Algorithmic Mechanism Design of Evolutionary Computation
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. PMID:26257777
Algorithmic Mechanism Design of Evolutionary Computation.
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. PMID:26257777
Fast Algorithms for Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan
2005-01-01
Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.
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.
An Evolutionary Model Based on Bit-String with Intelligence
NASA Astrophysics Data System (ADS)
He, Mingfeng; Pan, Qiuhui; Yu, Binglin
An evolutionary model based on bit-strings with intelligence is set up in this paper. In this model, gene is divided into two parts which relative to health and intelligence. The accumulated intelligence influences the survival process by the effect of food and space restrictions. We modify the Verhulst factor to study this effect. Both asexual and sexual model are discussed in this paper. The results show that after many time steps, stability is reached and the population self-organizes, just like the standard Penna model. The intelligence made the equilibrium to be reached larger both in asexual model and sexual model. Compared with asexual model the population size fluctuates more strongly in the sexual model.
Evolutionary Algorithm for Optimal Vaccination Scheme
NASA Astrophysics Data System (ADS)
Parousis-Orthodoxou, K. J.; Vlachos, D. S.
2014-03-01
The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease.
Evolutionary development of path planning algorithms
Hage, M
1998-09-01
This paper describes the use of evolutionary software techniques for developing both genetic algorithms and genetic programs. Genetic algorithms are evolved to solve a specific problem within a fixed and known environment. While genetic algorithms can evolve to become very optimized for their task, they often are very specialized and perform poorly if the environment changes. Genetic programs are evolved through simultaneous training in a variety of environments to develop a more general controller behavior that operates in unknown environments. Performance of genetic programs is less optimal than a specially bred algorithm for an individual environment, but the controller performs acceptably under a wider variety of circumstances. The example problem addressed in this paper is evolutionary development of algorithms and programs for path planning in nuclear environments, such as Chernobyl.
Synthesis of logic circuits with evolutionary algorithms
JONES,JAKE S.; DAVIDSON,GEORGE S.
2000-01-26
In the last decade there has been interest and research in the area of designing circuits with genetic algorithms, evolutionary algorithms, and genetic programming. However, the ability to design circuits of the size and complexity required by modern engineering design problems, simply by specifying required outputs for given inputs has as yet eluded researchers. This paper describes current research in the area of designing logic circuits using an evolutionary algorithm. The goal of the research is to improve the effectiveness of this method and make it a practical aid for design engineers. A novel method of implementing the algorithm is introduced, and results are presented for various multiprocessing systems. In addition to evolving standard arithmetic circuits, work in the area of evolving circuits that perform digital signal processing tasks is described.
Evolutionary Algorithm for Calculating Available Transfer Capability
NASA Astrophysics Data System (ADS)
Šošić, Darko; Škokljev, Ivan
2013-09-01
The paper presents an evolutionary algorithm for calculating available transfer capability (ATC). ATC is a measure of the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses. In this paper, MATLAB software is used to determine the ATC between any bus in deregulated power systems without violating system constraints such as thermal, voltage, and stability constraints. The algorithm is applied on IEEE 5 bus system and on IEEE 30 bus system.
Automated Antenna Design with Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Linden, Derek; Hornby, Greg; Lohn, Jason; Globus, Al; Krishunkumor, K.
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
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…
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.
Protein Structure Prediction with Evolutionary Algorithms
Hart, W.E.; Krasnogor, N.; Pelta, D.A.; Smith, J.
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.
Turbopump Performance Improved by Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Oyama, Akira; Liou, Meng-Sing
2002-01-01
The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.
Evolutionary algorithms and multi-agent systems
NASA Astrophysics Data System (ADS)
Oh, Jae C.
