better than traditional classification algorithm. The proposed method belongs to an effective Android malware detection means.

Neural-network-enhanced evolutionary algorithm applied to supported metal nanoparticles

NASA Astrophysics Data System (ADS)

Kolsbjerg, E. L.; Peterson, A. A.; Hammer, B.

2018-05-01

We show that approximate structural relaxation with a neural network enables orders of magnitude faster global optimization with an evolutionary algorithm in a density functional theory framework. The increased speed facilitates reliable identification of global minimum energy structures, as exemplified by our finding of a hollow Pt13 nanoparticle on an MgO support. We highlight the importance of knowing the correct structure when studying the catalytic reactivity of the different particle shapes. The computational speedup further enables screening of hundreds of different pathways in the search for optimum kinetic transitions between low-energy conformers and hence pushes the limits of the insight into thermal ensembles that can be obtained from theory.

A hybrid neural learning algorithm using evolutionary learning and derivative free local search method.

PubMed

Ghosh, Ranadhir; Yearwood, John; Ghosh, Moumita; Bagirov, Adil

2006-06-01

In this paper we investigate a hybrid model based on the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. Also we discuss different variants for hybrid models using the Discrete Gradient method and an evolutionary strategy for determining the weights in a feed forward artificial neural network. The Discrete Gradient method has the advantage of being able to jump over many local minima and find very deep local minima. However, earlier research has shown that a good starting point for the discrete gradient method can improve the quality of the solution point. Evolutionary algorithms are best suited for global optimisation problems. Nevertheless they are cursed with longer training times and often unsuitable for real world application. For optimisation problems such as weight optimisation for ANNs in real world applications the dimensions are large and time complexity is critical. Hence the idea of a hybrid model can be a suitable option. In this paper we propose different fusion strategies for hybrid models combining the evolutionary strategy with the discrete gradient method to obtain an optimal solution much quicker. Three different fusion strategies are discussed: a linear hybrid model, an iterative hybrid model and a restricted local search hybrid model. Comparative results on a range of standard datasets are provided for different fusion hybrid models.

Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

PubMed

Ferentinos, Konstantinos P

2005-09-01

Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

How Crossover Speeds up Building Block Assembly in Genetic Algorithms.

PubMed

Sudholt, Dirk

2017-01-01

We reinvestigate a fundamental question: How effective is crossover in genetic algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter, we show that using crossover makes every ([Formula: see text]+[Formula: see text]) genetic algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate [Formula: see text] and [Formula: see text]. Crossover is beneficial because it can capitalize on mutations that have both beneficial and disruptive effects on building blocks: crossover is able to repair the disruptive effects of mutation in later generations. Compared to mutation-based evolutionary algorithms, this makes multibit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from [Formula: see text] to [Formula: see text]. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building block functions.

Free-Piston Shock Tunnel Test Technique Development: An AEDC/DLR Cooperative Program

DTIC Science & Technology

2003-02-01

Calibration Data." AIAA-95-6039, April 1995. 15. Molvik, G. A., and Merkle , C. L. "A Set of Strongly Coupled, Upwind Algorithms for Com- puting Flows in...April 1997. h. Date of Termination: 28 April 2002. i. All Signing Officials, Title/Offices Represented, and Countries: (1) Herr Rolf Schreiber, Chief...agreement by the RTP MOU Executive Agents. The US RTP/EA Signature Signature Clinton V. Hom, Maj Gen. USAF Rolf Schreiber Name Name Principal Assistant

Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts.

PubMed

Dashtban, M; Balafar, Mohammadali

2017-03-01

Gene selection is a demanding task for microarray data analysis. The diverse complexity of different cancers makes this issue still challenging. In this study, a novel evolutionary method based on genetic algorithms and artificial intelligence is proposed to identify predictive genes for cancer classification. A filter method was first applied to reduce the dimensionality of feature space followed by employing an integer-coded genetic algorithm with dynamic-length genotype, intelligent parameter settings, and modified operators. The algorithmic behaviors including convergence trends, mutation and crossover rate changes, and running time were studied, conceptually discussed, and shown to be coherent with literature findings. Two well-known filter methods, Laplacian and Fisher score, were examined considering similarities, the quality of selected genes, and their influences on the evolutionary approach. Several statistical tests concerning choice of classifier, choice of dataset, and choice of filter method were performed, and they revealed some significant differences between the performance of different classifiers and filter methods over datasets. The proposed method was benchmarked upon five popular high-dimensional cancer datasets; for each, top explored genes were reported. Comparing the experimental results with several state-of-the-art methods revealed that the proposed method outperforms previous methods in DLBCL dataset. Copyright © 2017 Elsevier Inc. All rights reserved.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Generative Representations for Automated Design of Robots

NASA Technical Reports Server (NTRS)

Homby, Gregory S.; Lipson, Hod; Pollack, Jordan B.

2007-01-01

A method of automated design of complex, modular robots involves an evolutionary process in which generative representations of designs are used. The term generative representations as used here signifies, loosely, representations that consist of or include algorithms, computer programs, and the like, wherein encoded designs can reuse elements of their encoding and thereby evolve toward greater complexity. Automated design of robots through synthetic evolutionary processes has already been demonstrated, but it is not clear whether genetically inspired search algorithms can yield designs that are sufficiently complex for practical engineering. The ultimate success of such algorithms as tools for automation of design depends on the scaling properties of representations of designs. A nongenerative representation (one in which each element of the encoded design is used at most once in translating to the design) scales linearly with the number of elements. Search algorithms that use nongenerative representations quickly become intractable (search times vary approximately exponentially with numbers of design elements), and thus are not amenable to scaling to complex designs. Generative representations are compact representations and were devised as means to circumvent the above-mentioned fundamental restriction on scalability. In the present method, a robot is defined by a compact programmatic form (its generative representation) and the evolutionary variation takes place on this form. The evolutionary process is an iterative one, wherein each cycle consists of the following steps: 1. Generative representations are generated in an evolutionary subprocess. 2. Each generative representation is a program that, when compiled, produces an assembly procedure. 3. In a computational simulation, a constructor executes an assembly procedure to generate a robot. 4. A physical-simulation program tests the performance of a simulated constructed robot, evaluating the performance according to a fitness criterion to yield a figure of merit that is fed back into the evolutionary subprocess of the next iteration. In comparison with prior approaches to automated evolutionary design of robots, the use of generative representations offers two advantages: First, a generative representation enables the reuse of components in regular and hierarchical ways and thereby serves a systematic means of creating more complex modules out of simpler ones. Second, the evolved generative representation may capture intrinsic properties of the design problem, so that variations in the representations move through the design space more effectively than do equivalent variations in a nongenerative representation. This method has been demonstrated by using it to design some robots that move, variously, by walking, rolling, or sliding. Some of the robots were built (see figure). Although these robots are very simple, in comparison with robots designed by humans, their structures are more regular, modular, hierarchical, and complex than are those of evolved designs of comparable functionality synthesized by use of nongenerative representations.

Geomagnetic Navigation of Autonomous Underwater Vehicle Based on Multi-objective Evolutionary Algorithm.

PubMed

Li, Hong; Liu, Mingyong; Zhang, Feihu

2017-01-01

This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments.

An adaptive evolutionary multi-objective approach based on simulated annealing.

PubMed

Li, H; Landa-Silva, D

2011-01-01

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

Geomagnetic Navigation of Autonomous Underwater Vehicle Based on Multi-objective Evolutionary Algorithm

PubMed Central

Li, Hong; Liu, Mingyong; Zhang, Feihu

2017-01-01

This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments. PMID:28747884

An External Archive-Guided Multiobjective Particle Swarm Optimization Algorithm.

PubMed

Zhu, Qingling; Lin, Qiuzhen; Chen, Weineng; Wong, Ka-Chun; Coello Coello, Carlos A; Li, Jianqiang; Chen, Jianyong; Zhang, Jun

2017-09-01

The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.

Isolation and Characterization of Five Erwinia amylovora Bacteriophages and Assessment of Phage Resistance in Strains of Erwinia amylovora

PubMed Central

Schnabel, Elise L.; Jones, Alan L.

2001-01-01

Phages able to infect the fire blight pathogen Erwinia amylovora were isolated from apple, pear, and raspberry tissues and from soil samples collected at sites displaying fire blight symptoms. Among a collection of 50 phage isolates, 5 distinct phages, including relatives of the previously described phages φEa1 and φEa7 and 3 novel phages named φEa100, φEa125, and φEa116C, were identified based on differences in genome size and restriction fragment pattern. φEa1, the phage distributed most widely, had an approximately 46-kb genome which exhibited some restriction site variability between isolates. Phages φEa100, φEa7, and φEa125 each had genomes of approximately 35 kb and could be distinguished by their EcoRI restriction fragment patterns. φEa116C contained an approximately 75-kb genome. φEa1, φEa7, φEa100, φEa125, and φEa116C were able to infect 39, 36, 16, 20, and 40, respectively, of 40 E. amylovora strains isolated from apple orchards in Michigan and 8, 12, 10, 10, and 12, respectively, of 12 E. amylovora strains isolated from raspberry fields (Rubus spp.) in Michigan. Only 22 of 52 strains were sensitive to all five phages, and 23 strains exhibited resistance to more than one phage. φEa116C was more effective than the other phages at lysing E. amylovora strain Ea110 in liquid culture, reducing the final titer of Ea110 by >95% when added at a ratio of 1 PFU per 10 CFU and by 58 to 90% at 1 PFU per 105 CFU. PMID:11133428

Multi-Objective UAV Mission Planning Using Evolutionary Computation

DTIC Science & Technology

2008-03-01

on a Solution Space. . . . . . . . . . . . . . . . . . . . 41 4.3. Crowding distance calculation. Dark points are non-dominated solutions. [14...SPEA2 was devel- oped by Zitzler [64] as an improvement to the original SPEA algorithm [65]. SPEA2 Figure 4.3: Crowding distance calculation. Dark ...thesis, Los Angeles, CA, USA, 2003. Adviser-Maja J. Mataric . 114 21. Homberger, Joerg and Hermann Gehring. “Two Evolutionary Metaheuristics for the

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Dietary enzymatically treated Artemisia annua L. supplementation alleviates liver oxidative injury of broilers reared under high ambient temperature

NASA Astrophysics Data System (ADS)

Wan, Xiaoli; Zhang, Jingfei; He, Jintian; Bai, Kaiwen; Zhang, Lili; Wang, Tian

2017-09-01

Heat stress induced by high ambient temperature is a major concern in commercial broiler production. To evaluate the effects of dietary enzymatically treated Artemisia annua L. (EA) supplementation on growth performance and liver oxidative injury of broilers reared under heat stress, a total of 320 22-day-old male broilers were randomly allotted into five groups with eight replicates of eight birds each. Broilers in the control group were housed at 22 ± 1 °C and fed the basal diet. Broilers in the HS, HS-EA1, HS-EA2, and HS-EA3 groups were fed basal diet supplemented with 0, 0.75, 1.00, and 1.25 g/kg EA, respectively, and reared under cyclic high temperature (34 ± 1 °C for 8 h/day and 22 ± 1 °C for 16 h/day). Broilers fed EA diets had higher final body weight, average daily body weight gain, and average daily feed intake, as well as liver concentration of reduced glutathione, activities of antioxidant enzymes, abilities to inhibit hydroxyl radical and superoxide radical (HS-EA2 and HS-EA3), and lower liver concentrations of reactive oxygen metabolites, malondialdehyde, and protein carbonyl (HS-EA1, HS-EA2, and HS-EA3) than HS group ( P < 0.05). EA treatment downregulated the mRNA levels of heat shock proteins 70 and 90, upregulated the mRNA levels of nuclear factor erythroid 2-related factor 2 (HS-EA1, HS-EA2, and HS-EA3) and heme oxygenase 1 (HS-EA2 and HS-EA3) in liver of heat-treated broilers ( P < 0.05). In conclusion, EA alleviated heat stress-induced growth depression and liver oxidative injury in broilers, possibly through improving the antioxidant capacity and regulating the pertinent mRNA expression. The appropriate inclusion level of EA in broiler diet is 1.00-1.25 g/kg.

Genotoxic effect of ethacrynic acid and impact of antioxidants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ward, William M.; Hoffman, Jared D.; Loo, George, E-mail: g_loo@uncg.edu

It is known that ethacrynic acid (EA) decreases the intracellular levels of glutathione. Whether the anticipated oxidative stress affects the structural integrity of DNA is unknown. Therefore, DNA damage was assessed in EA-treated HCT116 cells, and the impact of several antioxidants was also determined. EA caused both concentration-dependent and time-dependent DNA damage that eventually resulted in cell death. Unexpectedly, the DNA damage caused by EA was intensified by either ascorbic acid or trolox. In contrast, EA-induced DNA damage was reduced by N-acetylcysteine and by the iron chelator, deferoxamine. In elucidating the DNA damage, it was determined that EA increased themore » production of reactive oxygen species, which was inhibited by N-acetylcysteine and deferoxamine but not by ascorbic acid and trolox. Also, EA decreased glutathione levels, which were inhibited by N-acetylcysteine. But, ascorbic acid, trolox, and deferoxamine neither inhibited nor enhanced the capacity of EA to decrease glutathione. Interestingly, the glutathione synthesis inhibitor, buthionine sulfoxime, lowered glutathione to a similar degree as EA, but no noticeable DNA damage was found. Nevertheless, buthionine sulfoxime potentiated the glutathione-lowering effect of EA and intensified the DNA damage caused by EA. Additionally, in examining redox-sensitive stress gene expression, it was found that EA increased HO-1, GADD153, and p21mRNA expression, in association with increased nuclear localization of Nrf-2 and p53 proteins. In contrast to ascorbic acid, trolox, and deferoxamine, N-acetylcysteine suppressed the EA-induced upregulation of GADD153, although not of HO-1. Overall, it is concluded that EA has genotoxic properties that can be amplified by certain antioxidants. - Highlights: • Ethacrynic acid (EA) caused cellular DNA damage. • EA-induced DNA damage was potentiated by ascorbic acid or trolox. • EA increased ROS production, not inhibited by ascorbic acid or trolox. • EA decreased glutathione levels, not prevented by ascorbic acid or trolox. • Buthionine sulfoxime intensified the DNA damage caused by EA.« less

Evaluation of the influence of dominance rules for the assembly line design problem under consideration of product design alternatives

NASA Astrophysics Data System (ADS)

Oesterle, Jonathan; Lionel, Amodeo

2018-06-01

The current competitive situation increases the importance of realistically estimating product costs during the early phases of product and assembly line planning projects. In this article, several multi-objective algorithms using difference dominance rules are proposed to solve the problem associated with the selection of the most effective combination of product and assembly lines. The list of developed algorithms includes variants of ant colony algorithms, evolutionary algorithms and imperialist competitive algorithms. The performance of each algorithm and dominance rule is analysed by five multi-objective quality indicators and fifty problem instances. The algorithms and dominance rules are ranked using a non-parametric statistical test.

47 CFR 11.51 - EAS code and Attention Signal Transmission requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false EAS code and Attention Signal Transmission... SYSTEM (EAS) Emergency Operations § 11.51 EAS code and Attention Signal Transmission requirements. (a... programming before EAS message transmission should not cause television receivers to mute EAS audio messages...

47 CFR 11.56 - EAS Participants receive CAP-formatted alerts.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false EAS Participants receive CAP-formatted alerts... SYSTEM (EAS) Emergency Operations § 11.56 EAS Participants receive CAP-formatted alerts. Notwithstanding anything herein to the contrary, all EAS Participants must be able to receive CAP-formatted EAS alerts no...

Adaptive elastic segmentation of brain MRI via shape-model-guided evolutionary programming.

PubMed

Pitiot, Alain; Toga, Arthur W; Thompson, Paul M

2002-08-01

This paper presents a fully automated segmentation method for medical images. The goal is to localize and parameterize a variety of types of structure in these images for subsequent quantitative analysis. We propose a new hybrid strategy that combines a general elastic template matching approach and an evolutionary heuristic. The evolutionary algorithm uses prior statistical information about the shape of the target structure to control the behavior of a number of deformable templates. Each template, modeled in the form of a B-spline, is warped in a potential field which is itself dynamically adapted. Such a hybrid scheme proves to be promising: by maintaining a population of templates, we cover a large domain of the solution space under the global guidance of the evolutionary heuristic, and thoroughly explore interesting areas. We address key issues of automated image segmentation systems. The potential fields are initially designed based on the spatial features of the edges in the input image, and are subjected to spatially adaptive diffusion to guarantee the deformation of the template. This also improves its global consistency and convergence speed. The deformation algorithm can modify the internal structure of the templates to allow a better match. We investigate in detail the preprocessing phase that the images undergo before they can be used more effectively in the iterative elastic matching procedure: a texture classifier, trained via linear discriminant analysis of a learning set, is used to enhance the contrast of the target structure with respect to surrounding tissues. We show how these techniques interact within a statistically driven evolutionary scheme to achieve a better tradeoff between template flexibility and sensitivity to noise and outliers. We focus on understanding the features of template matching that are most beneficial in terms of the achieved match. Examples from simulated and real image data are discussed, with considerations of algorithmic efficiency.

Human T cell activation. III. Induction of an early activation antigen, EA 1 by TPA, mitogens and antigens

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hara, T.; Jung, L.K.L.; FU, S.M.

1986-03-01

With human T cells activated for 12 hours by 12-o-tetradecanoyl phorbol-13-acetate (TPA) as immunogen, an IgG/sub 2a/ monoclonal antibody, mAb Ea 1, has been generated to a 60KD phosphorylated protein with 32KD and 28KD subunits. The antigen, Ea 1, is readily detected on 60% of isolated thymocytes by indirect immunofluorescence. A low level of Ea 1 expression is detectable on 2-6% of blood lymphocytes. Isolated T cells have been induced to express Ea 1 by TPA, mitogens and anitgens. TPA activated T cells express Ea 1 as early as 1 hour after activation. By 4 hours, greater than 95% ofmore » the T cells stain with mAb Ea 1. About 50% of the PHA or Con A activated T cells express Ea 1 with a similar kinetics. Ea 1 expression proceeds that of IL-2 receptor in these activation processes. T cells activated by soluble antigens (tetanus toxoid and PPD) and alloantigens in MLR also express Ea 1 after a long incubation. About 20% of the T cells stain for Ea 1 at day 6. Ea 1 expression is not limited to activated T cells. B cells activated by TPA or anti-IgM Ab plus B cell growth factor express Ea 1. The kinetics of Ea 1 expression is slower and the staining is less intense. Repeated attempts to detect Ea 1 on resting and activated monocytes and granulocytes have not been successful. Ea 1 expression is due to de novo synthesis for its induction is blocked by cycloheximide and actinomycin D. Ea 1 is the earliest activation antigen detectable to-date.« less

Multiobjective optimization and multivariable control of the beer fermentation process with the use of evolutionary algorithms.

PubMed

Andrés-Toro, B; Girón-Sierra, J M; Fernández-Blanco, P; López-Orozco, J A; Besada-Portas, E

2004-04-01

This paper describes empirical research on the model, optimization and supervisory control of beer fermentation. Conditions in the laboratory were made as similar as possible to brewery industry conditions. Since mathematical models that consider realistic industrial conditions were not available, a new mathematical model design involving industrial conditions was first developed. Batch fermentations are multiobjective dynamic processes that must be guided along optimal paths to obtain good results. The paper describes a direct way to apply a Pareto set approach with multiobjective evolutionary algorithms (MOEAs). Successful finding of optimal ways to drive these processes were reported. Once obtained, the mathematical fermentation model was used to optimize the fermentation process by using an intelligent control based on certain rules.

Improving Environmental Model Calibration and Prediction

DTIC Science & Technology

2011-01-18

REPORT Final Report - Improving Environmental Model Calibration and Prediction 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: First, we have continued to...develop tools for efficient global optimization of environmental models. Our algorithms are hybrid algorithms that combine evolutionary strategies...toward practical hybrid optimization tools for environmental models. 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 18-01-2011 13

A novel model-based evolutionary algorithm for multi-objective deformable image registration with content mismatch and large deformations: benchmarking efficiency and quality

NASA Astrophysics Data System (ADS)

Bouter, Anton; Alderliesten, Tanja; Bosman, Peter A. N.