2006-05-01
This paper discusses how evolutionary algorithms are related to multi-agent systems and the possibility of military applications using the two disciplines. In particular, we present a game theoretic model for multi-agent resource distribution and allocation where agents in the environment must help each other to survive. Each agent maintains a set of variables representing actual friendship and perceived friendship. The model directly addresses problems in reputation management schemes in multi-agent systems and Peer-to-Peer distributed systems. We present algorithms based on evolutionary game process for maintaining the friendship values as well as a utility equation used in each agent's decision making. For an application problem, we adapted our formal model to the military coalition support problem in peace-keeping missions. Simulation results show that efficient resource allocation and sharing with minimum communication cost is achieved without centralized control.
Use of evolutionary algorithms for telescope scheduling
NASA Astrophysics Data System (ADS)
Grim, Ruud; Jansen, Mischa; Baan, Arno; van Hemert, Jano; de Wolf, Hans
2002-07-01
LOFAR, a new radio telescope, will be designed to observe with up to 8 independent beams, thus allowing several simultaneous observations. Scheduling of multiple observations parallel in time, each having their own constraints, requires a more intelligent and flexible scheduling function then operated before. In support of the LOFAR radio telescope project, and in co-operation with Leiden University, Fokker Space has started a study to investigate the suitability of the use of evolutionary algorithms applied to complex scheduling problems. After a positive familiarization phase, we now examine the potential use of evolutionary algorithms via a demonstration project. Results of the familiarization phase, and the first results of the demonstration project are presented in this paper.
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.
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.
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Predicting polymeric crystal structures by evolutionary algorithms
NASA Astrophysics Data System (ADS)
Zhu, Qiang; Sharma, Vinit; Oganov, Artem R.; Ramprasad, Ramamurthy
2014-10-01
The recently developed evolutionary algorithm USPEX proved to be a tool that enables accurate and reliable prediction of structures. Here we extend this method to predict the crystal structure of polymers by constrained evolutionary search, where each monomeric unit is treated as a building block with fixed connectivity. This greatly reduces the search space and allows the initial structure generation with different sequences and packings of these blocks. The new constrained evolutionary algorithm is successfully tested and validated on a diverse range of experimentally known polymers, namely, polyethylene, polyacetylene, poly(glycolic acid), poly(vinyl chloride), poly(oxymethylene), poly(phenylene oxide), and poly (p-phenylene sulfide). By fixing the orientation of polymeric chains, this method can be further extended to predict the structures of complex linear polymers, such as all polymorphs of poly(vinylidene fluoride), nylon-6 and cellulose. The excellent agreement between predicted crystal structures and experimentally known structures assures a major role of this approach in the efficient design of the future polymeric materials.
Evolutionary algorithm for metabolic pathways synthesis.
Gerard, Matias F; Stegmayer, Georgina; Milone, Diego H
2016-06-01
Metabolic pathway building is an active field of research, necessary to understand and manipulate the metabolism of organisms. There are different approaches, mainly based on classical search methods, to find linear sequences of reactions linking two compounds. However, an important limitation of these methods is the exponential increase of search trees when a large number of compounds and reactions is considered. Besides, such models do not take into account all substrates for each reaction during the search, leading to solutions that lack biological feasibility in many cases. This work proposes a new evolutionary algorithm that allows searching not only linear, but also branched metabolic pathways, formed by feasible reactions that relate multiple compounds simultaneously. Tests performed using several sets of reactions show that this algorithm is able to find feasible linear and branched metabolic pathways. PMID:27080162
Performance Comparison Of Evolutionary Algorithms For Image Clustering
NASA Astrophysics Data System (ADS)
Civicioglu, P.; Atasever, U. H.; Ozkan, C.; Besdok, E.; Karkinli, A. E.; Kesikoglu, A.
2014-09-01
Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.
Wind farm optimization using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Ituarte-Villarreal, Carlos M.