2017-02-01

Taking a multi-objective optimization approach to deformable image registration has recently gained attention, because such an approach removes the requirement of manually tuning the weights of all the involved objectives. Especially for problems that require large complex deformations, this is a non-trivial task. From the resulting Pareto set of solutions one can then much more insightfully select a registration outcome that is most suitable for the problem at hand. To serve as an internal optimization engine, currently used multi-objective algorithms are competent, but rather inefficient. In this paper we largely improve upon this by introducing a multi-objective real-valued adaptation of the recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) for discrete optimization. In this work, GOMEA is tailored specifically to the problem of deformable image registration to obtain substantially improved efficiency. This improvement is achieved by exploiting a key strength of GOMEA: iteratively improving small parts of solutions, allowing to faster exploit the impact of such updates on the objectives at hand through partial evaluations. We performed experiments on three registration problems. In particular, an artificial problem containing a disappearing structure, a pair of pre- and post-operative breast CT scans, and a pair of breast MRI scans acquired in prone and supine position were considered. Results show that compared to the previously used evolutionary algorithm, GOMEA obtains a speed-up of up to a factor of 1600 on the tested registration problems while achieving registration outcomes of similar quality.

Fundamental resource-allocating model in colleges and universities based on Immune Clone Algorithms

NASA Astrophysics Data System (ADS)

Ye, Mengdie

2017-05-01

In this thesis we will seek the combination of antibodies and antigens converted from the optimal course arrangement and make an analogy with Immune Clone Algorithms. According to the character of the Algorithms, we apply clone, clone gene and clone selection to arrange courses. Clone operator can combine evolutionary search and random search, global search and local search. By cloning and clone mutating candidate solutions, we can find the global optimal solution quickly.

Evolutionary game based control for biological systems with applications in drug delivery.

PubMed

Li, Xiaobo; Lenaghan, Scott C; Zhang, Mingjun

2013-06-07

Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. Copyright © 2013 Elsevier Ltd. All rights reserved.

Computational complexity of ecological and evolutionary spatial dynamics

PubMed Central

Ibsen-Jensen, Rasmus; Chatterjee, Krishnendu; Nowak, Martin A.

2015-01-01

There are deep, yet largely unexplored, connections between computer science and biology. Both disciplines examine how information proliferates in time and space. Central results in computer science describe the complexity of algorithms that solve certain classes of problems. An algorithm is deemed efficient if it can solve a problem in polynomial time, which means the running time of the algorithm is a polynomial function of the length of the input. There are classes of harder problems for which the fastest possible algorithm requires exponential time. Another criterion is the space requirement of the algorithm. There is a crucial distinction between algorithms that can find a solution, verify a solution, or list several distinct solutions in given time and space. The complexity hierarchy that is generated in this way is the foundation of theoretical computer science. Precise complexity results can be notoriously difficult. The famous question whether polynomial time equals nondeterministic polynomial time (i.e., P = NP) is one of the hardest open problems in computer science and all of mathematics. Here, we consider simple processes of ecological and evolutionary spatial dynamics. The basic question is: What is the probability that a new invader (or a new mutant) will take over a resident population? We derive precise complexity results for a variety of scenarios. We therefore show that some fundamental questions in this area cannot be answered by simple equations (assuming that P is not equal to NP). PMID:26644569

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

The effect of orthology and coregulation on detecting regulatory motifs.

PubMed

Storms, Valerie; Claeys, Marleen; Sanchez, Aminael; De Moor, Bart; Verstuyf, Annemieke; Marchal, Kathleen

2010-02-03

Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Storms_Valerie_PlosONE.

The Effect of Orthology and Coregulation on Detecting Regulatory Motifs

PubMed Central

Storms, Valerie; Claeys, Marleen; Sanchez, Aminael; De Moor, Bart; Verstuyf, Annemieke; Marchal, Kathleen

2010-01-01

Background Computational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coregulation information when searching for motifs. Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information. In this study, we evaluated the conditions under which complementing coregulation with orthologous information improves motif detection for the class of probabilistic motif detection algorithms with an explicit evolutionary model. Methodology We designed datasets (real and synthetic) covering different degrees of coregulation and orthologous information to test how well Phylogibbs and Phylogenetic sampler, as representatives of the motif detection algorithms with evolutionary model performed as compared to MEME, a more classical motif detection algorithm that treats orthologs independently. Results and Conclusions Under certain conditions detecting motifs in the combined coregulation-orthology space is indeed more efficient than using each space separately, but this is not always the case. Moreover, the difference in success rate between the advanced algorithms and MEME is still marginal. The success rate of motif detection depends on the complex interplay between the added information and the specificities of the applied algorithms. Insights in this relation provide information useful to both developers and users. All benchmark datasets are available at http://homes.esat.kuleuven.be/~kmarchal/Supplementary_Storms_Valerie_PlosONE. PMID:20140085

EEG/ERP adaptive noise canceller design with controlled search space (CSS) approach in cuckoo and other optimization algorithms.

PubMed

Ahirwal, M K; Kumar, Anil; Singh, G K

2013-01-01

This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

PubMed Central

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

PubMed

Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine

2012-12-09

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a multiobjective evolutionary algorithm SPEA2(26), and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

The Future of Small Navy Ship Sickbays and Army Aeromedical Evacuation Aircraft

DTIC Science & Technology

2014-12-01

HEAT SEALING W/PO 1 EA 9B 715 6685015840785 MONITOR, HEAT STRESS 2 EA 9B 715 6670010976167 SCALE,WEIGHING 1 EA 9B 715 6530016200664 STERILIZER M11...ELECTRONIC THERMOMETER SURE TEMP PLUS 1 EA 9B 515 6685015840785 MONITOR, HEAT STRESS 2 EA 9B 515 6135015308136 BATTERY POWER SOURCE NON...6685015840785 MONITOR, HEAT STRESS 2 EA 9B 315 6530016200664 STERILIZER M11 ULTRACLAVE 115V AUTOMATIC DOOR 1 EA 9B 315 6685015816875 CALIBRATION KEY

Environmental assessment in The Netherlands: Effectively governing environmental protection? A discourse analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Runhaar, Hens, E-mail: h.a.c.runhaar@uu.nl; Laerhoven, Frank van, E-mail: vanLaerhoven@uu.nl; Driessen, Peter, E-mail: p.driessen@uu.nl

Environmental assessment (EA) aims to enhance environmental awareness and to ensure that environmental values are fully considered in decision-making. In the EA arena, different discourses exist on what EA should aim for and how it functions. We hypothesise that these discourses influence its application in practice as well as its effectiveness in terms of achieving the above goals. For instance, actors who consider EA as a hindrance to fast implementation of their projects will probably apply it as a mandatory checklist, whereas actors who believe that EA can help to develop more environmentally sound decisions will use EIA as amore » tool to design their initiatives. In this paper we explore discourses on EA in The Netherlands and elaborate on their implications for EA effectiveness. Based on an innovative research design comprising an online survey with 443 respondents and 20 supplementary semi-structured interviews we conclude that the dominant discourse is that EA is mainly a legal requirement; EAs are conducted because they have to be conducted, not because actors choose to do so. EA effectiveness however seems reasonably high, as a majority of respondents perceive that it enhances environmental awareness and contributes to environmental protection. However, the 'legal requirement' discourse also results in decision-makers seldom going beyond what is prescribed by EA and environmental law. Despite its mandatory character, the predominant attitude towards EA is quite positive. For most respondents, EA is instrumental in providing transparency of decision-making and in minimising the legal risks of not complying with environmental laws. Differences in discourses seldom reflect extreme opposites. The 'common ground' regarding EA provides a good basis for working with EA in terms of meeting legal requirements but at the same time does not stimulate creativity in decision-making or optimisation of environmental values. In countries characterised by less consensual political cultures we may expect more extreme discourses on EA, the consequences of which are reflected upon in this paper. - Highlights: Black-Right-Pointing-Pointer The effectiveness of environmental assessment (EA) depends in part on meanings associated with EA (i.e., discourse). Black-Right-Pointing-Pointer Our results suggest that the general discourse in The Netherlands is that EA is a legal requirement, nothing more. Black-Right-Pointing-Pointer This discourse makes EA effective in protecting the environment, but not in the optimisation of environmental values. Black-Right-Pointing-Pointer EA has a limited contribution to the development of policy alternatives or innovative solutions to environmental problems. Black-Right-Pointing-Pointer There is a high consensus among EA professionals, providing a common ground for working with EA.« less

Comparison of absolute intensity between EAS with gamma-families and general EAS at Mount Norikura

NASA Technical Reports Server (NTRS)

Mitsumune, T.; Nakatsuka, T.; Nishikawa, K.; Saito, T.; Sakata, M.; Shima, M.; Yamamoto, Y.; Dake, S.; Kawamoto, M.; Kusumose, M.

1985-01-01

Gamma-families with total energy greater than 10 TeV, found in the EX chamber which was cooperated with the EAS array were combined with EAS triggered by big bursts. The absolute intensity of the size spectrum of these combined EAS was compared with that of general EAS obtained by AS trigger. The EAS with sizes greater than 2x1 million were always accompanied by gamma-families with sigma E sub gamma H 10 TeV, n sub gamma, H 2 and Emin=3 TeV, although the rate of EAS accompaning such gamma-families decreases rapidly as their sizes decrease.

Classification of heavy metal ions present in multi-frequency multi-electrode potable water data using evolutionary algorithm

NASA Astrophysics Data System (ADS)

Karkra, Rashmi; Kumar, Prashant; Bansod, Baban K. S.; Bagchi, Sudeshna; Sharma, Pooja; Krishna, C. Rama

2017-11-01

Access to potable water for the common people is one of the most challenging tasks in the present era. Contamination of drinking water has become a serious problem due to various anthropogenic and geogenic events. The paper demonstrates the application of evolutionary algorithms, viz., particle swan optimization and genetic algorithm to 24 water samples containing eight different heavy metal ions (Cd, Cu, Co, Pb, Zn, Ar, Cr and Ni) for the optimal estimation of electrode and frequency to classify the heavy metal ions. The work has been carried out on multi-variate data, viz., single electrode multi-frequency, single frequency multi-electrode and multi-frequency multi-electrode water samples. The electrodes used are platinum, gold, silver nanoparticles and glassy carbon electrodes. Various hazardous metal ions present in the water samples have been optimally classified and validated by the application of Davis Bouldin index. Such studies are useful in the segregation of hazardous heavy metal ions found in water resources, thereby quantifying the degree of water quality.

Markov-modulated Markov chains and the covarion process of molecular evolution.

PubMed

Galtier, N; Jean-Marie, A

2004-01-01

The covarion (or site specific rate variation, SSRV) process of biological sequence evolution is a process by which the evolutionary rate of a nucleotide/amino acid/codon position can change in time. In this paper, we introduce time-continuous, space-discrete, Markov-modulated Markov chains as a model for representing SSRV processes, generalizing existing theory to any model of rate change. We propose a fast algorithm for diagonalizing the generator matrix of relevant Markov-modulated Markov processes. This algorithm makes phylogeny likelihood calculation tractable even for a large number of rate classes and a large number of states, so that SSRV models become applicable to amino acid or codon sequence datasets. Using this algorithm, we investigate the accuracy of the discrete approximation to the Gamma distribution of evolutionary rates, widely used in molecular phylogeny. We show that a relatively large number of classes is required to achieve accurate approximation of the exact likelihood when the number of analyzed sequences exceeds 20, both under the SSRV and among site rate variation (ASRV) models.

Evolutionary design of a generalized polynomial neural network for modelling sediment transport in clean pipes

NASA Astrophysics Data System (ADS)

Ebtehaj, Isa; Bonakdari, Hossein; Khoshbin, Fatemeh

2016-10-01

To determine the minimum velocity required to prevent sedimentation, six different models were proposed to estimate the densimetric Froude number (Fr). The dimensionless parameters of the models were applied along with a combination of the group method of data handling (GMDH) and the multi-target genetic algorithm. Therefore, an evolutionary design of the generalized GMDH was developed using a genetic algorithm with a specific coding scheme so as not to restrict connectivity configurations to abutting layers only. In addition, a new preserving mechanism by the multi-target genetic algorithm was utilized for the Pareto optimization of GMDH. The results indicated that the most accurate model was the one that used the volumetric concentration of sediment (CV), relative hydraulic radius (d/R), dimensionless particle number (Dgr) and overall sediment friction factor (λs) in estimating Fr. Furthermore, the comparison between the proposed method and traditional equations indicated that GMDH is more accurate than existing equations.

Multidisciplinary Multiobjective Optimal Design for Turbomachinery Using Evolutionary Algorithm

NASA Technical Reports Server (NTRS)

2005-01-01

This report summarizes Dr. Lian s efforts toward developing a robust and efficient tool for multidisciplinary and multi-objective optimal design for turbomachinery using evolutionary algorithms. This work consisted of two stages. The first stage (from July 2003 to June 2004) Dr. Lian focused on building essential capabilities required for the project. More specifically, Dr. Lian worked on two subjects: an enhanced genetic algorithm (GA) and an integrated optimization system with a GA and a surrogate model. The second stage (from July 2004 to February 2005) Dr. Lian formulated aerodynamic optimization and structural optimization into a multi-objective optimization problem and performed multidisciplinary and multi-objective optimizations on a transonic compressor blade based on the proposed model. Dr. Lian s numerical results showed that the proposed approach can effectively reduce the blade weight and increase the stage pressure ratio in an efficient manner. In addition, the new design was structurally safer than the original design. Five conference papers and three journal papers were published on this topic by Dr. Lian.

Combined Endometrial Ablation and Levonorgestrel Intrauterine System Use in Women With Dysmenorrhea and Heavy Menstrual Bleeding: Novel Approach for Challenging Cases.

PubMed

Papadakis, Efstathios P; El-Nashar, Sherif A; Laughlin-Tommaso, Shannon K; Shazly, Sherif A M; Hopkins, Matthew R; Breitkopf, Daniel M; Famuyide, Abimbola O

2015-01-01

To evaluate the feasibility and impact of levonorgestrel intrauterine system (LNG-IUS) on treatment failure after endometrial ablation (EA) in women with heavy menstrual bleeding (HMB) and dysmenorrhea at 4 years. Cohort study (Canadian Task Force II-2). An academic institution in the upper Midwest. All women with HMB and dysmenorrhea who underwent EA with combined placement of LNG-IUS (EA/LNG-IUS cohort, 23 women) after 2005 and an historic reference group from women who had EA alone (EA cohort, 65 women) from 1998 through the end of 2005. Radiofrequency EA, thermal balloon ablation, and LNG-IUS. The primary outcome was treatment failure defined as persistent pain, bleeding, and hysterectomy after EA at 4 years. The combined treatment failure outcome was documented in 2 patients (8.7%) in the EA/LNG-IUS group and 19 patients (29.2%) in the EA group with an unadjusted OR of .23 (95% CI, .05-1.08). After adjusting for known risk factors of failure, the adjusted OR was .19 (95% CI, .26-.88). None of the women who underwent EA/LNG-IUS had hysterectomy for treatment failure compared with 16 (24%) in the EA group (p = .009); postablation pelvic pain was documented in 1 woman (4.3%) in the EA/LNG-IUS group compared with 8 women (12.3%) in the EA group (p = .24). One woman in the EA/LNG-IUS group (4.3%) presented with persistent bleeding compared with 15 (23.1%) in the EA group (p = .059). Office removal of the intrauterine device was performed in 4 women with no complications. LNG-IUS insertion at the time of EA is feasible and can provide added benefit after EA in women with dysmenorrhea and HMB. Copyright © 2015 AAGL. Published by Elsevier Inc. All rights reserved.

Abnormalities in arterial-ventricular coupling in older healthy persons are attenuated by sodium nitroprusside

PubMed Central

Chantler, Paul D.; Nussbacher, Amit; Gerstenblith, Gary; Schulman, Steven P.; Becker, Lewis C.; Ferrucci, Luigi; Fleg, Jerome L.; Najjar, Samer S.

2011-01-01

The coupling between arterial elastance (EA; net afterload) and left ventricular elastance (ELV; pump performance), known as EA/ELV, is a key determinant of cardiovascular performance and shifts during exercise due to a greater increase in ELV versus EA. This normal exercise-induced reduction in EA/ELV decreases with advancing age. We hypothesized that sodium nitroprusside (SNP) can acutely ameliorate the age-associated deficits in EA/ELV. At rest and during graded exercise to exhaustion, EA was characterized as end-systolic pressure/stroke volume and ELV as end-systolic pressure/end-systolic volume. Resting EA/ELV did not differ between old (70 ± 8 yr, n = 15) and young (30 ± 5 yr, n = 17) subjects because of a tandem increase in EA and ELV in older subjects. During peak exercise, a blunted increase in ELV in old (7.8 ± 3.1 mmHg/ml) versus young (11.4 ± 6.5 mmHg/ml) subjects blunted the normal exercise-induced decline in EA/ELV in old (0.25 ± 0.11) versus young (0.16 ± 0.05) subjects. SNP administration to older subjects lowered resting EA/ELV by 31% via a reduction in EA (10%) and an increase in ELV (47%) and lowered peak exercise EA/ELV (36%) via an increase in ELV (68%) without a change in EA. Importantly, SNP attenuated the age-associated deficits in EA/ELV and ELV during exercise, and at peak exercise EA/ELV in older subjects on drug administration did not differ from young subjects without drug administration. In conclusion, some age-associated deficiencies in EA/ELV, EA, and ELV, in older subjects can be acutely abolished by SNP infusion. This is relevant to common conditions in older subjects associated with a significant impairment of exercise performance such as frailty or heart failure with preserved ejection fraction. PMID:21378146

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation.

PubMed

Zeng, Jia; Hannenhalli, Sridhar

2013-01-01

Gene duplication, followed by functional evolution of duplicate genes, is a primary engine of evolutionary innovation. In turn, gene expression evolution is a critical component of overall functional evolution of paralogs. Inferring evolutionary history of gene expression among paralogs is therefore a problem of considerable interest. It also represents significant challenges. The standard approaches of evolutionary reconstruction assume that at an internal node of the duplication tree, the two duplicates evolve independently. However, because of various selection pressures functional evolution of the two paralogs may be coupled. The coupling of paralog evolution corresponds to three major fates of gene duplicates: subfunctionalization (SF), conserved function (CF) or neofunctionalization (NF). Quantitative analysis of these fates is of great interest and clearly influences evolutionary inference of expression. These two interrelated problems of inferring gene expression and evolutionary fates of gene duplicates have not been studied together previously and motivate the present study. Here we propose a novel probabilistic framework and algorithm to simultaneously infer (i) ancestral gene expression and (ii) the likely fate (SF, NF, CF) at each duplication event during the evolution of gene family. Using tissue-specific gene expression data, we develop a nonparametric belief propagation (NBP) algorithm to predict the ancestral expression level as a proxy for function, and describe a novel probabilistic model that relates the predicted and known expression levels to the possible evolutionary fates. We validate our model using simulation and then apply it to a genome-wide set of gene duplicates in human. Our results suggest that SF tends to be more frequent at the earlier stage of gene family expansion, while NF occurs more frequently later on.

The Human Behavioral Ecology of Contemporary World Issues : Applications to Public Policy and International Development.

PubMed

Tucker, Bram; Rende Taylor, Lisa

2007-09-01

Human behavioral ecology (HBE) began as an attempt to explain human economic, reproductive, and social behavior using neodarwinian theory in concert with theory from ecology and economics, and ethnographic methods. HBE has addressed subsistence decision-making, cooperation, life history trade-offs, parental investment, mate choice, and marriage strategies among hunter-gatherers, herders, peasants, and wage earners in rural and urban settings throughout the world. Despite our rich insights into human behavior, HBE has very rarely been used as a tool to help the people with whom we work. This article introduces a special issue of Human Nature which explores the application of HBE to significant world issues through the design and critique of public policy and international development projects. The articles by Tucker, Shenk, Leonetti et al., and Neil were presented at the 104th annual meeting of the American Anthropological Association (AAA) in Washington, D.C., in December 2005, in the first organized session of the nascent Evolutionary Anthropology Section (EAS). We conclude this introduction by summarizing some theoretical challenges to applying HBE, and ways in which evolutionary anthropologists can contribute to solving tough world issues.

[A comparative study of effects of electroacupuncture with different stimulation parameters on medicine-induced abortion].

PubMed

Ma, Liang-xiao; Yang, Fang; Zhu, Jiang; He, Zhi-ping; Chen, Yan; Xu, Hong-yan; Liu, Yu-qi; Chen, Yin-ying

2008-07-01

To observe the effect of electroacupuncture (EA) with different stimulation parameters on medicine-induced abortion. One hundred and nine cases of early pregnancy who asked medicine-induced abortion were allocated to an EA group A (n = 37), an EA group B (n = 38) and a medication group (n = 34). Within 30-60 min after oral administration of Misoprostol, in the EA group A, EA was given at bilateral Hegu (LI 4) and Sanyinjiao (SP 6) with cluster waves of 100 Hz and in the EA group B, EA was given at Hegu (LI 4) for 20 min and then at Sanyinjiao (SP 6) for 5 min with continuous waves of 50 Hz. EA was not given to the medication group. The complete abortion rate, duration of eliminating embryonic sac, colporrhagia lasting time and abdominal pain condition were recorded. The complete abortion rate was 91.9% in the EA group A and 86.8% in the EA group B, which were higher than 82.4% in the medication group, with no significant differences between the 3 groups (P>0.05); the duration of eliminating embryonic sac and the colporrhagia lasting time in the two EA groups were significantly shorter than those in the medication group (P<0.05, P<0.01); alleviation of abdominal pain in the EA group B was better than the medication group (P<0.01) and the EA group A (P<0.05). Different stimulation parameters of EA have different effects on abortion.

Repetitive Electroacupuncture Attenuates Cold-Induced Hypertension through Enkephalin in the Rostral Ventral Lateral Medulla.

PubMed

Li, Min; Tjen-A-Looi, Stephanie C; Guo, Zhi-Ling; Longhurst, John C

2016-10-24

Acupuncture lowers blood pressure (BP) in hypertension, but mechanisms underlying its action are unclear. To simulate clinical studies, we performed electroacupuncture (EA) in unanesthetized rats with cold-induced hypertension (CIH) induced by six weeks of cold exposure (6 °C). EA (0.1 - 0.4 mA, 2 Hz) was applied at ST36-37 acupoints overlying the deep peroneal nerve for 30 min twice weekly for five weeks while sham-EA was conducted with the same procedures as EA except for no electrical stimulation. Elevated BP was reduced after six sessions of EA treatment and remained low 72 hrs after EA in 18 CIH rats, but not in sham-EA (n = 12) and untreated (n = 6) CIH ones. The mRNA level of preproenkephalin in the rostral ventrolateral medulla (rVLM) 72 hr after EA was increased (n = 9), compared to the sham-EA (n = 6), untreated CIH rats (n = 6) and normotensive control animals (n = 6). Microinjection of ICI 174,864, a δ-opioid receptor antagonist, into the rVLM of EA-treated CIH rats partially reversed EA's effect on elevated BP (n = 4). Stimulation of rVLM of CIH rats treated with sham-EA using a δ-opioid agonist, DADLE, decreased BP (n = 6). These data suggest that increased enkephalin in the rVLM induced by repetitive EA contributes to BP lowering action of EA.

The effects of opioid receptor antagonists on electroacupuncture-produced anti-allodynia/hyperalgesia in rats with paclitaxel-evoked peripheral neuropathy.

PubMed

Meng, Xianze; Zhang, Yu; Li, Aihui; Xin, Jiajia; Lao, Lixing; Ren, Ke; Berman, Brian M; Tan, Ming; Zhang, Rui-Xin

2011-09-26

Research supports the effectiveness of acupuncture for conditions such as chronic low back and knee pain. In a five-patient pilot study the modality also improved the symptoms of chemotherapy-induced neuropathic pain. Using an established rat model of paclitaxel-induced peripheral neuropathy, we evaluated the effect of electroacupuncture (EA) on paclitaxel-induced hyperalgesia and allodynia that has not been studied in an animal model. We hypothesize that EA would relieve the paclitaxel-induced mechanical allodynia and hyperalgesia, which was assessed 30 min after EA using von Frey filaments. Beginning on day 13, the response frequency to von Frey filaments (4-15 g) was significantly increased in paclitaxel-injected rats compared to those injected with vehicle. EA at 10 Hz significantly (P<0.05) decreased response frequency at 4-15 g compared to sham EA; EA at 100 Hz only decreased response frequency at 15 g stimulation. Compared to sham EA plus vehicle, EA at 10 Hz plus either a μ, δ, or κ opioid receptor antagonist did not significantly decrease mechanical response frequency, indicating that all three antagonists blocked EA inhibition of allodynia and hyperalgesia. Since we previously demonstrated that μ and δ but not κ opioid receptors affect EA anti-hyperalgesia in an inflammatory pain model, these data show that EA inhibits pain through different opioid receptors under varying conditions. Our data indicate that EA at 10 Hz inhibits mechanical allodynia/hyperalgesia more potently than does EA at 100 Hz. Thus, EA significantly inhibits paclitaxel-induced allodynia/hyperalgesia through spinal opioid receptors, and EA may be a useful complementary treatment for neuropathic pain patients. Copyright © 2011 Elsevier B.V. All rights reserved.

Faint Debris Detection by Particle Based Track-Before-Detect Method

NASA Astrophysics Data System (ADS)

Uetsuhara, M.; Ikoma, N.

2014-09-01

This study proposes a particle method to detect faint debris, which is hardly seen in single frame, from an image sequence based on the concept of track-before-detect (TBD). The most widely used detection method is detect-before-track (DBT), which firstly detects signals of targets from single frame by distinguishing difference of intensity between foreground and background then associate the signals for each target between frames. DBT is capable of tracking bright targets but limited. DBT is necessary to consider presence of false signals and is difficult to recover from false association. On the other hand, TBD methods try to track targets without explicitly detecting the signals followed by evaluation of goodness of each track and obtaining detection results. TBD has an advantage over DBT in detecting weak signals around background level in single frame. However, conventional TBD methods for debris detection apply brute-force search over candidate tracks then manually select true one from the candidates. To reduce those significant drawbacks of brute-force search and not-fully automated process, this study proposes a faint debris detection algorithm by a particle based TBD method consisting of sequential update of target state and heuristic search of initial state. The state consists of position, velocity direction and magnitude, and size of debris over the image at a single frame. The sequential update process is implemented by a particle filter (PF). PF is an optimal filtering technique that requires initial distribution of target state as a prior knowledge. An evolutional algorithm (EA) is utilized to search the initial distribution. The EA iteratively applies propagation and likelihood evaluation of particles for the same image sequences and resulting set of particles is used as an initial distribution of PF. This paper describes the algorithm of the proposed faint debris detection method. The algorithm demonstrates performance on image sequences acquired during observation campaigns dedicated to GEO breakup fragments, which would contain a sufficient number of faint debris images. The results indicate the proposed method is capable of tracking faint debris with moderate computational costs at operational level.

User Instructions for the EPIC-2 Code.

DTIC Science & Technology

1986-09-01

10 1 TAM IIFAILIDARAC EFAIL 5 MATERIAL CARDS FOR SOLIDS INPUT DATA L45,5X, FSO, A48. R(8FDO.OJ, MATL I WAR I iAIL "EFAILMAtEA :SCRIPT ION DENSITY SPH...failure of the elements must be achieved by the eroding interface algorithm, it is important that EFAIL (a mate- rial property) be much greater than ERODE...If left blank (DFRAC z 0) factor will be set to DFRAC = 1.0 EFAIL = Equivalent plastic strain (true) which, if exceeded, will totally fail the element

Analgesic and physiological effect of electroacupuncture combined with epidural lidocaine in goats.

PubMed

Cui, Lu-Ying; Guo, Ni-Ni; Li, Yu-Lin; Li, Meng; Ding, Ming-Xing

2017-07-01

To investigate physiological and antinociceptive effects of electroacupuncture (EA) with lidocaine epidural nerve block in goats. Prospective experimental trial. Forty-eight hybrid male goats weighing 27 ± 2 kg. The goats were randomly assigned to six groups: L2.2, epidural lidocaine (2.2 mg kg -1 ); L4.4, epidural lidocaine (4.4 mg kg -1 ); EA; EA-L1.1, EA with epidural lidocaine (1.1 mg kg -1 ); EA-L2.2, EA with epidural lidocaine (2.2 mg kg -1 ); and EA-L4.4, EA with epidural lidocaine (4.4 mg kg -1 ). EA was administered for 120 minutes. Epidural lidocaine was administered 25 minutes after EA started. Nociceptive thresholds of flank and thigh regions, abdominal muscle tone, mean arterial pressure (MAP), heart rate (HR), respiratory frequency (f R ) and rectal temperature were recorded at 30, 60, 90, 120, 150 and 180 minutes. Lidocaine dose-dependently increased nociceptive thresholds. There were no differences in nociceptive thresholds between L4.4 and EA from 30 to 120 minutes. The threshold in EA-L2.2 was lower than in EA-L4.4 from 30 to 120 minutes, but higher than in EA-L1.1 from 30 to 150 minutes or in L4.4 from 30 to 180 minutes. The abdominal muscle tone in EA-L2.2 was higher at 30 minutes, but lower at 90 and 120 minutes than at 0 minutes. There were no differences in muscle tone between L4.4 and L2.2 or EA-L4.4, and between any two of the three EA-lidocaine groups from 0 to 180 minutes. The f R and HR decreased in L4.4 at 60 and 90 minutes compared with 0 minutes. No differences in f R , HR, MAP and temperature among the groups occurred from 30 to 180 minutes. EA combined with 2.2 mg kg -1 epidural lidocaine provides better antinociceptive effect than 4.4 mg kg -1 epidural lidocaine alone in goats. EA provided antinociception and allowed a decrease in epidural lidocaine dose. Copyright © 2017 Association of Veterinary Anaesthetists and American College of Veterinary Anesthesia and Analgesia. Published by Elsevier Ltd. All rights reserved.

47 CFR 11.32 - EAS Encoder.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 1 2011-10-01 2011-10-01 false EAS Encoder. 11.32 Section 11.32....32 EAS Encoder. (a) EAS Encoders must at a minimum be capable of encoding the EAS protocol described... must additionally provide the following minimum specifications: (1) Encoder programming. Access to...

Understanding the relationships between self-esteem, experiential avoidance, and paranoia: structural equation modelling and experience sampling studies.

PubMed

Udachina, Alisa; Thewissen, Viviane; Myin-Germeys, Inez; Fitzpatrick, Sam; O'kane, Aisling; Bentall, Richard P

2009-09-01

Hypothesized relationships between experiential avoidance (EA), self-esteem, and paranoia were tested using structural equation modeling in a sample of student participants (N = 427). EA in everyday life was also investigated using the Experience Sampling Method in a subsample of students scoring high (N = 17) and low (N = 15) on paranoia. Results showed that paranoid students had lower self-esteem and reported higher levels of EA than nonparanoid participants. The interactive influence of EA and stress predicted negative self-esteem: EA was particularly damaging at high levels of stress. Greater EA and higher social stress independently predicted lower positive self-esteem. Low positive self-esteem predicted engagement in EA. A direct association between EA and paranoia was also found. These results suggest that similar mechanisms may underlie EA and thought suppression. Although people may employ EA to regulate self-esteem, this strategy is maladaptive as it damages self-esteem, incurs cognitive costs, and fosters paranoid thinking.

Artificial immune algorithm for multi-depot vehicle scheduling problems

NASA Astrophysics Data System (ADS)

Wu, Zhongyi; Wang, Donggen; Xia, Linyuan; Chen, Xiaoling

2008-10-01

In the fast-developing logistics and supply chain management fields, one of the key problems in the decision support system is that how to arrange, for a lot of customers and suppliers, the supplier-to-customer assignment and produce a detailed supply schedule under a set of constraints. Solutions to the multi-depot vehicle scheduling problems (MDVRP) help in solving this problem in case of transportation applications. The objective of the MDVSP is to minimize the total distance covered by all vehicles, which can be considered as delivery costs or time consumption. The MDVSP is one of nondeterministic polynomial-time hard (NP-hard) problem which cannot be solved to optimality within polynomial bounded computational time. Many different approaches have been developed to tackle MDVSP, such as exact algorithm (EA), one-stage approach (OSA), two-phase heuristic method (TPHM), tabu search algorithm (TSA), genetic algorithm (GA) and hierarchical multiplex structure (HIMS). Most of the methods mentioned above are time consuming and have high risk to result in local optimum. In this paper, a new search algorithm is proposed to solve MDVSP based on Artificial Immune Systems (AIS), which are inspirited by vertebrate immune systems. The proposed AIS algorithm is tested with 30 customers and 6 vehicles located in 3 depots. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving MDVSP problems.

ITO-based evolutionary algorithm to solve traveling salesman problem

NASA Astrophysics Data System (ADS)

Dong, Wenyong; Sheng, Kang; Yang, Chuanhua; Yi, Yunfei

2014-03-01

In this paper, a ITO algorithm inspired by ITO stochastic process is proposed for Traveling Salesmen Problems (TSP), so far, many meta-heuristic methods have been successfully applied to TSP, however, as a member of them, ITO needs further demonstration for TSP. So starting from designing the key operators, which include the move operator, wave operator, etc, the method based on ITO for TSP is presented, and moreover, the ITO algorithm performance under different parameter sets and the maintenance of population diversity information are also studied.

Discovering new materials and new phenomena with evolutionary algorithms

NASA Astrophysics Data System (ADS)

Oganov, Artem

Thanks to powerful evolutionary algorithms, in particular the USPEX method, it is now possible to predict both the stable compounds and their crystal structures at arbitrary conditions, given just the set of chemical elements. Recent developments include major increases of efficiency and extensions to low-dimensional systems and molecular crystals (which allowed large structures to be handled easily, e.g. Mg(BH4)2 and H2O-H2) and new techniques called evolutionary metadynamics and Mendelevian search. Some of the results that I will discuss include: 1. Theoretical and experimental evidence for a new partially ionic phase of boron, γ-B and an insulating and optically transparent form of sodium. 2. Predicted stability of ``impossible'' chemical compounds that become stable under pressure - e.g. Na3Cl, Na2Cl, Na3Cl2, NaCl3, NaCl7, Mg3O2 and MgO2. 3. Novel surface phases (e.g. boron surface reconstructions). 4. Novel dielectric polymers, and novel permanent magnets confirmed by experiment and ready for applications. 5. Prediction of new ultrahard materials and computational proof that diamond is the hardest possible material.

Optimal GENCO bidding strategy

NASA Astrophysics Data System (ADS)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

A Parallel Genetic Algorithm for Automated Electronic Circuit Design

NASA Technical Reports Server (NTRS)

Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)

2000-01-01

We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discovery.

PubMed

Orzechowski, Patryk; Sipper, Moshe; Huang, Xiuzhen; Moore, Jason H

2018-05-22

Biclustering algorithms are commonly used for gene expression data analysis. However, accurate identification of meaningful structures is very challenging and state-of-the-art methods are incapable of discovering with high accuracy different patterns of high biological relevance. In this paper a novel biclustering algorithm based on evolutionary computation, a subfield of artificial intelligence (AI), is introduced. The method called EBIC aims to detect order-preserving patterns in complex data. EBIC is capable of discovering multiple complex patterns with unprecedented accuracy in real gene expression datasets. It is also one of the very few biclustering methods designed for parallel environments with multiple graphics processing units (GPUs). We demonstrate that EBIC greatly outperforms state-of-the-art biclustering methods, in terms of recovery and relevance, on both synthetic and genetic datasets. EBIC also yields results over 12 times faster than the most accurate reference algorithms. EBIC source code is available on GitHub at https://github.com/EpistasisLab/ebic. Correspondence and requests for materials should be addressed to P.O. (email: patryk.orzechowski@gmail.com) and J.H.M. (email: jhmoore@upenn.edu). Supplementary Data with results of analyses and additional information on the method is available at Bioinformatics online.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

1/f Noise in the Simple Genetic Algorithm Applied to a Traveling Salesman Problem

NASA Astrophysics Data System (ADS)

Yamada, Mitsuhiro

Complex dynamical systems are observed in physics, biology, and even economics. Such systems in balance are considered to be in a critical state, and 1/f noise is considered to be a footprint. Complex dynamical systems have also been investigated in the field of evolutionary algorithms inspired by biological evolution. The genetic algorithm (GA) is a well-known evolutionary algorithm in which many individuals interact, and the simplest GA is referred to as the simple GA (SGA). However, the GA has not been examined from the viewpoint of the emergence of 1/f noise. In the present paper, the SGA is applied to a traveling salesman problem in order to investigate the SGA from such a viewpoint. The timecourses of the fitness of the candidate solution were examined. As a result, when the mutation and crossover probabilities were optimal, the system evolved toward a critical state in which the average maximum fitness over all trial runs was maximum. In this situation, the fluctuation of the fitness of the candidate solution resulted in the 1/f power spectrum, and the dynamics of the system had no intrinsic time or length scale.

Evolutionary Technologies: Fundamentals and Applications to Information/Communication Systems and Manufacturing/Logistics Systems

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Kawakami, Hiroshi; Tsujimura, Yasuhiro; Handa, Hisashi; Lin, Lin; Okamoto, Azuma

As efficient utilization of computational resources is increasing, evolutionary technology based on the Genetic Algorithm (GA), Genetic Programming (GP), Evolution Strategy (ES) and other Evolutionary Computations (ECs) is making rapid progress, and its social recognition and the need as applied technology are increasing. This is explained by the facts that EC offers higher robustness for knowledge information processing systems, intelligent production and logistics systems, most advanced production scheduling and other various real-world problems compared to the approaches based on conventional theories, and EC ensures flexible applicability and usefulness for any unknown system environment even in a case where accurate mathematical modeling fails in the formulation. In this paper, we provide a comprehensive survey of the current state-of-the-art in the fundamentals and applications of evolutionary technologies.

The effect of acupuncture needle combination on central pain processing-an fMRI study

PubMed Central

2014-01-01

Background Empirical acupuncture treatment paradigm for acute pain utilizing Tendinomuscular Meridians (TMM) calls for the stimulation of Ting Points (TPs) and Gathering point(GP). This study aims to compare the supraspinal neuronal mechanisms associated with both TPs and GP needling (EA3), and TPs needling alone (EA2) with fMRI. Results A significant (P < 0.01) difference between pre-scan (heat Pain) HP, and post-EA HP VAS scores in both paradigms was noted (n = 11). The post-EA HP VAS score was significantly (P < 0.05) lower with EA3 comparing to EA2 Within-group random effect analysis indicated that EA3+HP>EA3 (condition EA3+HP subtracted by condition EA3) appeared to exert a significant degree of activity suppression in the affective supraspinal regions including the IPL, anterior cingulate cortex (ACC) and the insular cortex (IN). This level of suppression was not observed in the EA2+HP>EA2 (condition EA2+HP subtracted by condition EA2) within-group random effect analysis Between-group random effect analysis indicated that EA3 induced a significantly (P < 0.01, cluster size threshold 150) higher degree of deactivation than EA2 in several pain related supraspinal regions including the right prefrontal cortex, rostral anterior cingulate (rACC), medial cingulate cortex, left inferior frontal lobe and posterior cerebellum. The 2-factor ANOVA in those regions indicated both rACC and posterior cerebellum had a significant (P < 0.01) needle effect, and the right prefrontal area showed a significant (P < 0.01) HP effect. However, a significant interaction between the two factors was only found in the right prefrontal lobe. Granger causality analysis showed EA3 induced a much higher degree of inference among HP related supraspinal somatosensory, affective and modulatory components than EA2. Deactivation pattern at the medullary-pontine area casted a direct inference on the deactivation pattern of secondary somatosensory cortices which also affected the deactivation of the IN. Conclusions While both EA2 and EA3 induced a significant degree of deactivation in the human brain regions related to pain processing, the addition of GP stimulation further exerts an inhibitory effect on the ascending spinoreticular pain pathway. Therefore, different needling position as mandated in different empirical acupuncture treatment paradigms may play a different role in modulating pain related neuronal functions. PMID:24667015

Evolution with Reinforcement Learning in Negotiation

PubMed Central

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm. PMID:25048108

Evolution with reinforcement learning in negotiation.