In recent years, the wind power industry has focused its efforts on solving the Wind Farm Layout Optimization (WFLO) problem. Wind resource assessment is a pivotal step in optimizing the wind-farm design and siting and, in determining whether a project is economically feasible or not. In the present work, three (3) different optimization methods are proposed for the solution of the WFLO: (i) A modified Viral System Algorithm applied to the optimization of the proper location of the components in a wind-farm to maximize the energy output given a stated wind environment of the site. The optimization problem is formulated as the minimization of energy cost per unit produced and applies a penalization for the lack of system reliability. The viral system algorithm utilized in this research solves three (3) well-known problems in the wind-energy literature; (ii) a new multiple objective evolutionary algorithm to obtain optimal placement of wind turbines while considering the power output, cost, and reliability of the system. The algorithm presented is based on evolutionary computation and the objective functions considered are the maximization of power output, the minimization of wind farm cost and the maximization of system reliability. The final solution to this multiple objective problem is presented as a set of Pareto solutions and, (iii) A hybrid viral-based optimization algorithm adapted to find the proper component configuration for a wind farm with the introduction of the universal generating function (UGF) analytical approach to discretize the different operating or mechanical levels of the wind turbines in addition to the various wind speed states. The proposed methodology considers the specific probability functions of the wind resource to describe their proper behaviors to account for the stochastic comportment of the renewable energy components, aiming to increase their power output and the reliability of these systems. The developed heuristic considers a
A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis
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
Evolutionary algorithm for vehicle driving cycle generation.
Perhinschi, Mario G; Marlowe, Christopher; Tamayo, Sergio; Tu, Jun; Wayne, W Scott
2011-09-01
Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University. PMID:22010377
A Review of Surrogate Assisted Multiobjective Evolutionary Algorithms
Díaz-Manríquez, Alan; Toscano, Gregorio; Barron-Zambrano, Jose Hugo; Tello-Leal, Edgar
2016-01-01
Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class. PMID:27382366
Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm
Lagos, Carolina; Crawford, Broderick; Cabrera, Enrique; Rubio, José-Miguel; Paredes, Fernando
2014-01-01
Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric. PMID:25254257
Learning evasive maneuvers using evolutionary algorithms and neural networks
NASA Astrophysics Data System (ADS)
Kang, Moung Hung
In this research, evolutionary algorithms and recurrent neural networks are combined to evolve control knowledge to help pilots avoid being struck by a missile, based on a two-dimensional air combat simulation model. The recurrent neural network is used for representing the pilot's control knowledge and evolutionary algorithms (i.e., Genetic Algorithms, Evolution Strategies, and Evolutionary Programming) are used for optimizing the weights and/or topology of the recurrent neural network. The simulation model of the two-dimensional evasive maneuver problem evolved is used for evaluating the performance of the recurrent neural network. Five typical air combat conditions were selected to evaluate the performance of the recurrent neural networks evolved by the evolutionary algorithms. Analysis of Variance (ANOVA) tests and response graphs were used to analyze the results. Overall, there was little difference in the performance of the three evolutionary algorithms used to evolve the control knowledge. However, the number of generations of each algorithm required to obtain the best performance was significantly different. ES converges the fastest, followed by EP and then by GA. The recurrent neural networks evolved by the evolutionary algorithms provided better performance than the traditional recommendations for evasive maneuvers, maximum gravitational turn, for each air combat condition. Furthermore, the recommended actions of the recurrent neural networks are reasonable and can be used for pilot training.
PACS model based on digital watermarking and its core algorithms
NASA Astrophysics Data System (ADS)
Que, Dashun; Wen, Xianlin; Chen, Bi
2009-10-01
PACS model based on digital watermarking is proposed by analyzing medical image features and PACS requirements from the point of view of information security, its core being digital watermarking server and the corresponding processing module. Two kinds of digital watermarking algorithm are studied; one is non-region of interest (NROI) digital watermarking algorithm based on wavelet domain and block-mean, the other is reversible watermarking algorithm on extended difference and pseudo-random matrix. The former belongs to robust lossy watermarking, which embedded in NROI by wavelet provides a good way for protecting the focus area (ROI) of images, and introduction of block-mean approach a good scheme to enhance the anti-attack capability; the latter belongs to fragile lossless watermarking, which has the performance of simple implementation and can realize tamper localization effectively, and the pseudo-random matrix enhances the correlation and security between pixels. Plenty of experimental research has been completed in this paper, including the realization of digital watermarking PACS model, the watermarking processing module and its anti-attack experiments, the digital watermarking server and the network transmission simulating experiments of medical images. Theoretical analysis and experimental results show that the designed PACS model can effectively ensure confidentiality, authenticity, integrity and security of medical image information.