PubMed

Zou, Yi; Zhan, Wenjie; Shao, Yuan

2014-01-01

Adaptive behavior depends less on the details of the negotiation process and makes more robust predictions in the long term as compared to in the short term. However, the extant literature on population dynamics for behavior adjustment has only examined the current situation. To offset this limitation, we propose a synergy of evolutionary algorithm and reinforcement learning to investigate long-term collective performance and strategy evolution. The model adopts reinforcement learning with a tradeoff between historical and current information to make decisions when the strategies of agents evolve through repeated interactions. The results demonstrate that the strategies in populations converge to stable states, and the agents gradually form steady negotiation habits. Agents that adopt reinforcement learning perform better in payoff, fairness, and stableness than their counterparts using classic evolutionary algorithm.

A conceptual evolutionary aseismic decision support framework for hospitals

NASA Astrophysics Data System (ADS)

Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

2012-12-01

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

Quantitative property-structural relation modeling on polymeric dielectric materials

NASA Astrophysics Data System (ADS)

Wu, Ke

Nowadays, polymeric materials have attracted more and more attention in dielectric applications. But searching for a material with desired properties is still largely based on trial and error. To facilitate the development of new polymeric materials, heuristic models built using the Quantitative Structure Property Relationships (QSPR) techniques can provide reliable "working solutions". In this thesis, the application of QSPR on polymeric materials is studied from two angles: descriptors and algorithms. A novel set of descriptors, called infinite chain descriptors (ICD), are developed to encode the chemical features of pure polymers. ICD is designed to eliminate the uncertainty of polymer conformations and inconsistency of molecular representation of polymers. Models for the dielectric constant, band gap, dielectric loss tangent and glass transition temperatures of organic polymers are built with high prediction accuracy. Two new algorithms, the physics-enlightened learning method (PELM) and multi-mechanism detection, are designed to deal with two typical challenges in material QSPR. PELM is a meta-algorithm that utilizes the classic physical theory as guidance to construct the candidate learning function. It shows better out-of-domain prediction accuracy compared to the classic machine learning algorithm (support vector machine). Multi-mechanism detection is built based on a cluster-weighted mixing model similar to a Gaussian mixture model. The idea is to separate the data into subsets where each subset can be modeled by a much simpler model. The case study on glass transition temperature shows that this method can provide better overall prediction accuracy even though less data is available for each subset model. In addition, the techniques developed in this work are also applied to polymer nanocomposites (PNC). PNC are new materials with outstanding dielectric properties. As a key factor in determining the dispersion state of nanoparticles in the polymer matrix, the surface tension components of polymers are modeled using ICD. Compared to the 3D surface descriptors used in a previous study, the model with ICD has a much improved prediction accuracy and stability particularly for the polar component. In predicting the enhancement effect of grafting functional groups on the breakdown strength of PNC, a simple local charge transfer model is proposed where the electron affinity (EA) and ionization energy (IE) determines the main charge trap depth in the system. This physical model is supported by first principle computation. QSPR models for EA and IE are also built, decreasing the computation time of EA and IE for a single molecule from several hours to less than one second. Furthermore, the designs of two web-based tools are introduced. The tools represent two commonly used applications for QSPR studies: data inquiry and prediction. Making models and data public available and easy to use is particularly crucial for QSPR research. The web tools described in this work should provide a good guidance and starting point for the further development of information tools enabling more efficient cooperation between computational and experimental communities.

Managing changes in the enterprise architecture modelling context

NASA Astrophysics Data System (ADS)

Khanh Dam, Hoa; Lê, Lam-Son; Ghose, Aditya

2016-07-01

Enterprise architecture (EA) models the whole enterprise in various aspects regarding both business processes and information technology resources. As the organisation grows, the architecture of its systems and processes must also evolve to meet the demands of the business environment. Evolving an EA model may involve making changes to various components across different levels of the EA. As a result, an important issue before making a change to an EA model is assessing the ripple effect of the change, i.e. change impact analysis. Another critical issue is change propagation: given a set of primary changes that have been made to the EA model, what additional secondary changes are needed to maintain consistency across multiple levels of the EA. There has been however limited work on supporting the maintenance and evolution of EA models. This article proposes an EA description language, namely ChangeAwareHierarchicalEA, integrated with an evolution framework to support both change impact analysis and change propagation within an EA model. The core part of our framework is a technique for computing the impact of a change and a new method for generating interactive repair plans from Alloy consistency rules that constrain the EA model.

Experiential avoidance in the vulnerability to depression among adolescent females.

PubMed

Mellick, William; Vanwoerden, Salome; Sharp, Carla

2017-01-15

Although various mechanisms in the maternal transmission of Major Depressive Disorder (MDD) have been investigated, it is unknown whether experiential avoidance (EA) is a vulnerability factor in the development of depression or a consequence of the illness. The present study utilized a high-risk design to determine if EA indeed poses vulnerability to adolescent MDD. Secondly, we examined the means by which adolescent EA may come to pose vulnerability, namely that it explains the relation between maternal EA and adolescent depressive symptoms. One-hundred and forty-six biological mother/adolescent daughter dyads comprised three diagnostic groups: mothers with a history of MDD and their depressed daughters (MDD; n=21), mothers with a history of MDD and their never-depressed daughters (high-risk, HR; n=69), and healthy controls (HCs; n=56). Groups differed on daughter EA such that the MDD group reported greater EA than the HR group, which in turn reported greater EA than HCs. Daughter EA mediated the relation between maternal EA and daughter depressive symptoms after controlling for maternal depressive symptoms. Strengths aside, this study included a relatively small group of depressed mother-daughter dyads and relied on cross-sectional self-report data. EA appears to serve as a vulnerability factor for adolescent MDD, and the mechanistic role of daughter EA highlights the significance of intergenerational EA in the maternal transmission of depression. Therapeutic approaches may therefore consider reducing the transmission of EA from mothers to daughters. Copyright © 2016 Elsevier B.V. All rights reserved.

Repetitive Electroacupuncture Attenuates Cold-Induced Hypertension through Enkephalin in the Rostral Ventral Lateral Medulla

PubMed Central

Li, Min; Tjen-A-Looi, Stephanie C.; Guo, Zhi-Ling; Longhurst, John C.

2016-01-01

Acupuncture lowers blood pressure (BP) in hypertension, but mechanisms underlying its action are unclear. To simulate clinical studies, we performed electroacupuncture (EA) in unanesthetized rats with cold-induced hypertension (CIH) induced by six weeks of cold exposure (6 °C). EA (0.1 – 0.4 mA, 2 Hz) was applied at ST36-37 acupoints overlying the deep peroneal nerve for 30 min twice weekly for five weeks while sham-EA was conducted with the same procedures as EA except for no electrical stimulation. Elevated BP was reduced after six sessions of EA treatment and remained low 72 hrs after EA in 18 CIH rats, but not in sham-EA (n = 12) and untreated (n = 6) CIH ones. The mRNA level of preproenkephalin in the rostral ventrolateral medulla (rVLM) 72 hr after EA was increased (n = 9), compared to the sham-EA (n = 6), untreated CIH rats (n = 6) and normotensive control animals (n = 6). Microinjection of ICI 174,864, a δ-opioid receptor antagonist, into the rVLM of EA-treated CIH rats partially reversed EA’s effect on elevated BP (n = 4). Stimulation of rVLM of CIH rats treated with sham-EA using a δ-opioid agonist, DADLE, decreased BP (n = 6). These data suggest that increased enkephalin in the rVLM induced by repetitive EA contributes to BP lowering action of EA. PMID:27775047

Lower extremity energy absorption and biomechanics during landing, part II: frontal-plane energy analyses and interplanar relationships.

PubMed

Norcross, Marc F; Lewek, Michael D; Padua, Darin A; Shultz, Sandra J; Weinhold, Paul S; Blackburn, J Troy

2013-01-01

Greater sagittal-plane energy absorption (EA) during the initial impact phase (INI) of landing is consistent with sagittal-plane biomechanics that likely increase anterior cruciate ligament (ACL) loading, but it does not appear to influence frontal-plane biomechanics. We do not know whether frontal-plane INI EA is related to high-risk frontal-plane biomechanics. To compare biomechanics among INI EA groups, determine if women are represented more in the high group, and evaluate interplanar INI EA relationships. Descriptive laboratory study. Research laboratory. Participants included 82 (41 men, 41 women; age = 21.0 ± 2.4 years, height = 1.74 ± 0.10 m, mass = 70.3 ± 16.1 kg) healthy, physically active volunteers. We assessed landing biomechanics with an electromagnetic motion-capture system and force plate. We calculated frontal- and sagittal-plane total, hip, knee, and ankle INI EA. Total frontal-plane INI EA was used to create high, moderate, and low tertiles. Frontal-plane knee and hip kinematics, peak vertical and posterior ground reaction forces, and peak internal knee-varus moment (pKVM) were identified and compared across groups using 1-way analyses of variance. We used a χ (2) analysis to evaluate male and female allocation to INI EA groups. We used simple, bivariate Pearson product moment correlations to assess interplanar INI EA relationships. The high-INI EA group exhibited greater knee valgus at ground contact, hip adduction at pKVM, and peak hip adduction than the low-INI EA group (P < .05) and greater peak knee valgus, pKVM, and knee valgus at pKVM than the moderate- (P < .05) and low- (P < .05) INI EA groups. Women were more likely than men to be in the high-INI EA group (χ(2) = 4.909, P = .03). Sagittal-plane knee and frontal-plane hip INI EA (r = 0.301, P = .006) and sagittal-plane and frontal-plane ankle INI EA were associated (r = 0.224, P = .04). No other interplanar INI EA relationships were found (P > .05). Greater frontal-plane INI EA was associated with less favorable frontal-plane biomechanics that likely result in greater ACL loading. Women were more likely than men to use greater frontal-plane INI EA. The magnitudes of sagittal- and frontal-plane INI EA were largely independent.

Electroacupuncture is not effective in chronic painful neuropathies.

PubMed

Penza, Paola; Bricchi, Monica; Scola, Amalia; Campanella, Angela; Lauria, Giuseppe

2011-12-01

  To investigate the analgesic efficacy of electroacupuncture (EA) in patients with chronic painful neuropathy.   Double-blind, placebo-controlled, cross-over study. Inclusion criteria were diagnosis of peripheral neuropathy, neuropathic pain (visual analog scale > 4) for at least 6 months, and stable analgesic medications for at least 3 months.   Sixteen patients were randomized into two arms to be treated with EA or pseudo-EA (placebo).   The protocol included 6 weeks of treatment, 12 weeks free of treatment, and then further 6 weeks of treatment. EA or pseudo-EA was performed weekly during each treatment period.   The primary outcome was the number of patients treated with EA achieving at least 50% of pain relief at the end of each treatment compared with pain intensity at baseline. Secondary outcomes were modification in patient's global impression of change, depression and anxiety, and quality of life.   Eleven patients were randomized to EA and five patients to pseudo-EA as the first treatment. Only one patient per group (EA and pseudo-EA) reported 50% of pain relief at the end of each treatment compared with pain intensity at baseline. Pain intensity did not differ between EA (5.7 ± 2.3 at baseline and 4.97 ± 3.23 after treatment) and pseudo-EA (4.9 ± 1.9 at baseline and 4.18 ± 2.69 after treatment). There was no difference between patients who received EA as the first treatment and patients initially treated with placebo. There was no change in the secondary outcomes.   Our results do not support the use of EA in this population of painful neuropathy patients. Further studies in larger groups of patients are warranted to confirm our observation. Wiley Periodicals, Inc.

Enhanced anticancer activity and oral bioavailability of ellagic acid through encapsulation in biodegradable polymeric nanoparticles.

PubMed

Mady, Fatma M; Shaker, Mohamed A

2017-01-01

Despite the fact that various studies have investigated the clinical relevance of ellagic acid (EA) as a naturally existing bioactive substance in cancer therapy, little has been reported regarding the efficient strategy for improving its oral bioavailability. In this study, we report the formulation of EA-loaded nanoparticles (EA-NPs) to find a way to enhance its bioactivity as well as bioavailability after oral administration. Poly(ε-caprolactone) (PCL) was selected as the biodegradable polymer for the formulation of EA-NPs through the emulsion-diffusion-evaporation technique. The obtained NPs have been characterized by measuring particle size, zeta potential, Fourier transform infrared, differential scanning calorimetry, and X-ray diffraction. The entrapment efficiency and the release profile of EA was also determined. In vitro cellular uptake and cytotoxicity of the obtained NPs were evaluated using Caco-2 and HCT-116 cell lines, respectively. Moreover, in vivo study has been performed to measure the oral bioavailability of EA-NPs compared to free EA, using New Zealand white rabbits. NPs with distinct shape were obtained with high entrapment and loading efficiencies. Diffusion-driven release profile of EA from the prepared NPs was determined. EA-NP-treated HCT-116 cells showed relatively lower cell viability compared to free EA-treated cells. Fluorometric imaging revealed the cellular uptake and efficient localization of EA-NPs in the nuclear region of Caco-2 cells. In vivo testing revealed that the oral administration of EA-NPs produced a 3.6 times increase in the area under the curve compared to that of EA. From these results, it can be concluded that incorporation of EA into PCL as NPs enhances its oral bioavailability and activity.

Enhanced anticancer activity and oral bioavailability of ellagic acid through encapsulation in biodegradable polymeric nanoparticles

PubMed Central

Mady, Fatma M; Shaker, Mohamed A

2017-01-01

Despite the fact that various studies have investigated the clinical relevance of ellagic acid (EA) as a naturally existing bioactive substance in cancer therapy, little has been reported regarding the efficient strategy for improving its oral bioavailability. In this study, we report the formulation of EA-loaded nanoparticles (EA-NPs) to find a way to enhance its bioactivity as well as bioavailability after oral administration. Poly(ε-caprolactone) (PCL) was selected as the biodegradable polymer for the formulation of EA-NPs through the emulsion–diffusion–evaporation technique. The obtained NPs have been characterized by measuring particle size, zeta potential, Fourier transform infrared, differential scanning calorimetry, and X-ray diffraction. The entrapment efficiency and the release profile of EA was also determined. In vitro cellular uptake and cytotoxicity of the obtained NPs were evaluated using Caco-2 and HCT-116 cell lines, respectively. Moreover, in vivo study has been performed to measure the oral bioavailability of EA-NPs compared to free EA, using New Zealand white rabbits. NPs with distinct shape were obtained with high entrapment and loading efficiencies. Diffusion-driven release profile of EA from the prepared NPs was determined. EA-NP-treated HCT-116 cells showed relatively lower cell viability compared to free EA-treated cells. Fluorometric imaging revealed the cellular uptake and efficient localization of EA-NPs in the nuclear region of Caco-2 cells. In vivo testing revealed that the oral administration of EA-NPs produced a 3.6 times increase in the area under the curve compared to that of EA. From these results, it can be concluded that incorporation of EA into PCL as NPs enhances its oral bioavailability and activity. PMID:29066891

Effects of Different Electroacupuncture Scheduling Regimens on Murine Bone Tumor-Induced Hyperalgesia: Sex Differences and Role of Inflammation

PubMed Central

Smeester, Branden A.; Al-Gizawiy, Mona; Beitz, Alvin J.

2012-01-01

Previous studies have shown that electroacupuncture (EA) is able to reduce hyperalgesia in rodent models of persistent pain, but very little is known about the analgesic effects and potential sex differences of different EA treatment regimens. In the present study, we examined the effects of five different EA treatments on tumor-induced hyperalgesia in male and female mice. EA applied to the ST-36 acupoint either twice weekly (EA-2X/3) beginning on postimplantation day (PID) 3 or prophylactically three times prior to implantation produced the most robust and longest lasting antinociceptive effects. EA treatment given once per week beginning at PID 7 only produced an antinociceptive effect in female animals. The analgesic effect of EA-2X/3 began earlier in males, but lasted longer in females indicating sex differences in EA. We further demonstrate that EA-2X/3 elicits a marked decrease in tumor-associated inflammation as evidenced by a significant reduction in tumor-associated neutrophils at PID 7. Moreover, EA-2X/3 produced a significant reduction in tumor-associated PGE2 as measured in microperfusate samples. Collectively, these data provide evidence that EA-2X/3 treatment reduces tumor-induced hyperalgesia, which is associated with a decrease in tumor-associated inflammation and PGE2 concentration at the tumor site suggesting possible mechanisms by which EA reduces tumor nociception. PMID:23320035

Echinocystic acid inhibits RANKL-induced osteoclastogenesis by regulating NF-κB and ERK signaling pathways

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Jian-hui, E-mail: jianhui_yangxa@163.com; Li, Bing; Wu, Qiong

Receptor activator of nuclear factor-κB ligand (RANKL) is a key factor in the differentiation and activation of osteoclasts. Echinocystic acid (EA), a pentacyclic triterpene isolated from the fruits of Gleditsia sinensis Lam, was reported to prevent reduction of bone mass and strength and improve the cancellous bone structure and biochemical properties in ovariectomy rats. However, the molecular mechanism of EA on the osteoclast formation has not been reported. The purpose of this study was to investigate the effects and mechanism of EA on RANKL-induced osteoclastogenesis. Our results showed that EA inhibited the formation of osteoclast, as well as the expressionmore » of osteoclastogenesis-related marker proteins in bone marrow macrophages (BMMs). At molecular levels, EA inhibited RANKL-induced NF-κB activation and ERK phosphorylation in BMMs. In conclusion, the present study demonstrated that EA can suppress osteoclastogenesis in vitro. Moreover, we clarified that these inhibitory effects of EA occur through suppression of NF-κB and ERK activation. Therefore, EA may be a potential agent in the treatment of osteoclast-related diseases such as osteoporosis. - Highlights: • EA inhibited the formation of osteoclast in BMMs. • EA inhibits the expression of osteoclastogenesis-related marker proteins in BMMs. • EA inhibits RANKL-induced NF-κB activation in BMMs. • EA inhibits RANKL-induced ERK phosphorylation in BMMs.« less

An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure.

PubMed

Waldispühl, Jérôme; Ponty, Yann

2011-11-01

The analysis of the relationship between sequences and structures (i.e., how mutations affect structures and reciprocally how structures influence mutations) is essential to decipher the principles driving molecular evolution, to infer the origins of genetic diseases, and to develop bioengineering applications such as the design of artificial molecules. Because their structures can be predicted from the sequence data only, RNA molecules provide a good framework to study this sequence-structure relationship. We recently introduced a suite of algorithms called RNAmutants which allows a complete exploration of RNA sequence-structure maps in polynomial time and space. Formally, RNAmutants takes an input sequence (or seed) to compute the Boltzmann-weighted ensembles of mutants with exactly k mutations, and sample mutations from these ensembles. However, this approach suffers from major limitations. Indeed, since the Boltzmann probabilities of the mutations depend of the free energy of the structures, RNAmutants has difficulties to sample mutant sequences with low G+C-contents. In this article, we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by classical algorithms. We applied these methods to sample mutations with low G+C-contents. These adaptive sampling techniques can be easily adapted to explore other regions of the sequence and structural landscapes which are difficult to sample. Importantly, these algorithms come at a minimal computational cost. We demonstrate the insights offered by these techniques on studies of complete RNA sequence structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-content has a strong influence on the size and shape of the evolutionary accessible sequence and structural spaces. In particular, we show that low G+C-contents favor the apparition of internal loops and thus possibly the synthesis of tertiary structure motifs. On the other hand, high G+C-contents significantly reduce the size of the evolutionary accessible mutational landscapes.

47 CFR 11.51 - EAS code and Attention Signal Transmission requirements.

Code of Federal Regulations, 2012 CFR

2012-10-01

... Message (EOM) codes using the EAS Protocol. The Attention Signal must precede any emergency audio message... audio messages. No Attention Signal is required for EAS messages that do not contain audio programming... EAS messages in the main audio channel. All DAB stations shall also transmit EAS messages on all audio...

47 CFR 11.51 - EAS code and Attention Signal Transmission requirements.

Code of Federal Regulations, 2014 CFR

2014-10-01

... Message (EOM) codes using the EAS Protocol. The Attention Signal must precede any emergency audio message... audio messages. No Attention Signal is required for EAS messages that do not contain audio programming... EAS messages in the main audio channel. All DAB stations shall also transmit EAS messages on all audio...

47 CFR 11.51 - EAS code and Attention Signal Transmission requirements.

Code of Federal Regulations, 2013 CFR

2013-10-01

... Message (EOM) codes using the EAS Protocol. The Attention Signal must precede any emergency audio message... audio messages. No Attention Signal is required for EAS messages that do not contain audio programming... EAS messages in the main audio channel. All DAB stations shall also transmit EAS messages on all audio...

47 CFR 11.34 - Acceptability of the equipment.

Code of Federal Regulations, 2011 CFR

2011-10-01

...) Equipment Requirements § 11.34 Acceptability of the equipment. (a) An EAS Encoder used for generating the...) The functions of the EAS decoder, Attention Signal generator and receiver, and the EAS encoder... information on how to install, operate and program an EAS Encoder, EAS Decoder, or combined unit and a list of...

47 CFR 11.34 - Acceptability of the equipment.

Code of Federal Regulations, 2010 CFR

2010-10-01

...) Equipment Requirements § 11.34 Acceptability of the equipment. (a) An EAS Encoder used for generating the...) The functions of the EAS decoder, Attention Signal generator and receiver, and the EAS encoder... information on how to install, operate and program an EAS Encoder, EAS Decoder, or combined unit and a list of...

47 CFR 11.52 - EAS code and Attention Signal Monitoring requirements.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false EAS code and Attention Signal Monitoring... SYSTEM (EAS) Emergency Operations § 11.52 EAS code and Attention Signal Monitoring requirements. (a) EAS Participants must be capable of receiving the Attention Signal required by § 11.32(a)(9) and emergency messages...

77 FR 1676 - EasTrans, LLC; Notice Granting Extension of Time

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-11

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. PR10-30-002] EasTrans, LLC; Notice Granting Extension of Time On December 16, 2011, EasTrans, LLC (EasTrans) filed a request to... 20, 2010). Upon consideration, notice is hereby given that an extension of time for EasTrans to file...

TPS In-Flight Health Monitoring Project Progress Report

NASA Technical Reports Server (NTRS)

Kostyk, Chris; Richards, Lance; Hudston, Larry; Prosser, William

2007-01-01

Progress in the development of new thermal protection systems (TPS) is reported. New approaches use embedded lightweight, sensitive, fiber optic strain and temperature sensors within the TPS. Goals of the program are to develop and demonstrate a prototype TPS health monitoring system, develop a thermal-based damage detection algorithm, characterize limits of sensor/system performance, and develop ea methodology transferable to new designs of TPS health monitoring systems. Tasks completed during the project helped establish confidence in understanding of both test setup and the model and validated system/sensor performance in a simple TPS structure. Other progress included complete initial system testing, commencement of the algorithm development effort, generation of a damaged thermal response characteristics database, initial development of a test plan for integration testing of proven FBG sensors in simple TPS structure, and development of partnerships to apply the technology.

Derivation of hydrous pyrolysis kinetic parameters from open-system pyrolysis

NASA Astrophysics Data System (ADS)

Tseng, Yu-Hsin; Huang, Wuu-Liang

2010-05-01

Kinetic information is essential to predict the temperature, timing or depth of hydrocarbon generation within a hydrocarbon system. The most common experiments for deriving kinetic parameters are mainly by open-system pyrolysis. However, it has been shown that the conditions of open-system pyrolysis are deviant from nature by its low near-ambient pressure and high temperatures. Also, the extrapolation of heating rates in open-system pyrolysis to geological conditions may be questionable. Recent study of Lewan and Ruble shows hydrous-pyrolysis conditions can simulate the natural conditions better and its applications are supported by two case studies with natural thermal-burial histories. Nevertheless, performing hydrous pyrolysis experiment is really tedious and requires large amount of sample, while open-system pyrolysis is rather convenient and efficient. Therefore, the present study aims at the derivation of convincing distributed hydrous pyrolysis Ea with only routine open-system Rock-Eval data. Our results unveil that there is a good correlation between open-system Rock-Eval parameter Tmax and the activation energy (Ea) derived from hydrous pyrolysis. The hydrous pyrolysis single Ea can be predicted from Tmax based on the correlation, while the frequency factor (A0) is estimated based on the linear relationship between single Ea and log A0. Because the Ea distribution is more rational than single Ea, we modify the predicted single hydrous pyrolysis Ea into distributed Ea by shifting the pattern of Ea distribution from open-system pyrolysis until the weight mean Ea distribution equals to the single hydrous pyrolysis Ea. Moreover, it has been shown that the shape of the Ea distribution is very much alike the shape of Tmax curve. Thus, in case of the absence of open-system Ea distribution, we may use the shape of Tmax curve to get the distributed hydrous pyrolysis Ea. The study offers a new approach as a simple method for obtaining distributed hydrous pyrolysis Ea with only routine open-system Rock-Eval data, which will allow for better estimating hydrocarbon generation.

Biodegradable in situ gelling system for subcutaneous administration of ellagic acid and ellagic acid loaded nanoparticles: evaluation of their antioxidant potential against cyclosporine induced nephrotoxicity in rats.

PubMed

Sharma, G; Italia, J L; Sonaje, K; Tikoo, K; Ravi Kumar, M N V

2007-03-12

Ellagic acid (EA) is a potent antioxidant marketed as a nutritional supplement. Its pharmacological activity has been reported in wide variety of disease models; however its use has been limited owing to its poor biopharmaceutical properties, thereby poor bioavailability. The objective of the current study was to develop chitosan-glycerol phosphate (C-GP) in situ gelling system for sustained delivery of ellagic acid (EA) via subcutaneous route. EA was incorporated in the system employing propylene glycol (PG) and triethanolamine (TEA) as co-solvents; on the other hand EA loaded PLGA nanoparticles (np) were dispersed in the gelling system using water. These in situ gelling systems were thoroughly characterized for mechanical, rheological and swelling properties. These systems are liquid at room temperature and gels at 37 degrees C. The EA C-GP system showed an initial burst release in vitro with about 85% drug released in 12 h followed by a steady release till 160 h, on the other hand EA nanoparticles entrapped in the C-GP system displayed sustained release till 360 h. The histopathological analysis indicates the absence of inflammation on administration, suggesting that these formulations are safe during the studied period. Furthermore, the antioxidant potential of EA C-GP and EA np C-GP gels has been evaluated against cyclosporine induced nephrotoxicity in rats. The data indicates that formulations were effective against cyclosporine induced nephrotoxicity, where the EA C-GP gels showed activity at 10 times lower dose and the EA np C-GP gels at 150 times lower dose when compared to orally given EA. Formulating nanoparticles of EA and incorporating them in C-GP system results in 15 times lowering of dose in comparison EA C-GP gels which is quite significant. Together, these results indicate that the bioavailability of ellagic acid can be improved by subcutaneous formulations administered as simple EA or EA nps.

An evolutionary computation based algorithm for calculating solar differential rotation by automatic tracking of coronal bright points

NASA Astrophysics Data System (ADS)

Shahamatnia, Ehsan; Dorotovič, Ivan; Fonseca, Jose M.; Ribeiro, Rita A.

2016-03-01

Developing specialized software tools is essential to support studies of solar activity evolution. With new space missions such as Solar Dynamics Observatory (SDO), solar images are being produced in unprecedented volumes. To capitalize on that huge data availability, the scientific community needs a new generation of software tools for automatic and efficient data processing. In this paper a prototype of a modular framework for solar feature detection, characterization, and tracking is presented. To develop an efficient system capable of automatic solar feature tracking and measuring, a hybrid approach combining specialized image processing, evolutionary optimization, and soft computing algorithms is being followed. The specialized hybrid algorithm for tracking solar features allows automatic feature tracking while gathering characterization details about the tracked features. The hybrid algorithm takes advantages of the snake model, a specialized image processing algorithm widely used in applications such as boundary delineation, image segmentation, and object tracking. Further, it exploits the flexibility and efficiency of Particle Swarm Optimization (PSO), a stochastic population based optimization algorithm. PSO has been used successfully in a wide range of applications including combinatorial optimization, control, clustering, robotics, scheduling, and image processing and video analysis applications. The proposed tool, denoted PSO-Snake model, was already successfully tested in other works for tracking sunspots and coronal bright points. In this work, we discuss the application of the PSO-Snake algorithm for calculating the sidereal rotational angular velocity of the solar corona. To validate the results we compare them with published manual results performed by an expert.

Comparing genomes with rearrangements and segmental duplications.

PubMed

Shao, Mingfu; Moret, Bernard M E

2015-06-15

Large-scale evolutionary events such as genomic rearrange.ments and segmental duplications form an important part of the evolution of genomes and are widely studied from both biological and computational perspectives. A basic computational problem is to infer these events in the evolutionary history for given modern genomes, a task for which many algorithms have been proposed under various constraints. Algorithms that can handle both rearrangements and content-modifying events such as duplications and losses remain few and limited in their applicability. We study the comparison of two genomes under a model including general rearrangements (through double-cut-and-join) and segmental duplications. We formulate the comparison as an optimization problem and describe an exact algorithm to solve it by using an integer linear program. We also devise a sufficient condition and an efficient algorithm to identify optimal substructures, which can simplify the problem while preserving optimality. Using the optimal substructures with the integer linear program (ILP) formulation yields a practical and exact algorithm to solve the problem. We then apply our algorithm to assign in-paralogs and orthologs (a necessary step in handling duplications) and compare its performance with that of the state-of-the-art method MSOAR, using both simulations and real data. On simulated datasets, our method outperforms MSOAR by a significant margin, and on five well-annotated species, MSOAR achieves high accuracy, yet our method performs slightly better on each of the 10 pairwise comparisons. http://lcbb.epfl.ch/softwares/coser. © The Author 2015. Published by Oxford University Press.

A Business Case Analysis on the Feasibility of Recapturing Inpatient Obstetrical Services for Naval Hospital Beaufort

DTIC Science & Technology

2006-06-14

F0220 Chair, Conference 1 EA $765 $765 F0250 Chair, Arm, Lounge Type 2 EA $460 $920 F0295 Chair, Stacking, 34 X 21 X 24 1 EA $87 $87 F0465 Cabinet...Patient Transport, Folding 1 EA $1,561 $1,561 STAFF LOUNGE 1 A5075 Dispenser, Soap, Disposable 1 EA $18 $18 A6046 Artwork, Decorative, With Frame 1 EA...Percentage 0% 0% 0% 0% 0% Staff Expense for E &T

The HGF Receptor c-Met Is Overexpressed in Esophageal Adenocarcinoma1

PubMed Central

Herrera, Luis J; El-Hefnawy, Talal; Queiroz de Oliveira, Pierre E; Raja, Siva; Finkelstein, Sydney; Gooding, William; Luketich, James D; Godfrey, Tony E; Hughes, Steven J

2005-01-01

Abstract The hepatocyte growth factor (HGF) receptor, Met, has established oncogenic properties; however, its expression and function in esophageal adenocarcinoma (EA) remain poorly understood. We aimed to determine the expression and potential alterations in Met expression in EA. Met expression was investigated in surgical specimens of EA, Barrett's esophagus (BE), and normal esophagus (NE) using immunohistochemistry (IHC) and quantitative reverse transcriptase polymerase chain reaction. Met expression, phosphorylation, and the effect of COX-2 inhibition on expression were examined in EA cell lines. IHC demonstrated intense Met immunoreactivity in all (100%) EA and dysplastic BE specimens. In contrast, minimal immunostaining was observed in BE without dysplasia or NE specimens. Met mRNA and protein levels were increased in three EA cell lines, and Met protein was phosphorylated in the absence of serum. Sequence analysis found the kinase domain of c-met to be wild type in all three EA cell lines. HGF mRNA expression was identified in two EA cell lines. In COX-2-overexpressing cells, COX-2 inhibition decreased Met expression. Met is consistently overexpressed in EA surgical specimens and in three EA cell lines. Met dysregulation occurs early in Barrett's dysplasia to adenocarcinoma sequence. Future study of Met inhibition as a potential biologic therapy for EA is warranted. PMID:15720819

The influences of sex and posture on joint energetics during drop landings.

PubMed

Norcross, M F; Shultz, S J; Weinhold, P S; Lewek, M D; Padua, D A; Blackburn, J T

2015-04-01

Previous observations suggest that females utilize a more erect initial landing posture than males with sex differences in landing posture possibly related to sex-specific energy absorption (EA) strategies. However, sex-specific EA strategies have only been observed when accompanied by sex differences in initial landing posture. This study (a) investigated the potential existence of sex-specific EA strategies; and (b) determined the influences of sex and initial landing posture on the biomechanical determinants of EA. The landing biomechanics of 80 subjects were recorded during drop landings in Preferred, Flexed, and Erect conditions. No sex differences in joint EA were identified after controlling for initial landing posture. Males and females exhibited greater ankle EA during Erect vs Flexed landings with this increase driven by 12% greater ankle velocity, but no change in ankle extensor moment. No differences in hip and knee EA were observed between conditions. However, to achieve similar knee EA, subjects used 7% greater mean knee extensor moment but 9% less knee angular velocity during Flexed landings. The results suggest that sex-specific EA strategies do not exist, and that the magnitude of knee joint EA can be maintained by modulating the relative contributions of joint moment and angular velocity to EA. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

The Nature of Nurture: Using a Virtual-Parent Design to Test Parenting Effects on Children's Educational Attainment in Genotyped Families.

PubMed

Bates, Timothy C; Maher, Brion S; Medland, Sarah E; McAloney, Kerrie; Wright, Margaret J; Hansell, Narelle K; Kendler, Kenneth S; Martin, Nicholas G; Gillespie, Nathan A

2018-04-01

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.

Chemicals having estrogenic activity can be released from some bisphenol A-free, hard and clear, thermoplastic resins.

PubMed

Bittner, George D; Denison, Michael S; Yang, Chun Z; Stoner, Matthew A; He, Guochun

2014-12-04

Chemicals that have estrogenic activity (EA) can potentially cause adverse health effects in mammals including humans, sometimes at low doses in fetal through juvenile stages with effects detected in adults. Polycarbonate (PC) thermoplastic resins made from bisphenol A (BPA), a chemical that has EA, are now often avoided in products used by babies. Other BPA-free thermoplastic resins, some hypothesized or advertised to be EA-free, are replacing PC resins used to make reusable hard and clear thermoplastic products such as baby bottles. We used two very sensitive and accurate in vitro assays (MCF-7 and BG1Luc human cell lines) to quantify the EA of chemicals leached into ethanol or water/saline extracts of fourteen unstressed or stressed (autoclaving, microwaving, UV radiation) thermoplastic resins. Estrogen receptor (ER)-dependent agonist responses were confirmed by their inhibition with the ER antagonist ICI 182,780. Our data showed that some (4/14) unstressed and stressed BPA-free thermoplastic resins leached chemicals having significant levels of EA, including one polystyrene (PS), and three Tritan™ resins, the latter reportedly EA-free. Exposure to UV radiation in natural sunlight resulted in an increased release of EA from Tritan™ resins. Triphenyl-phosphate (TPP), an additive used to manufacture some thermoplastic resins such as Tritan™, exhibited EA in both MCF-7 and BG1Luc assays. Ten unstressed or stressed glycol-modified polyethylene terephthalate (PETG), cyclic olefin polymer (COP) or copolymer (COC) thermoplastic resins did not release chemicals with detectable EA under any test condition. This hazard survey study assessed the release of chemicals exhibiting EA as detected by two sensitive, widely used and accepted, human cell line in vitro assays. Four PC replacement resins (Tritan™ and PS) released chemicals having EA. However, ten other PC-replacement resins did not leach chemicals having EA (EA-free-resins). These results indicate that PC-replacement plastic products could be made from EA-free resins (if appropriate EA-free additives are chosen) that maintain advantages of re-usable plastic items (price, weight, shatter resistance) without releasing chemicals having EA that potentially produce adverse health effects on current or future generations.

47 CFR 90.761 - EA and Regional licenses.

Code of Federal Regulations, 2010 CFR

2010-10-01

... Policies Governing the Licensing and Use of Phase II Ea, Regional and Nationwide Systems § 90.761 EA and Regional licenses. (a) EA licenses for spectrum blocks listed in Table 2 of § 90.721(b) are available in... 47 Telecommunication 5 2010-10-01 2010-10-01 false EA and Regional licenses. 90.761 Section 90.761...

47 CFR 90.761 - EA and Regional licenses.

Code of Federal Regulations, 2011 CFR

2011-10-01

... Policies Governing the Licensing and Use of Phase II Ea, Regional and Nationwide Systems § 90.761 EA and Regional licenses. (a) EA licenses for spectrum blocks listed in Table 2 of § 90.721(b) are available in... 47 Telecommunication 5 2011-10-01 2011-10-01 false EA and Regional licenses. 90.761 Section 90.761...

47 CFR 90.359 - Field strength limits for EA-licensed LMS systems.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 5 2011-10-01 2011-10-01 false Field strength limits for EA-licensed LMS... § 90.359 Field strength limits for EA-licensed LMS systems. EA-licensed multilateration systems shall limit the field strength of signals transmitted from their base stations to 47 dBuV/m at their EA...

7 CFR 1794.23 - Proposals normally requiring an EA.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 12 2013-01-01 2013-01-01 false Proposals normally requiring an EA. 1794.23 Section... § 1794.23 Proposals normally requiring an EA. RUS will normally prepare an EA for all proposed actions... require an EA and shall be subject to the requirements of §§ 1794.40 through 1794.44. (a) General...

7 CFR 1794.23 - Proposals normally requiring an EA.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 12 2014-01-01 2013-01-01 true Proposals normally requiring an EA. 1794.23 Section... § 1794.23 Proposals normally requiring an EA. RUS will normally prepare an EA for all proposed actions... require an EA and shall be subject to the requirements of §§ 1794.40 through 1794.44. (a) General...

7 CFR 1794.23 - Proposals normally requiring an EA.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 12 2012-01-01 2012-01-01 false Proposals normally requiring an EA. 1794.23 Section... § 1794.23 Proposals normally requiring an EA. RUS will normally prepare an EA for all proposed actions... require an EA and shall be subject to the requirements of §§ 1794.40 through 1794.44. (a) General...

A Comprehensive Review of Swarm Optimization Algorithms

PubMed Central

2015-01-01

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches. PMID:25992655

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

PubMed

Wang, Rui; Zhou, Yongquan; Zhao, Chengyan; Wu, Haizhou

2015-01-01

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Devices That May Interfere with Pacemakers

MedlinePlus

... Devices with risk Anti-theft systems (also called electronic article surveillance or EAS): Interactions with EAS systems ... the pulse generator Anti-theft systems (also called electronic article surveillance or EAS): Interactions with EAS systems ...

Erosive arthritis in systemic lupus erythematosus: analysis of a distinct clinical and serological subset.