Evolutionary algorithm based structure search for hard ruthenium carbides
NASA Astrophysics Data System (ADS)
Harikrishnan, G.; Ajith, K. M.; Chandra, Sharat; Valsakumar, M. C.
2015-12-01
An exhaustive structure search employing evolutionary algorithm and density functional theory has been carried out for ruthenium carbides, for the three stoichiometries Ru1C1, Ru2C1 and Ru3C1, yielding five lowest energy structures. These include the structures from the two reported syntheses of ruthenium carbides. Their emergence in the present structure search in stoichiometries, unlike the previously reported ones, is plausible in the light of the high temperature required for their synthesis. The mechanical stability and ductile character of all these systems are established by their elastic constants, and the dynamical stability of three of them by the phonon data. Rhombohedral structure ≤ft(R\\bar{3}m\\right) is found to be energetically the most stable one in Ru1C1 stoichiometry and hexagonal structure ≤ft( P\\bar{6}m2\\right) , the most stable in Ru3C1 stoichiometry. RuC-Zinc blende system is a semiconductor with a band gap of 0.618 eV while the other two stable systems are metallic. Employing a semi-empirical model based on the bond strength, the hardness of RuC-Zinc blende is found to be a significantly large value of ~37 GPa while a fairly large value of ~21GPa is obtained for the RuC-Rhombohedral system. The positive formation energies of these systems show that high temperature and possibly high pressure are necessary for their synthesis.
Locally-adaptive and memetic evolutionary pattern search algorithms.
Hart, William E
2003-01-01
Recent convergence analyses of evolutionary pattern search algorithms (EPSAs) have shown that these methods have a weak stationary point convergence theory for a broad class of unconstrained and linearly constrained problems. This paper describes how the convergence theory for EPSAs can be adapted to allow each individual in a population to have its own mutation step length (similar to the design of evolutionary programing and evolution strategies algorithms). These are called locally-adaptive EPSAs (LA-EPSAs) since each individual's mutation step length is independently adapted in different local neighborhoods. The paper also describes a variety of standard formulations of evolutionary algorithms that can be used for LA-EPSAs. Further, it is shown how this convergence theory can be applied to memetic EPSAs, which use local search to refine points within each iteration. PMID:12804096
Biased Randomized Algorithm for Fast Model-Based Diagnosis
NASA Technical Reports Server (NTRS)
Williams, Colin; Vartan, Farrokh
2005-01-01
A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the
Using Evolutionary Algorithms to Induce Oblique Decision Trees
Cantu-Paz, E.; Kamath, C.
2000-01-21
This paper illustrates the application of evolutionary algorithms (EAs) to the problem of oblique decision tree induction. The objectives are to demonstrate that EAs can find classifiers whose accuracy is competitive with other oblique tree construction methods, and that this can be accomplished in a shorter time. Experiments were performed with a (1+1) evolutionary strategy and a simple genetic algorithm on public domain and artificial data sets. The empirical results suggest that the EAs quickly find Competitive classifiers, and that EAs scale up better than traditional methods to the dimensionality of the domain and the number of training instances.
Evolutionary algorithms, simulated annealing, and Tabu search: a comparative study
NASA Astrophysics Data System (ADS)
Youssef, Habib; Sait, Sadiq M.; Adiche, Hakim
1998-10-01
Evolutionary algorithms, simulated annealing (SA), and Tabu Search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are Genetic Algorithms (GA). GA, SA, and TS have been found to be very effective and robust in solving numerous problems from a wide range of application domains.Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. the three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time, and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem was is the floorplanning of very large scale integrated circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation function, which is then used to rate competing solutions.