PubMed

Richter Cohen, M; Steiner, G; Smolen, J S; Isenberg, D A

1998-04-01

Erosive arthritis (EA) in systemic lupus erythematosus (SLE) can be debilitating and deforming with uncertain factors for risk, although antibodies to the A2 hnRNP core protein, known as anti-RA33, have been associated with EA. Two hundred patients under long-term follow-up for SLE were evaluated for EA and associated clinical and serological abnormalities. In addition, sera were tested in a masked fashion for anti-RA33 antibodies in a total of 60 patients: 10 with EA and 50 age-, sex- and ethnically matched controls. Ten of 200 (5%) patients with SLE, mainly non-white women, had EA. There were trends for increased renal involvement (P = 0.06), Sjögren's syndrome (P = 0.07) and Raynaud's phenomenon (P = 0.03) in patients with EA compared to those without EA. Rheumatoid factor (RF) was increased in patients with EA (P < 0.02), as were antibodies to double-stranded DNA (P < 0.05), Sm (P < 0.01) and La/SS-B (P < 0.001). Anti-RA33 antibodies were present in 70% with EA compared to 28% without EA (P < 0.05). RF correlated with anti-RA33 antibodies in patients with EA, but not with the presence of anti-RA33 alone. Thus, anti-RA33 antibodies may identify those patients with SLE who are at risk for EA, and an association with RF suggests a common immune response or pathological mechanism in autoimmune erosive joint disease.

Effect of Electroacupuncture on Rats with Chronic Constriction Injury-Induced Neuropathic Pain

PubMed Central

Tang, Nou-Ying; Lin, Yi-Wen; Li, Tsai-Chung; Liu, Hsu-Jan

2014-01-01

We adopt the chronic constriction injury (CCI) model to induce neuropathic pain to Spragrue-Dawley (SD) rats by ligating the right sciatic nerve of using four 4-0 chromic gut sutures and subsequently applying 2 and 15 Hz electroacupuncture (EA), respectively, to the right (ipsilateral) Zusanli (St-36) and Shangjuxu (St-37) acupoints. The results of this study are summarized as follows: (1) the differences in withdrawal latencies for the radiant heat test and total lift leg counts for the cold plate test (4°C) of the control (i.e., non-EA) and sham groups were greater than those of the 2 Hz EA (2EA) and 15 Hz EA (15EA) groups; (2) the von Frey test filament gram counts of the control and sham groups were less than those of the 2EA and 15EA groups on the 6th, 7th, 8th, 11th, 12th, and 13th day following ligation; and (3) the 2EA and 15EA groups exhibited reduced cerebral transient receptor potential vanilloid type 4 (TRPV4) expressions, although we did not observe a similar effect for cerebral TRPV1 or spinal TRPV4/TRPV1 expressions. These findings show that 2 and 15 Hz EA can reduce CCI-induced neuropathic pain, which indicates that various spinal segmental and gate effects have a crucial function in pain reduction. The relationship between EA and TRPV4/TRPV1 expression requires further study. PMID:24605047

Lower Extremity Energy Absorption and Biomechanics During Landing, Part II: Frontal-Plane Energy Analyses and Interplanar Relationships

PubMed Central

Norcross, Marc F.; Lewek, Michael D.; Padua, Darin A.; Shultz, Sandra J.; Weinhold, Paul S.; Blackburn, J. Troy

2013-01-01

Context: Greater sagittal-plane energy absorption (EA) during the initial impact phase (INI) of landing is consistent with sagittal-plane biomechanics that likely increase anterior cruciate ligament (ACL) loading, but it does not appear to influence frontal-plane biomechanics. We do not know whether frontal-plane INI EA is related to high-risk frontal-plane biomechanics. Objective: To compare biomechanics among INI EA groups, determine if women are represented more in the high group, and evaluate interplanar INI EA relationships. Design: Descriptive laboratory study. Setting: Research laboratory. Patients or Other Participants: Participants included 82 (41 men, 41 women; age = 21.0 ± 2.4 years, height = 1.74 ± 0.10 m, mass = 70.3 ± 16.1 kg) healthy, physically active volunteers. Intervention(s): We assessed landing biomechanics with an electromagnetic motion-capture system and force plate. Main Outcome Measure(s): We calculated frontal- and sagittal-plane total, hip, knee, and ankle INI EA. Total frontal-plane INI EA was used to create high, moderate, and low tertiles. Frontal-plane knee and hip kinematics, peak vertical and posterior ground reaction forces, and peak internal knee-varus moment (pKVM) were identified and compared across groups using 1-way analyses of variance. We used a χ2 analysis to evaluate male and female allocation to INI EA groups. We used simple, bivariate Pearson product moment correlations to assess interplanar INI EA relationships. Results: The high–INI EA group exhibited greater knee valgus at ground contact, hip adduction at pKVM, and peak hip adduction than the low–INI EA group (P < .05) and greater peak knee valgus, pKVM, and knee valgus at pKVM than the moderate– (P < .05) and low– (P < .05) INI EA groups. Women were more likely than men to be in the high–INI EA group (χ2 = 4.909, P = .03). Sagittal-plane knee and frontal-plane hip INI EA (r = 0.301, P = .006) and sagittal-plane and frontal-plane ankle INI EA were associated (r = 0.224, P = .04). No other interplanar INI EA relationships were found (P > .05). Conclusions: Greater frontal-plane INI EA was associated with less favorable frontal-plane biomechanics that likely result in greater ACL loading. Women were more likely than men to use greater frontal-plane INI EA. The magnitudes of sagittal- and frontal-plane INI EA were largely independent. PMID:23944381

A hybrid method for evaluating enterprise architecture implementation.

PubMed

Nikpay, Fatemeh; Ahmad, Rodina; Yin Kia, Chiam

2017-02-01

Enterprise Architecture (EA) implementation evaluation provides a set of methods and practices for evaluating the EA implementation artefacts within an EA implementation project. There are insufficient practices in existing EA evaluation models in terms of considering all EA functions and processes, using structured methods in developing EA implementation, employing matured practices, and using appropriate metrics to achieve proper evaluation. The aim of this research is to develop a hybrid evaluation method that supports achieving the objectives of EA implementation. To attain this aim, the first step is to identify EA implementation evaluation practices. To this end, a Systematic Literature Review (SLR) was conducted. Second, the proposed hybrid method was developed based on the foundation and information extracted from the SLR, semi-structured interviews with EA practitioners, program theory evaluation and Information Systems (ISs) evaluation. Finally, the proposed method was validated by means of a case study and expert reviews. This research provides a suitable foundation for researchers who wish to extend and continue this research topic with further analysis and exploration, and for practitioners who would like to employ an effective and lightweight evaluation method for EA projects. Copyright © 2016 Elsevier Ltd. All rights reserved.

The effect of electroacupuncture and tramadol on experimental tourniquet pain.

PubMed

Musial, Frauke; Choi, Kyung-Eun; Gabriel, Tim; Lüdtke, Rainer; Rampp, Thomas; Michalsen, Andreas; Dobos, Gustav

2012-03-01

The hypoalgesic effect of electroacupuncture (EA) was directly compared with the analgesic effect of pharmacological interventions using the submaximum effort tourniquet technique (SETT). 125 healthy subjects (mean age 24.44±4.46 years; 62.4% female, 37.6% male) performed SETT at baseline and under one of five experimental conditions (n=25 per group): EA (2 Hz with burst pulses in alternating one-phase-square wave pulses; burst length 180 μs, burst frequency 80 Hz, stimulation time/pulse width 3 s), tramadol (50 mg), ibuprofen (400 mg), placebo pill or non-treatment control. EA was performed at LI4 and LI10 contralaterally with stimulation beginning 20 min before SETT and lasting throughout SETT. The pharmacological interventions were given in a double-blind design 1 h before the SETT assessment. Subjects showed a hypoalgesic effect of the opiate and of the EA for subjective pain rating (EA p=0.0051; tramadol p=0.0299), and pain tolerance index (time/rating) (EA p=0.043; tramadol p=0.047) analysed using analysis of covariance. More subjects reached the strict time limit of 30 min (analysed by logistic regression and adjusted OR as a post-hoc analysis) under EA compared with most other experimental conditions. Only EA and tramadol were not significantly different (95% Wald confidence limits: non-treatment control vs EA 0.011 to 0.542; placebo pill vs EA 0.009 to 0.438; ibuprofen vs EA 0.021 to 0.766; tramadol vs EA 0.065 to 1.436). In a laboratory setting, an EA procedure was as effective as a single dose of an orally administered opiate in reducing experimentally induced ischaemic pain.

47 CFR 11.51 - EAS code and Attention Signal Transmission requirements.

Code of Federal Regulations, 2011 CFR

2011-10-01

... transmitting requirements contained in this section for the combined stations or systems with one EAS Encoder... the encoder. (2) Manual interrupt of programming and transmission of EAS messages may be used. EAS...

Quantum Mechanical Calculations of Monoxides of Silicon Carbide Molecules

DTIC Science & Technology

2003-03-01

Data for CO Final Energy Charge Mult Basis Set (hart) EA (eV) ZPE (hart) EA (eV) w/ ZPE 0 1 DVZ -112.6850703739 2.02121 -1 2 DVZ...Energy Charge Mult Basis Set (hart) EA (eV) ZPE (hart) EA (eV) w/ ZPE 0 1 DVZ -363.7341927429 0.617643 -1 2 DVZ -363.7114852831 0 3 DVZ...Input Geometry Output Geometry Basis Set Final Energy (hart) EA (eV) ZPE (hart) EA (eV) w/ ZPE -1 2 O-C-Si Linear O-C-Si Linear DZV -401.5363

On the determination of the depth of EAS development maximum using the lateral distribution of Cerenkov light at distances 150 m from EAS axis

NASA Technical Reports Server (NTRS)

Aliev, N.; Alimov, T.; Kakhkharov, M.; Makhmudov, B. M.; Rakhimova, N.; Tashpulatov, R.; Kalmykov, N. N.; Khristiansen, G. B.; Prosin, V. V.

1985-01-01

The Samarkand extensive air showers (EAS) array was used to measure the mean and individual lateral distribution functions (LDF) of EAS Cerenkov light. The analysis of the individual parameters b showed that the mean depth of EAS maximum and the variance of the depth distribution of maxima of EAS with energies of approx. 2x10 to the 15th power eV can properly be described in terms of Kaidalov-Martirosyan quark-gluon string model (QGSM).

Turbomachinery Airfoil Design Optimization Using Differential Evolution

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

An aerodynamic design optimization procedure that is based on a evolutionary algorithm known at Differential Evolution is described. Differential Evolution is a simple, fast, and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems, including highly nonlinear systems with discontinuities and multiple local optima. The method is combined with a Navier-Stokes solver that evaluates the various intermediate designs and provides inputs to the optimization procedure. An efficient constraint handling mechanism is also incorporated. Results are presented for the inverse design of a turbine airfoil from a modern jet engine. The capability of the method to search large design spaces and obtain the optimal airfoils in an automatic fashion is demonstrated. Substantial reductions in the overall computing time requirements are achieved by using the algorithm in conjunction with neural networks.

Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

NASA Astrophysics Data System (ADS)

Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin

2010-08-01

In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.

Evolutionary Initial Poses of Reduced D.O.F’s Quadruped Robot

NASA Astrophysics Data System (ADS)

Iida, Ken-Ichi; Nakata, Yoshitaka; Hira, Toshio; Kamano, Takuya; Suzuki, Takayuki

In this paper, an application of genetic algorithm for generation of evolutionary initial poses of a quadrupedal robot which reduced degrees of freedom is described. To reduce degree of freedom, each leg of the robot has a slider-crank mechanism and is driven by an actuator. Furthermore we introduced the forward movement mode and the rotating mode because the omnidirection movement should be made possible. To generate the suitable initial pose, the initial angle of four legs are coded under gray code and tuned by an estimation function in each mode with the genetic algorithm. As a result of generation, the cooperation of the legs is realized to move toward the omnidirection. The experimental results demonstrate that the proposed scheme is effective for generation of the suitable initial poses and the robot can walk smoothly with the generated patterns.

Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction.

PubMed

Chira, Camelia; Horvath, Dragos; Dumitrescu, D

2011-07-30

Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations.

PubMed

Branke, Jürgen; Hildebrandt, Torsten; Scholz-Reiter, Bernd

2015-01-01

Dispatching rules are frequently used for real-time, online scheduling in complex manufacturing systems. Design of such rules is usually done by experts in a time consuming trial-and-error process. Recently, evolutionary algorithms have been proposed to automate the design process. There are several possibilities to represent rules for this hyper-heuristic search. Because the representation determines the search neighborhood and the complexity of the rules that can be evolved, a suitable choice of representation is key for a successful evolutionary algorithm. In this paper we empirically compare three different representations, both numeric and symbolic, for automated rule design: A linear combination of attributes, a representation based on artificial neural networks, and a tree representation. Using appropriate evolutionary algorithms (CMA-ES for the neural network and linear representations, genetic programming for the tree representation), we empirically investigate the suitability of each representation in a dynamic stochastic job shop scenario. We also examine the robustness of the evolved dispatching rules against variations in the underlying job shop scenario, and visualize what the rules do, in order to get an intuitive understanding of their inner workings. Results indicate that the tree representation using an improved version of genetic programming gives the best results if many candidate rules can be evaluated, closely followed by the neural network representation that already leads to good results for small to moderate computational budgets. The linear representation is found to be competitive only for extremely small computational budgets.

Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes

PubMed Central

Duchen, Pablo; Leuenberger, Christoph; Szilágyi, Sándor M.; Harmon, Luke; Eastman, Jonathan; Schweizer, Manuel

2017-01-01

Abstract Although it is now widely accepted that the rate of phenotypic evolution may not necessarily be constant across large phylogenies, the frequency and phylogenetic position of periods of rapid evolution remain unclear. In his highly influential view of evolution, G. G. Simpson supposed that such evolutionary jumps occur when organisms transition into so-called new adaptive zones, for instance after dispersal into a new geographic area, after rapid climatic changes, or following the appearance of an evolutionary novelty. Only recently, large, accurate and well calibrated phylogenies have become available that allow testing this hypothesis directly, yet inferring evolutionary jumps remains computationally very challenging. Here, we develop a computationally highly efficient algorithm to accurately infer the rate and strength of evolutionary jumps as well as their phylogenetic location. Following previous work we model evolutionary jumps as a compound process, but introduce a novel approach to sample jump configurations that does not require matrix inversions and thus naturally scales to large trees. We then make use of this development to infer evolutionary jumps in Anolis lizards and Loriinii parrots where we find strong signal for such jumps at the basis of clades that transitioned into new adaptive zones, just as postulated by Simpson’s hypothesis. [evolutionary jump; Lévy process; phenotypic evolution; punctuated equilibrium; quantitative traits. PMID:28204787

Electroacupuncture improves neurobehavioral function and brain injury in rat model of intracerebral hemorrhage.

PubMed

Zhu, Yan; Deng, Li; Tang, Huajun; Gao, Xiaoqing; Wang, Youhua; Guo, Kan; Kong, Jiming; Yang, Chaoxian

2017-05-01

Acupuncture has been widely used as a treatment for stroke in China for a long time. Recently, studies have demonstrated that electroacupuncture (EA) can accelerate intracerebral hemorrhage (ICH)-induced angiogenesis in rats. In the present study, we investigated the effect of EA on neurobehavioral function and brain injury in ICH rats. ICH was induced by stereotactic injection of collagenase type I and heparin into the right caudate putamen. Adult ICH rats were randomly divided into the following three groups: model control group (MC), EA at non-acupoint points group (non-acupoint EA) and EA at Baihui and Dazhui acupoints group (EA). The neurobehavioral deficits of ICH rats were assessed by modified neurological severity score (mNSS) and gait analysis. The hemorrhage volume and glucose metabolism of hemorrhagic foci were detected by PET/CT. The expression levels of MBP, NSE and S100-B proteins in serum were tested by ELISA. The histopathological features were examined by haematoxylin-eosin (H&E) staining. Apoptosis-associated proteins in the perihematomal region were observed by immunohistochemistry. EA treatment significantly promoted the recovery of neurobehavioral function in ICH rats. Hemorrhage volume reduced in EA group at day 14 when compared with MC and non-acupoint EA groups. ELISA showed that the levels of MBP, NSE and S100-B in serum were all down-regulated by EA treatment. The brain tissue of ICH rat in the EA group was more intact and compact than that in the MC and non-acupoint groups. In the perihematomal regions, the expression of Bcl-2 protein increased and expressions of Caspase-3 and Bax proteins decreased in the EA group vs MC and non-acupoint EA groups. Our data suggest that EA treatment can improve neurobehavioral function and brain injury, which were likely connected with the absorption of hematoma and regulation of apoptosis-related proteins. Copyright © 2017 Elsevier Inc. All rights reserved.

Delayed brain ischemia tolerance induced by electroacupuncture pretreatment is mediated via MCP-induced protein 1

PubMed Central

2013-01-01

Background Emerging studies have demonstrated that pretreatment with electroacupuncture (EA) induces significant tolerance to focal cerebral ischemia. The present study seeks to determine the involvement of monocyte chemotactic protein-induced protein 1 (MCPIP1), a recently identified novel modulator of inflammatory reactions, in the cerebral neuroprotection conferred by EA pretreatment in the animal model of focal cerebral ischemia and to elucidate the mechanisms of EA pretreatment-induced ischemic brain tolerance. Methods Twenty-four hours after the end of the last EA pretreatment, focal cerebral ischemia was induced by middle cerebral artery occlusion (MCAO) for 90 minutes in male C57BL/6 mice and MCPIP1 knockout mice. Transcription and expression of MCPIP1 gene was monitored by qRT-PCR, Western blot and immunohistochemistry. The neurobehavioral scores, infarction volumes, proinflammatory cytokines and leukocyte infiltration in brain and NF-κB signaling were evaluated after ischemia/reperfusion. Results MCPIP1 protein and mRNA levels significantly increased specifically in mouse brain undergoing EA pretreatment. EA pretreatment significantly attenuated the infarct volume, neurological deficits, upregulation of proinflammatory cytokines and leukocyte infiltration in the brain of wild-type mice after MCAO compared with that of the non-EA group. MCPIP1-deficient mice failed to evoke EA pretreatment-induced tolerance compared with that of the control MCPIP1 knockout group without EA treatment. Furthermore, the activation of NF-κB signaling was significantly reduced in EA-pretreated wild-type mice after MCAO compared to that of the non-EA control group and MCPIP1-deficient mice failed to confer the EA pretreatment-induced inhibition of NF-κB signaling after MCAO. Conclusions Our data demonstrated that MCPIP1 deficiency caused significant lack of EA pretreatment-induced cerebral protective effects after MCAO compared with the control group and that MCPIP1 is involved in EA pretreatment-induced delayed brain ischemia tolerance. PMID:23663236

Epoxyalkane:Coenzyme M Transferase Gene Diversity and Distribution in Groundwater Samples from Chlorinated-Ethene-Contaminated Sites

PubMed Central

Liu, Xikun

2016-01-01

ABSTRACT Epoxyalkane:coenzyme M transferase (EaCoMT) plays a critical role in the aerobic biodegradation and assimilation of alkenes, including ethene, propene, and the toxic chloroethene vinyl chloride (VC). To improve our understanding of the diversity and distribution of EaCoMT genes in the environment, novel EaCoMT-specific terminal-restriction fragment length polymorphism (T-RFLP) and nested-PCR methods were developed and applied to groundwater samples from six different contaminated sites. T-RFLP analysis revealed 192 different EaCoMT T-RFs. Using clone libraries, we retrieved 139 EaCoMT gene sequences from these samples. Phylogenetic analysis revealed that a majority of the sequences (78.4%) grouped with EaCoMT genes found in VC- and ethene-assimilating Mycobacterium strains and Nocardioides sp. strain JS614. The four most-abundant T-RFs were also matched with EaCoMT clone sequences related to Mycobacterium and Nocardioides strains. The remaining EaCoMT sequences clustered within two emergent EaCoMT gene subgroups represented by sequences found in propene-assimilating Gordonia rubripertincta strain B-276 and Xanthobacter autotrophicus strain Py2. EaCoMT gene abundance was positively correlated with VC and ethene concentrations at the sites studied. IMPORTANCE The EaCoMT gene plays a critical role in assimilation of short-chain alkenes, such as ethene, VC, and propene. An improved understanding of EaCoMT gene diversity and distribution is significant to the field of bioremediation in several ways. The expansion of the EaCoMT gene database and identification of incorrectly annotated EaCoMT genes currently in the database will facilitate improved design of environmental molecular diagnostic tools and high-throughput sequencing approaches for future bioremediation studies. Our results further suggest that potentially significant aerobic VC degraders in the environment are not well represented in pure culture. Future research should aim to isolate and characterize aerobic VC-degrading bacteria from these underrepresented groups. PMID:27016563

Ellagic acid inhibits iron-mediated free radical formation

NASA Astrophysics Data System (ADS)

Dalvi, Luana T.; Moreira, Daniel C.; Andrade, Roberto; Ginani, Janini; Alonso, Antonio; Hermes-Lima, Marcelo

2017-02-01

Polyphenols are reported to have some health benefits, which are link to their antioxidant properties. In the case of ellagic acid (EA), there is evidence that it has free radical scavenger properties and that it is able to form complexes with metal ions. However, information on a possible link between the formation of iron-EA complexes and their interference in Haber-Weiss/Fenton reactions was not yet determined. Thus, the present study investigated the in vitro antioxidant mechanism of EA in a system containing ascorbate, Fe(III) and different iron ligands (EDTA, citrate and NTA). Iron-mediated oxidative degradation of 2-deoxyribose was poorly inhibited (by 12%) in the presence of EA (50 μM) and EDTA. When citrate or NTA - which form weak iron complexes - were used, the 2-deoxyribose protection increased to 89-97% and 45%, respectively. EA also presented equivalent inhibitory effects on iron-mediated oxygen uptake and ascorbyl radical formation. Spectral analyses of iron-EA complexes show that EA removes Fe(III) from EDTA within hours, and from citrate within 1 min. This difference in the rate of iron-EA complex formation may explain the antioxidant effects of EA. Furthermore, the EA antioxidant effectiveness was inversely proportional to the Fe(III) concentration, suggesting a competition with EDTA. In conclusion, the results indicate that EA may prevent in vitro free radical formation when it forms a complex with iron ions.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-06-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-03-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

Molecular evolutionary rates are not correlated with temperature and latitude in Squamata: an exception to the metabolic theory of ecology?