PARALLELISATION OF THE MODEL-BASED ITERATIVE RECONSTRUCTION ALGORITHM DIRA.
Örtenberg, A; Magnusson, M; Sandborg, M; Alm Carlsson, G; Malusek, A
2016-06-01
New paradigms for parallel programming have been devised to simplify software development on multi-core processors and many-core graphical processing units (GPU). Despite their obvious benefits, the parallelisation of existing computer programs is not an easy task. In this work, the use of the Open Multiprocessing (OpenMP) and Open Computing Language (OpenCL) frameworks is considered for the parallelisation of the model-based iterative reconstruction algorithm DIRA with the aim to significantly shorten the code's execution time. Selected routines were parallelised using OpenMP and OpenCL libraries; some routines were converted from MATLAB to C and optimised. Parallelisation of the code with the OpenMP was easy and resulted in an overall speedup of 15 on a 16-core computer. Parallelisation with OpenCL was more difficult owing to differences between the central processing unit and GPU architectures. The resulting speedup was substantially lower than the theoretical peak performance of the GPU; the cause was explained. PMID:26454270
A Hybrid Evolutionary Algorithm for Wheat Blending Problem
Bonyadi, Mohammad Reza; Michalewicz, Zbigniew; Barone, Luigi
2014-01-01
This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. PMID:24707222
A novel fitness evaluation method for evolutionary algorithms
NASA Astrophysics Data System (ADS)
Wang, Ji-feng; Tang, Ke-zong
2013-03-01
Fitness evaluation is a crucial task in evolutionary algorithms because it can affect the convergence speed and also the quality of the final solution. But these algorithms may require huge computation power for solving nonlinear programming problems. This paper proposes a novel fitness evaluation approach which employs similarity-base learning embedded in a classical differential evolution (SDE) to evaluate all new individuals. Each individual consists of three elements: parameter vector (v), a fitness value (f), and a reliability value(r). The f is calculated using NFEA, and only when the r is below a threshold is the f calculated using true fitness function. Moreover, applying error compensation system to the proposed algorithm further enhances the performance of the algorithm to make r much closer to true fitness value for each new child. Simulation results over a comprehensive set of benchmark functions show that the convergence rate of the proposed algorithm is much faster than much that of the compared algorithms.
A survey on evolutionary algorithm based hybrid intelligence in bioinformatics.
Li, Shan; Kang, Liying; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969
A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics
Li, Shan; Zhao, Xing-Ming
2014-01-01
With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks. PMID:24729969
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.
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 they suggest that EPSAs may be more robust on larger, more complex problems.
Receiver Diversity Combining Using Evolutionary Algorithms in Rayleigh Fading Channel
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
Supervised and unsupervised discretization methods for evolutionary algorithms
Cantu-Paz, E
2001-01-24
This paper introduces simple model-building evolutionary algorithms (EAs) that operate on continuous domains. The algorithms are based on supervised and unsupervised discretization methods that have been used as preprocessing steps in machine learning. The basic idea is to discretize the continuous variables and use the discretization as a simple model of the solutions under consideration. The model is then used to generate new solutions directly, instead of using the usual operators based on sexual recombination and mutation. The algorithms presented here have fewer parameters than traditional and other model-building EAs. They expect that the proposed algorithms that use multivariate models scale up better to the dimensionality of the problem than existing EAs.
Models based on "out-of Kilter" algorithm
NASA Astrophysics Data System (ADS)
Adler, M. J.; Drobot, R.
2012-04-01
In case of many water users along the river stretches, it is very important, in case of low flows and droughty periods to develop an optimization model for water allocation, to cover all needs under certain predefined constraints, depending of the Contingency Plan for drought management. Such a program was developed during the implementation of the WATMAN Project, in Romania (WATMAN Project, 2005-2006, USTDA) for Arges-Dambovita-Ialomita Basins water transfers. This good practice was proposed for WATER CoRe Project- Good Practice Handbook for Drought Management, (InterregIVC, 2011), to be applied for the European Regions. Two types of simulation-optimization models based on an improved version of out-of-kilter algorithm as optimization technique have been developed and used in Romania: • models for founding of the short-term operation of a WMS, • models generically named SIMOPT that aim to the analysis of long-term WMS operation and have as the main results the statistical WMS functional parameters. A real WMS is modeled by an arcs-nodes network so the real WMS operation problem becomes a problem of flows in networks. The nodes and oriented arcs as well as their characteristics such as lower and upper limits and associated costs are the direct analog of the physical and operational WMS characteristics. Arcs represent both physical and conventional elements of WMS such as river branches, channels or pipes, water user demands or other water management requirements, trenches of water reservoirs volumes, water levels in channels or rivers, nodes are junctions of at least two arcs and stand for locations of lakes or water reservoirs and/or confluences of river branches, water withdrawal or wastewater discharge points, etc. Quantitative features of water resources, water users and water reservoirs or other water works are expressed as constraints of non-violating the lower and upper limits assigned on arcs. Options of WMS functioning i.e. water retention/discharge in
Filter model based dwell time algorithm for ion beam figuring
NASA Astrophysics Data System (ADS)
Li, Yun; Xing, Tingwen; Jia, Xin; Wei, Haoming
2010-10-01
The process of Ion Beam Figuring (IBF) can be described by a two-dimensional convolution equation which including dwell time. Solving the dwell time is a key problem in IBF. Theoretically, the dwell time can be solved from a two-dimensional deconvolution. However, it is often ill-posed]; the suitable solution of that is hard to get. In this article, a dwell time algorithm is proposed, depending on the characters of IBF. Usually, the Beam Removal Function (BRF) in IBF is Gaussian, which can be regarded as a headstand Gaussian filter. In its stop-band, the filter has various filtering abilities for various frequencies. The dwell time algorithm proposed in this article is just based on this concept. The Curved Surface Smooth Extension (CSSE) method and Fast Fourier Transform (FFT) algorithm are also used. The simulation results show that this algorithm is high precision, effective, and suitable for actual application.
Multi-objective Job Shop Rescheduling with Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Hao, Xinchang; Gen, Mitsuo
In current manufacturing systems, production processes and management are involved in many unexpected events and new requirements emerging constantly. This dynamic environment implies that operation rescheduling is usually indispensable. A wide variety of procedures and heuristics has been developed to improve the quality of rescheduling. However, most proposed approaches are derived usually with respect to simplified assumptions. As a consequence, these approaches might be inconsistent with the actual requirements in a real production environment, i.e., they are often unsuitable and inflexible to respond efficiently to the frequent changes. In this paper, a multi-objective job shop rescheduling problem (moJSRP) is formulated to improve the practical application of rescheduling. To solve the moJSRP model, an evolutionary algorithm is designed, in which a random key-based representation and interactive adaptive-weight (i-awEA) fitness assignment are embedded. To verify the effectiveness, the proposed algorithm has been compared with other apporaches and benchmarks on the robustness of moJRP optimziation. The comparison results show that iAWGA-A is better than weighted fitness method in terms of effectiveness and stability. Simlarly, iAWGA-A also outperforms other well stability approachessuch as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm2 (SPEA2).
Optimal classification of standoff bioaerosol measurements using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Nyhavn, Ragnhild; Moen, Hans J. F.; Farsund, Øystein; Rustad, Gunnar
2011-05-01
Early warning systems based on standoff detection of biological aerosols require real-time signal processing of a large quantity of high-dimensional data, challenging the systems efficiency in terms of both computational complexity and classification accuracy. Hence, optimal feature selection is essential in forming a stable and efficient classification system. This involves finding optimal signal processing parameters, characteristic spectral frequencies and other data transformations in large magnitude variable space, stating the need for an efficient and smart search algorithm. Evolutionary algorithms are population-based optimization methods inspired by Darwinian evolutionary theory. These methods focus on application of selection, mutation and recombination on a population of competing solutions and optimize this set by evolving the population of solutions for each generation. We have employed genetic algorithms in the search for optimal feature selection and signal processing parameters for classification of biological agents. The experimental data were achieved with a spectrally resolved lidar based on ultraviolet laser induced fluorescence, and included several releases of 5 common simulants. The genetic algorithm outperform benchmark methods involving analytic, sequential and random methods like support vector machines, Fisher's linear discriminant and principal component analysis, with significantly improved classification accuracy compared to the best classical method.