PubMed

Rolland, Jonathan; Loiseau, Oriane; Romiguier, Jonathan; Salamin, Nicolas

2016-05-20

The metabolic theory of ecology stipulates that molecular evolutionary rates should correlate with temperature and latitude in ectothermic organisms. Previous studies have shown that most groups of vertebrates, such as amphibians, turtles and even endothermic mammals, have higher molecular evolutionary rates in regions where temperature is high. However, the association between molecular evolutionary rates and temperature or latitude has never been tested in Squamata. We used a large dataset including the spatial distributions and environmental variables for 1,651 species of Squamata and compared the contrast of the rates of molecular evolution with the contrast of temperature and latitude between sister species. Using major axis regressions and a new algorithm to choose independent sister species pairs, we found that temperature and absolute latitude were not associated with molecular evolutionary rates. This absence of association in such a diverse ectothermic group questions the mechanisms explaining current pattern of species diversity in Squamata and challenges the presupposed universality of the metabolic theory of ecology.

Multiobjective Aerodynamic Shape Optimization Using Pareto Differential Evolution and Generalized Response Surface Metamodels

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple, fast, and robust evolutionary algorithm that has proven effective in determining the global optimum for several difficult single-objective optimization problems. The DE algorithm has been recently extended to multiobjective optimization problem by using a Pareto-based approach. In this paper, a Pareto DE algorithm is applied to multiobjective aerodynamic shape optimization problems that are characterized by computationally expensive objective function evaluations. To improve computational expensive the algorithm is coupled with generalized response surface meta-models based on artificial neural networks. Results are presented for some test optimization problems from the literature to demonstrate the capabilities of the method.

Crossover versus mutation: a comparative analysis of the evolutionary strategy of genetic algorithms applied to combinatorial optimization problems.

PubMed

Osaba, E; Carballedo, R; Diaz, F; Onieva, E; de la Iglesia, I; Perallos, A

2014-01-01

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

PubMed Central

Osaba, E.; Carballedo, R.; Diaz, F.; Onieva, E.; de la Iglesia, I.; Perallos, A.

2014-01-01

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. PMID:25165731

Pareto Design of State Feedback Tracking Control of a Biped Robot via Multiobjective PSO in Comparison with Sigma Method and Genetic Algorithms: Modified NSGAII and MATLAB's Toolbox

PubMed Central

Mahmoodabadi, M. J.; Taherkhorsandi, M.; Bagheri, A.

2014-01-01

An optimal robust state feedback tracking controller is introduced to control a biped robot. In the literature, the parameters of the controller are usually determined by a tedious trial and error process. To eliminate this process and design the parameters of the proposed controller, the multiobjective evolutionary algorithms, that is, the proposed method, modified NSGAII, Sigma method, and MATLAB's Toolbox MOGA, are employed in this study. Among the used evolutionary optimization algorithms to design the controller for biped robots, the proposed method operates better in the aspect of designing the controller since it provides ample opportunities for designers to choose the most appropriate point based upon the design criteria. Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort. Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot. PMID:24616619

Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

NASA Astrophysics Data System (ADS)

Lane, Peter C. R.; Gobet, Fernand

2013-03-01

Creating robust, reproducible and optimal computational models is a key challenge for theorists in many sciences. Psychology and cognitive science face particular challenges as large amounts of data are collected and many models are not amenable to analytical techniques for calculating parameter sets. Particular problems are to locate the full range of acceptable model parameters for a given dataset, and to confirm the consistency of model parameters across different datasets. Resolving these problems will provide a better understanding of the behaviour of computational models, and so support the development of general and robust models. In this article, we address these problems using evolutionary algorithms to develop parameters for computational models against multiple sets of experimental data; in particular, we propose the `speciated non-dominated sorting genetic algorithm' for evolving models in several theories. We discuss the problem of developing a model of categorisation using twenty-nine sets of data and models drawn from four different theories. We find that the evolutionary algorithms generate high quality models, adapted to provide a good fit to all available data.

Development of mathematical models and optimization of the process parameters of laser surface hardened EN25 steel using elitist non-dominated sorting genetic algorithm

NASA Astrophysics Data System (ADS)

Vignesh, S.; Dinesh Babu, P.; Surya, G.; Dinesh, S.; Marimuthu, P.

2018-02-01

The ultimate goal of all production entities is to select the process parameters that would be of maximum strength, minimum wear and friction. The friction and wear are serious problems in most of the industries which are influenced by the working set of parameters, oxidation characteristics and mechanism involved in formation of wear. The experimental input parameters such as sliding distance, applied load, and temperature are utilized in finding out the optimized solution for achieving the desired output responses such as coefficient of friction, wear rate, and volume loss. The optimization is performed with the help of a novel method, Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) based on an evolutionary algorithm. The regression equations obtained using Response Surface Methodology (RSM) are used in determining the optimum process parameters. Further, the results achieved through desirability approach in RSM are compared with that of the optimized solution obtained through NSGA-II. The results conclude that proposed evolutionary technique is much effective and faster than the desirability approach.

Multi-Objectives Optimization of Ventilation Controllers for Passive Cooling in Residential Buildings

PubMed Central

Grygierek, Krzysztof; Ferdyn-Grygierek, Joanna

2018-01-01

An inappropriate indoor climate, mostly indoor temperature, may cause occupants’ discomfort. There are a great number of air conditioning systems that make it possible to maintain the required thermal comfort. Their installation, however, involves high investment costs and high energy demand. The study analyses the possibilities of limiting too high a temperature in residential buildings using passive cooling by means of ventilation with ambient cool air. A fuzzy logic controller whose aim is to control mechanical ventilation has been proposed and optimized. In order to optimize the controller, the modified Multiobjective Evolutionary Algorithm, based on the Strength Pareto Evolutionary Algorithm, has been adopted. The optimization algorithm has been implemented in MATLAB®, which is coupled by MLE+ with EnergyPlus for performing dynamic co-simulation between the programs. The example of a single detached building shows that the occupants’ thermal comfort in a transitional climate may improve significantly owing to mechanical ventilation controlled by the suggested fuzzy logic controller. When the system is connected to the traditional cooling system, it may further bring about a decrease in cooling demand. PMID:29642525

Late onset hereditary episodic ataxia.

PubMed

Damak, M; Riant, F; Boukobza, M; Tournier-Lasserve, E; Bousser, M-G; Vahedi, K

2009-05-01

Episodic ataxias (EA) are hereditary paroxysmal neurological diseases with considerable clinical and genetic heterogeneity. So far seven loci have been reported and four different genes have been identified. Analysis of additional sporadic or familial cases is needed to better delineate the clinical and genetic spectrum of EA. A two generation French family with late onset episodic ataxia was examined. All consenting family members had a brain MRI with volumetric analysis of the cerebellum. Haplotype analysis was performed for the EA2 locus (19p13), the EA5 locus (2q22), the EA6 locus (5p13) and the EA7 locus (19q13). Mutation screening was performed for all exons of CACNA1A (EA2), EAAT1 (EA6) and the coding sequence of KCNA1 (EA1). Four family members had episodic ataxia with onset between 48 and 56 years of age but with heterogeneity in the severity and duration of symptoms. The two most severely affected had daily attacks of EA with a slowly progressive and disabling permanent cerebellar ataxia and a poor response to acetazolamide. Brain MRI showed in three affected members a decrease in the ratio of cerebellar volume:total intracranial volume, indicating cerebellar atrophy. No deleterious mutation was found in CACNA1A, SCA6, EAAT1 or KCNA1. In addition, the EA5 locus was excluded. A new phenotype of episodic ataxia has been described, characterised clinically by a late onset and progressive permanent cerebellar signs, and genetically by exclusion of the genes so far identified in EA.

Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm.

PubMed

Zhang, Jie; Wang, Yuping; Feng, Junhong

2013-01-01

In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

Attribute Index and Uniform Design Based Multiobjective Association Rule Mining with Evolutionary Algorithm

PubMed Central

Wang, Yuping; Feng, Junhong

2013-01-01

In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption. PMID:23766683

Searching for discrimination rules in protease proteolytic cleavage activity using genetic programming with a min-max scoring function.

PubMed

Yang, Zheng Rong; Thomson, Rebecca; Hodgman, T Charles; Dry, Jonathan; Doyle, Austin K; Narayanan, Ajit; Wu, XiKun

2003-11-01

This paper presents an algorithm which is able to extract discriminant rules from oligopeptides for protease proteolytic cleavage activity prediction. The algorithm is developed using genetic programming. Three important components in the algorithm are a min-max scoring function, the reverse Polish notation (RPN) and the use of minimum description length. The min-max scoring function is developed using amino acid similarity matrices for measuring the similarity between an oligopeptide and a rule, which is a complex algebraic equation of amino acids rather than a simple pattern sequence. The Fisher ratio is then calculated on the scoring values using the class label associated with the oligopeptides. The discriminant ability of each rule can therefore be evaluated. The use of RPN makes the evolutionary operations simpler and therefore reduces the computational cost. To prevent overfitting, the concept of minimum description length is used to penalize over-complicated rules. A fitness function is therefore composed of the Fisher ratio and the use of minimum description length for an efficient evolutionary process. In the application to four protease datasets (Trypsin, Factor Xa, Hepatitis C Virus and HIV protease cleavage site prediction), our algorithm is superior to C5, a conventional method for deriving decision trees.

Classifier-Guided Sampling for Complex Energy System Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Backlund, Peter B.; Eddy, John P.

2015-09-01

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of omore » bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.« less

A master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design under general hydrogeological conditions

NASA Astrophysics Data System (ADS)

Wu, J.; Yang, Y.; Luo, Q.; Wu, J.

2012-12-01

This study presents a new hybrid multi-objective evolutionary algorithm, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), whereby the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions arose from the evolving nondominated sorting genetic algorithm II (NSGA-II) population. Also, the NPTSGA coupled with the commonly used groundwater flow and transport codes, MODFLOW and MT3DMS, is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large-scale field groundwater remediation system for cleanup of large trichloroethylene (TCE) plume at the Massachusetts Military Reservation (MMR) in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface (MPI) is incorporated into the NPTSGA to implement objective function evaluations in distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world application. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Simulating (log(c) n)-wise Independence in NC

DTIC Science & Technology

1989-05-01

independent) distribution. However, Xk (A) = (1x(I))k = Z Z y (i,)...X(ik). iEA ijEAi 2 EA IkEA So Lemma 2.4 applies to show that any k-wise independent...AEA AEAi1EA ikEA So henceforth we want an X such that F(X) >_ E[F(X)]. 2.5 Generating k-Wise Independent Variables It still remains to demonstrate a k

Cloning, production, and functional expression of the bacteriocin enterocin A, produced by Enterococcus faecium T136, by the yeasts Pichia pastoris, Kluyveromyces lactis, Hansenula polymorpha, and Arxula adeninivorans.

PubMed

Borrero, Juan; Kunze, Gotthard; Jiménez, Juan J; Böer, Erik; Gútiez, Loreto; Herranz, Carmen; Cintas, Luis M; Hernández, Pablo E

2012-08-01

The bacteriocin enterocin A (EntA) produced by Enterococcus faecium T136 has been successfully cloned and produced by the yeasts Pichia pastoris X-33EA, Kluyveromyces lactis GG799EA, Hansenula polymorpha KL8-1EA, and Arxula adeninivorans G1212EA. Moreover, P. pastoris X-33EA and K. lactis GG799EA produced EntA in larger amounts and with higher antimicrobial and specific antimicrobial activities than the EntA produced by E. faecium T136.

Analysis of Leadership in Energy and Environmental Design Construction in the Air Force

DTIC Science & Technology

2012-03-01

I EQ 8.2: Day light & Views - Views for 90% of Spaces EA 1: 32% for New Buli ld ings/28% for Existing ... IEQ8.1: Daylight& Views - Dayli ght 75...Publlic ... EA 2: On-Site Renewable Energy- 1%* ID 1.5: lnnovat1ion in Design - 1 Credit* EA 1: 38% for New Buli ld ings/34% for Existing ... EA 6...Green Power MR 3: M ater ia ls Reuse: 5% • 1 Point EA 1: 34% for New Bui ld ings/3D% for Existing .. . EA 1: 36% for New Buli ld ings/32% for Existing

Expert Advisor (EA) Evaluation System Using Web-based ELECTRE Method in Foreign Exchange (Forex) Market

NASA Astrophysics Data System (ADS)

Satibi; Widodo, Catur Edi; Farikhin

2018-02-01

This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York) to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criteria that calculated using web-based ELECTRE method. The ideas of this research are to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR/USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels.

Reduced energy availability: implications for bone health in physically active populations.

PubMed

Papageorgiou, Maria; Dolan, Eimear; Elliott-Sale, Kirsty J; Sale, Craig

2018-04-01

The present review critically evaluates existing literature on the effects of short- and long-term low energy availability (EA) on bone metabolism and health in physically active individuals. We reviewed the literature on the short-term effects of low EA on markers of bone metabolism and the long-term effects of low EA on outcomes relating to bone health (bone mass, microarchitecture and strength, bone metabolic markers and stress fracture injury risk) in physically active individuals. Available evidence indicates that short-term low EA may increase markers of bone resorption and decrease markers of bone formation in physically active women. Bone metabolic marker responses to low EA are less well known in physically active men. Cross-sectional studies investigating the effects of long-term low EA suggest that physically active individuals who have low EA present with lower bone mass, altered bone metabolism (favouring bone resorption), reduced bone strength and increased risk for stress fracture injuries. Reduced EA has a negative influence on bone in both the short- and long-term, and every effort should be made to reduce its occurrence in physically active individuals. Future interventions are needed to explore the effects of long-term reduced EA on bone health outcomes, while short-term low EA studies are also required to give insight into the pathophysiology of bone alterations.

Enhancing Data Assimilation by Evolutionary Particle Filter and Markov Chain Monte Carlo

NASA Astrophysics Data System (ADS)

Moradkhani, H.; Abbaszadeh, P.; Yan, H.

2016-12-01

Particle Filters (PFs) have received increasing attention by the researchers from different disciplines in hydro-geosciences as an effective method to improve model predictions in nonlinear and non-Gaussian dynamical systems. The implication of dual state and parameter estimation by means of data assimilation in hydrology and geoscience has evolved since 2005 from SIR-PF to PF-MCMC and now to the most effective and robust framework through evolutionary PF approach based on Genetic Algorithm (GA) and Markov Chain Monte Carlo (MCMC), the so-called EPF-MCMC. In this framework, the posterior distribution undergoes an evolutionary process to update an ensemble of prior states that more closely resemble realistic posterior probability distribution. The premise of this approach is that the particles move to optimal position using the GA optimization coupled with MCMC increasing the number of effective particles, hence the particle degeneracy is avoided while the particle diversity is improved. The proposed algorithm is applied on a conceptual and highly nonlinear hydrologic model and the effectiveness, robustness and reliability of the method in jointly estimating the states and parameters and also reducing the uncertainty is demonstrated for few river basins across the United States.

Hybrid intelligent methodology to design translation invariant morphological operators for Brazilian stock market prediction.

PubMed

Araújo, Ricardo de A

2010-12-01

This paper presents a hybrid intelligent methodology to design increasing translation invariant morphological operators applied to Brazilian stock market prediction (overcoming the random walk dilemma). The proposed Translation Invariant Morphological Robust Automatic phase-Adjustment (TIMRAA) method consists of a hybrid intelligent model composed of a Modular Morphological Neural Network (MMNN) with a Quantum-Inspired Evolutionary Algorithm (QIEA), which searches for the best time lags to reconstruct the phase space of the time series generator phenomenon and determines the initial (sub-optimal) parameters of the MMNN. Each individual of the QIEA population is further trained by the Back Propagation (BP) algorithm to improve the MMNN parameters supplied by the QIEA. Also, for each prediction model generated, it uses a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in stock market time series. Furthermore, an experimental analysis is conducted with the proposed method through four Brazilian stock market time series, and the achieved results are discussed and compared to results found with random walk models and the previously introduced Time-delay Added Evolutionary Forecasting (TAEF) and Morphological-Rank-Linear Time-lag Added Evolutionary Forecasting (MRLTAEF) methods. Copyright © 2010 Elsevier Ltd. All rights reserved.

Optimizing a reconfigurable material via evolutionary computation

NASA Astrophysics Data System (ADS)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Evolution, learning, and cognition

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Y.C.

1988-01-01

The book comprises more than fifteen articles in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.

Modular Matrix Multiplication on a Linear Array.

DTIC Science & Technology

1983-11-01

is fl(n2). 2 Case e Irl __ (see Figure 5.2) 2 2 ,1 Y, " X2v- ’ Y2 -. x= -- ~ Y4 "i; Yin Figure 5Ŗ At t--xi, either all Gk, such that IkEA , have n...nat and Image Proceuing, IEEE Transactions on Computers, Vol. C-31, No. 10 22 (October, 1982), pp. IO0oo09. [41 H.T. Kung, Let’s Design Algorithms for...VLSI Systems, Proc. Caltech Conf. on Very Large Scale Integration: Architecture, Design , Fabrication (January, 1979), pp. 65. 90. 151 H.T. Kung, and

Does labour epidural slow the progress of labour and lead to complications? Obstetricians' perception working in private and public sector teaching hospitals in a developing country.

PubMed

Sohaib, Muhammad; Ismail, Samina

2015-12-01

Obstetricians play a major role in the decision making for provision of analgesia for the woman in labour. As epidural analgesia (EA) is the most preferred technique, it is important to know obstetricians' perception regarding its effect on progress of labour and associated complications. The 6 months cross-sectional study included 114 obstetricians from teaching hospitals. After informed consent, obstetricians were asked to fill a predesigned questionnaire containing 13 close ended questions regarding their perception on the effect of EA on progress of labour, EA complications and whether they would recommend EA to their patients or not. Other variables included age, gender, training in EA, practice type and hospital settings (private or public sector). Majority of the obstetricians had the perception of EA prolonging the first stage (89.5%) and second stage (98.2%) of labour, increasing the rate of caesarean section (87.7%), instrumental delivery (58.8%) and increasing the incidence of backache (85.5%). None of the obstetricians received any formal training in EA. Majority (84.2%) were not sure if they would recommend EA to their patients. When these responses were compared between public and private sector, a statistically higher percentage (P < 0.001) of public sector obstetricians had negative perception of EA. Perception of obstetrician regarding EA is contrary to the current evidence. There is a need to introduce formal curriculum on EA in obstetric training program and conduct regular refresher courses.

Optimization of sequence alignment for simple sequence repeat regions.

PubMed

Jighly, Abdulqader; Hamwieh, Aladdin; Ogbonnaya, Francis C

2011-07-20

Microsatellites, or simple sequence repeats (SSRs), are tandemly repeated DNA sequences, including tandem copies of specific sequences no longer than six bases, that are distributed in the genome. SSR has been used as a molecular marker because it is easy to detect and is used in a range of applications, including genetic diversity, genome mapping, and marker assisted selection. It is also very mutable because of slipping in the DNA polymerase during DNA replication. This unique mutation increases the insertion/deletion (INDELs) mutation frequency to a high ratio - more than other types of molecular markers such as single nucleotide polymorphism (SNPs).SNPs are more frequent than INDELs. Therefore, all designed algorithms for sequence alignment fit the vast majority of the genomic sequence without considering microsatellite regions, as unique sequences that require special consideration. The old algorithm is limited in its application because there are many overlaps between different repeat units which result in false evolutionary relationships. To overcome the limitation of the aligning algorithm when dealing with SSR loci, a new algorithm was developed using PERL script with a Tk graphical interface. This program is based on aligning sequences after determining the repeated units first, and the last SSR nucleotides positions. This results in a shifting process according to the inserted repeated unit type.When studying the phylogenic relations before and after applying the new algorithm, many differences in the trees were obtained by increasing the SSR length and complexity. However, less distance between different linage had been observed after applying the new algorithm. The new algorithm produces better estimates for aligning SSR loci because it reflects more reliable evolutionary relations between different linages. It reduces overlapping during SSR alignment, which results in a more realistic phylogenic relationship.

Ellagic acid inhibits the proliferation of human pancreatic carcinoma PANC-1 cells in vitro and in vivo.

PubMed

Cheng, Hao; Lu, Chenglin; Tang, Ribo; Pan, Yiming; Bao, Shanhua; Qiu, Yudong; Xie, Min

2017-02-14

Ellagic aicd (EA), a dietary polyphenolic compound found in plants and fruits, possesses various pharmacological activities. This study investigated the effect of EA on human pancreatic carcinoma PANC-1 cells both in vitro and in vivo; and defined the associated molecular mechanisms. In vitro, the cell growth and repairing ability were assessed by CCK-8 assay and wound healing assay. The cell migration and invasion activity was evaluated by Tanswell assay. In vivo, PANC-1 cell tumor-bearing mice were treated with different concentrations of EA. We found that EA significantly inhibited cell growth, cell repairing activity, and cell migration and invasion in a dose-dependent manner. Treatment of PANC-1 xenografted mice with EA resulted in significant inhibition in tumor growth and prolong mice survival rate. Furthermore, flow cytometric analysis showed that EA increased the percentage of cells in the G1 phase of cell cycle. Western blot analysis revealed that EA inhibited the expression of COX-2 and NF-κB. In addition, EA reversed epithelial to mesenchymal transition by up-regulating E-cadherin and down-regulating Vimentin. In summary, the present study demonstrated that EA inhibited cell growth, cell repairing activity, cell migration and invasion in a dose-dependent manner. EA also effectively inhibit human pancreatic cancer growth in mice. The anti-tumor effect of EA might be related to cell cycle arrest, down-regulating the expression of COX-2 and NF-κB, reversing epithelial to mesenchymal transition by up-regulating E-cadherin and down-regulating Vimentin. Our findings suggest that the use of EA would be beneficial for the management of pancreatic cancer.

Electroacupuncture modulation of reflex hypertension in rats: role of cholecystokinin octapeptide

PubMed Central

Tjen-A-Looi, Stephanie C.; Guo, Zhi-Ling; Longhurst, John C.

2013-01-01

Acupuncture or electroacupuncture (EA) potentially offers a nonpharmacological approach to reduce high blood pressure (BP). However, ∼70% of the patients and animal subjects respond to EA, while 30% do not. EA acts, in part, through an opioid mechanism in the rostral ventrolateral medulla (rVLM) to inhibit sympathoexcitatory reflexes induced by gastric distention. CCK-8 opposes the action of opioids during analgesia. Therefore, we hypothesized that CCK-8 in the rVLM antagonizes EA modulation of sympathoexcitatory cardiovascular reflex responses. Male rats anesthetized with ketamine and α-chloralose subjected to repeated gastric distension every 10 min were examined for their responsiveness to EA (2 Hz, 0.5 ms, 1–4 mA) at P5-P6 acupoints overlying median nerve. Repeated gastric distension every 10 min evoked consistent sympathoexcitatory responses. EA at P5-P6 modulated gastric distension-induced responses. Microinjection of CCK-8 in the rVLM reversed the EA effect in seven responders. The CCK1 receptor antagonist devazepide microinjected into the rVLM converted six nonresponders to responders by lowering the reflex response from 21 ± 2.2 to 10 ± 2.9 mmHg (first vs. second application of EA). The EA modulatory action in rats converted to responders with devazepide was reversed with rVLM microinjection of naloxone (n = 6). Microinjection of devazepide in the absence of a second application of EA did not influence the primary pressor reflexes of nonresponders. These data suggest that CCK-8 antagonizes EA modulation of sympathoexcitatory cardiovascular responses through an opioid mechanism and that inhibition of CCK-8 can convert animals that initially are unresponsive to EA to become responsive. PMID:23785073

Ergot cluster-encoded catalase is required for synthesis of chanoclavine-I in Aspergillus fumigatus.

PubMed

Goetz, Kerry E; Coyle, Christine M; Cheng, Johnathan Z; O'Connor, Sarah E; Panaccione, Daniel G

2011-06-01

Genes required for ergot alkaloid biosynthesis are clustered in the genomes of several fungi. Several conserved ergot cluster genes have been hypothesized, and in some cases demonstrated, to encode early steps of the pathway shared among fungi that ultimately make different ergot alkaloid end products. The deduced amino acid sequence of one of these conserved genes (easC) indicates a catalase as the product, but a role for a catalase in the ergot alkaloid pathway has not been established. We disrupted easC of Aspergillus fumigatus by homologous recombination with a truncated copy of that gene. The resulting mutant (ΔeasC) failed to produce the ergot alkaloids typically observed in A. fumigatus, including chanoclavine-I, festuclavine, and fumigaclavines B, A, and C. The ΔeasC mutant instead accumulated N-methyl-4-dimethylallyltryptophan (N-Me-DMAT), an intermediate recently shown to accumulate in Claviceps purpurea strains mutated at ccsA (called easE in A. fumigatus) (Lorenz et al. Appl Environ Microbiol 76:1822-1830, 2010). A ΔeasE disruption mutant of A. fumigatus also failed to accumulate chanoclavine-I and downstream ergot alkaloids and, instead, accumulated N-Me-DMAT. Feeding chanoclavine-I to the ΔeasC mutant restored ergot alkaloid production. Complementation of either ΔeasC or ΔeasE mutants with the respective wild-type allele also restored ergot alkaloid production. The easC gene was expressed in Escherichia coli, and the protein product displayed in vitro catalase activity with H(2)O(2) but did not act, in isolation, on N-Me-DMAT as substrate. The data indicate that the products of both easC (catalase) and easE (FAD-dependent oxidoreductase) are required for conversion of N-Me-DMAT to chanoclavine-I.

High correlation of estimated local conduction velocity with natural logarithm of bipolar electrogram amplitude in the reentry circuit of atrial flutter.

PubMed

Itoh, Taihei; Kimura, Masaomi; Sasaki, Shingo; Owada, Shingen; Horiuchi, Daisuke; Sasaki, Kenichi; Ishida, Yuji; Takahiko, Kinjo; Okumura, Ken

2014-04-01

Low conduction velocity (CV) in the area showing low electrogram amplitude (EA) is characteristic of reentry circuit of atypical atrial flutter (AFL). The quantitative relationship between CV and EA remains unclear. We characterized AFL reentry circuit in the right atrium (RA), focusing on the relationship between local CV and bipolar EA on the circuit. We investigated 26 RA AFL (10 with typical AFL; 10 atypical incisional AFL; 6 atypical nonincisional AFL) using CARTO system. By referring to isochronal and propagation maps delineated during AFL, points activated faster on the circuit were selected (median, 7 per circuit). At the 196 selected points obtained from all patients, local CV measured between the adjacent points and bipolar EA were analyzed. There was a highly significant correlation between local CV and natural logarithm of EA (lnEA) (R(2) = 0.809, P < 0.001). Among 26 AFL, linear regression analysis of mean CV, calculated by dividing circuit length (152.3 ± 41.7 mm) by tachycardia cycle length (TCL) (median 246 msec), and mean lnEA, calculated by dividing area under curve of lnEA during one tachycardia cycle by TCL, showed y = 0.695 + 0.191x (where: y = mean CV, x = lnEA; R(2) = 0.993, P < 0.001). Local CV estimated from EA with the use of this formula showed a highly significant linear correlation with that measured by the map (R(2) = 0.809, P < 0.001). The lnEA and estimated local CV show a highly positive linear correlation. CV is possibly estimated by EA measured by CARTO mapping. © 2013 Wiley Periodicals, Inc.

CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

NASA Astrophysics Data System (ADS)

Mallick, S.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2016-07-01

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSA-DE-based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

Evolutionary engineering for industrial microbiology.

PubMed

Vanee, Niti; Fisher, Adam B; Fong, Stephen S

2012-01-01

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

Improvements in Metabolic Health with Consumption of Ellagic Acid and Subsequent Conversion into Urolithins: Evidence and Mechanisms12

PubMed Central

Kang, Inhae; Buckner, Teresa; Gu, Liwei

2016-01-01

Ellagic acid (EA) is a naturally occurring polyphenol found in some fruits and nuts, including berries, pomegranates, grapes, and walnuts. EA has been investigated extensively because of its antiproliferative action in some cancers, along with its anti-inflammatory effects. A growing body of evidence suggests that the intake of EA is effective in attenuating obesity and ameliorating obesity-mediated metabolic complications, such as insulin resistance, type 2 diabetes, nonalcoholic fatty liver disease, and atherosclerosis. In this review, we summarize how intake of EA regulates lipid metabolism in vitro and in vivo, and delineate the potential mechanisms of action of EA on obesity-mediated metabolic complications. We also discuss EA as an epigenetic effector, as well as a modulator of the gut microbiome, suggesting that EA may exert a broader spectrum of health benefits than has been demonstrated to date. Therefore, this review aims to suggest the potential metabolic benefits of consumption of EA-containing fruits and nuts against obesity-associated health conditions. PMID:27633111

Ventriculo-arterial coupling detects occult RV dysfunction in chronic thromboembolic pulmonary vascular disease.

PubMed

Axell, Richard G; Messer, Simon J; White, Paul A; McCabe, Colm; Priest, Andrew; Statopoulou, Thaleia; Drozdzynska, Maja; Viscasillas, Jamie; Hinchy, Elizabeth C; Hampton-Till, James; Alibhai, Hatim I; Morrell, Nicholas; Pepke-Zaba, Joanna; Large, Stephen R; Hoole, Stephen P

2017-04-01

Chronic thromboembolic disease (CTED) is suboptimally defined by a mean pulmonary artery pressure (mPAP) <25 mmHg at rest in patients that remain symptomatic from chronic pulmonary artery thrombi. To improve identification of right ventricular (RV) pathology in patients with thromboembolic obstruction, we hypothesized that the RV ventriculo-arterial (Ees/Ea) coupling ratio at maximal stroke work (Ees/Ea max sw ) derived from an animal model of pulmonary obstruction may be used to identify occult RV dysfunction (low Ees/Ea) or residual RV energetic reserve (high Ees/Ea). Eighteen open chested pigs had conductance catheter RV pressure-volume (PV)-loops recorded during PA snare to determine Ees/Ea max sw This was then applied to 10 patients with chronic thromboembolic pulmonary hypertension (CTEPH) and ten patients with CTED, also assessed by RV conductance catheter and cardiopulmonary exercise testing. All patients were then restratified by Ees/Ea. The animal model determined an Ees/Ea max sw  = 0.68 ± 0.23 threshold, either side of which cardiac output and RV stroke work fell. Two patients with CTED were identified with an Ees/Ea well below 0.68 suggesting occult RV dysfunction whilst three patients with CTEPH demonstrated Ees/Ea ≥ 0.68 suggesting residual RV energetic reserve. Ees/Ea > 0.68 and Ees/Ea < 0.68 subgroups demonstrated constant RV stroke work but lower stroke volume (87.7 ± 22.1 vs. 60.1 ± 16.3 mL respectively, P  = 0.006) and higher end-systolic pressure (36.7 ± 11.6 vs. 68.1 ± 16.7 mmHg respectively, P  < 0.001). Lower Ees/Ea in CTED also correlated with reduced exercise ventilatory efficiency. Low Ees/Ea aligns with features of RV maladaptation in CTED both at rest and on exercise. Characterization of Ees/Ea in CTED may allow for better identification of occult RV dysfunction. © 2017 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

Euthanasia and physician-assisted suicide.

PubMed

Swarte, N B; Heintz, A P

1999-12-01

In the Netherlands there are about 9700 explicit requests for euthanasia or physician-assisted suicide (EAS) each year, of which approximately 3600 are granted. Other countries have criticized the Dutch policy concerning EAS. It has been suggested that palliative care in the Netherlands is not adequate and that euthanasia is often requested by patients with depression. In addition, this criticism is partly based on the firm stance that 'human life has an absolute value and a human being has under no circumstances the right of self-determination over his or her own life'. Many aspects of EAS are currently the focus of attention in the literature. In this review the following aspects of EAS are discussed: ethics, judicial questions, the relationship between depression and euthanasia, and the impact of EAS on members of the family. Also, the current situation concerning EAS in the Netherlands is summarized and described. Despite the fact that EAS have been widely discussed in the literature, the association between depression and the number of requests for EAS remains to be discovered. It is also not yet known what the effects of EAS are on members of the family, and whether unnatural death causes a higher incidence of complicated grief.

The diagnosis of fetal esophageal atresia and its implications on perinatal outcome.

PubMed

Kunisaki, Shaun M; Bruch, Steven W; Hirschl, Ronald B; Mychaliska, George B; Treadwell, Marjorie C; Coran, Arnold G

2014-10-01

The current diagnostic accuracy and perinatal outcome of fetuses with esophageal atresia (EA) continues to be debated. In this review, we report on our experience at a tertiary care fetal center with the prenatal ultrasound diagnosis of EA. Enrollment criteria included a small/absent stomach bubble with a normal or elevated amniotic fluid index between 2005 and 2013. Perinatal outcomes were analyzed and compared to postnatally diagnosed EA cases. Of the 22 fetuses evaluated, polyhydramnios occurred in 73%. Three (14%) died in utero or shortly after birth, but none had EA. In the presence of an absent/small stomach and polyhydramnios, the positive predictive value for EA was 67%. In fetal EA cases confirmed postnatally (group 1, n = 11), there were no differences in gestational age, birthweight, or mortality when compared to postnatally diagnosed infants (group 2, n = 59). Group 1 was associated with long-gap EA, need for esophageal replacement, and increased hospital length of stay. When taken in context with the current literature, we conclude that ultrasound findings suggestive of EA continue to be associated with a relatively high rate of false positives. However, among postnatally confirmed cases, there is an increased risk for long-gap EA and prolonged hospitalization.

ComEA Is Essential for the Transfer of External DNA into the Periplasm in Naturally Transformable Vibrio cholerae Cells

PubMed Central

Seitz, Patrick; Pezeshgi Modarres, Hassan; Borgeaud, Sandrine; Bulushev, Roman D.; Steinbock, Lorenz J.; Radenovic, Aleksandra; Dal Peraro, Matteo; Blokesch, Melanie

2014-01-01

The DNA uptake of naturally competent bacteria has been attributed to the action of DNA uptake machineries resembling type IV pilus complexes. However, the protein(s) for pulling the DNA across the outer membrane of Gram-negative bacteria remain speculative. Here we show that the competence protein ComEA binds incoming DNA in the periplasm of naturally competent Vibrio cholerae cells thereby promoting DNA uptake, possibly through ratcheting and entropic forces associated with ComEA binding. Using comparative modeling and molecular simulations, we projected the 3D structure and DNA-binding site of ComEA. These in silico predictions, combined with in vivo and in vitro validations of wild-type and site-directed modified variants of ComEA, suggested that ComEA is not solely a DNA receptor protein but plays a direct role in the DNA uptake process. Furthermore, we uncovered that ComEA homologs of other bacteria (both Gram-positive and Gram-negative) efficiently compensated for the absence of ComEA in V. cholerae, suggesting that the contribution of ComEA in the DNA uptake process might be conserved among naturally competent bacteria. PMID:24391524

Rac1/WAVE2 and Cdc42/N-WASP Participation in Actin-Dependent Host Cell Invasion by Extracellular Amastigotes of Trypanosoma cruzi.

PubMed

Bonfim-Melo, Alexis; Ferreira, Éden R; Mortara, Renato A

2018-01-01

This study evaluated the participation of host cell Rho-family GTPases and their effector proteins in the actin-dependent invasion by Trypanosoma cruzi extracellular amastigotes (EAs). We observed that all proteins were recruited and colocalized with actin at EA invasion sites in live or fixed cells. EA internalization was inhibited in cells depleted in Rac1, N-WASP, and WAVE2. Time-lapse experiments with Rac1, N-WASP and WAVE2 depleted cells revealed that EA internalization kinetics is delayed even though no differences were observed in the proportion of EA-induced actin recruitment in these groups. Overexpression of constitutively active constructs of Rac1 and RhoA altered the morphology of actin recruitments to EA invasion sites. Additionally, EA internalization was increased in cells overexpressing CA-Rac1 but inhibited in cells overexpressing CA-RhoA. WT-Cdc42 expression increased EA internalization, but curiously, CA-Cdc42 inhibited it. Altogether, these results corroborate the hypothesis of EA internalization in non-phagocytic cells by a phagocytosis-like mechanism and present Rac1 as the key Rho-family GTPase in this process.

Rac1/WAVE2 and Cdc42/N-WASP Participation in Actin-Dependent Host Cell Invasion by Extracellular Amastigotes of Trypanosoma cruzi

PubMed Central

Bonfim-Melo, Alexis; Ferreira, Éden R.; Mortara, Renato A.

2018-01-01

This study evaluated the participation of host cell Rho-family GTPases and their effector proteins in the actin-dependent invasion by Trypanosoma cruzi extracellular amastigotes (EAs). We observed that all proteins were recruited and colocalized with actin at EA invasion sites in live or fixed cells. EA internalization was inhibited in cells depleted in Rac1, N-WASP, and WAVE2. Time-lapse experiments with Rac1, N-WASP and WAVE2 depleted cells revealed that EA internalization kinetics is delayed even though no differences were observed in the proportion of EA-induced actin recruitment in these groups. Overexpression of constitutively active constructs of Rac1 and RhoA altered the morphology of actin recruitments to EA invasion sites. Additionally, EA internalization was increased in cells overexpressing CA-Rac1 but inhibited in cells overexpressing CA-RhoA. WT-Cdc42 expression increased EA internalization, but curiously, CA-Cdc42 inhibited it. Altogether, these results corroborate the hypothesis of EA internalization in non-phagocytic cells by a phagocytosis-like mechanism and present Rac1 as the key Rho-family GTPase in this process. PMID:29541069