Virus evolutionary genetic algorithm for task collaboration of logistics distribution
NASA Astrophysics Data System (ADS)
Ning, Fanghua; Chen, Zichen; Xiong, Li
2005-12-01
In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.
Logit Model based Performance Analysis of an Optimization Algorithm
NASA Astrophysics Data System (ADS)
Hernández, J. A.; Ospina, J. D.; Villada, D.
2011-09-01
In this paper, the performance of the Multi Dynamics Algorithm for Global Optimization (MAGO) is studied through simulation using five standard test functions. To guarantee that the algorithm converges to a global optimum, a set of experiments searching for the best combination between the only two MAGO parameters -number of iterations and number of potential solutions, are considered. These parameters are sequentially varied, while increasing the dimension of several test functions, and performance curves were obtained. The MAGO was originally designed to perform well with small populations; therefore, the self-adaptation task with small populations is more challenging while the problem dimension is higher. The results showed that the convergence probability to an optimal solution increases according to growing patterns of the number of iterations and the number of potential solutions. However, the success rates slow down when the dimension of the problem escalates. Logit Model is used to determine the mutual effects between the parameters of the algorithm.
Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation
NASA Astrophysics Data System (ADS)
Barukčić, Marinko; Nikolovski, Srete; Jović, Franjo
2010-11-01
The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.
The algorithmic anatomy of model-based evaluation
Daw, Nathaniel D.; Dayan, Peter
2014-01-01
Despite many debates in the first half of the twentieth century, it is now largely a truism that humans and other animals build models of their environments and use them for prediction and control. However, model-based (MB) reasoning presents severe computational challenges. Alternative, computationally simpler, model-free (MF) schemes have been suggested in the reinforcement learning literature, and have afforded influential accounts of behavioural and neural data. Here, we study the realization of MB calculations, and the ways that this might be woven together with MF values and evaluation methods. There are as yet mostly only hints in the literature as to the resulting tapestry, so we offer more preview than review. PMID:25267820
Evolutionary pattern search algorithms for unconstrained and linearly constrained optimization
HART,WILLIAM E.
2000-06-01
The authors describe a convergence theory for evolutionary pattern search algorithms (EPSAs) on a broad class of unconstrained and linearly constrained problems. EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. The analysis significantly extends the previous convergence theory for EPSAs. The analysis applies to a broader class of EPSAs,and it applies to problems that are nonsmooth, have unbounded objective functions, and which are linearly constrained. Further, they describe a modest change to the algorithmic framework of EPSAs for which a non-probabilistic convergence theory applies. These analyses are also noteworthy because they are considerably simpler than previous analyses of EPSAs.
Characterization of atmospheric contaminant sources using adaptive evolutionary algorithms
NASA Astrophysics Data System (ADS)
Cervone, Guido; Franzese, Pasquale; Grajdeanu, Adrian
2010-10-01
The characteristics of an unknown source of emissions in the atmosphere are identified using an Adaptive Evolutionary Strategy (AES) methodology based on ground concentration measurements and a Gaussian plume model. The AES methodology selects an initial set of source characteristics including position, size, mass emission rate, and wind direction, from which a forward dispersion simulation is performed. The error between the simulated concentrations from the tentative source and the observed ground measurements is calculated. Then the AES algorithm prescribes the next tentative set of source characteristics. The iteration proceeds towards minimum error, corresponding to convergence towards the real source. The proposed methodology was used to identify the source characteristics of 12 releases from the Prairie Grass field experiment of dispersion, two for each atmospheric stability class, ranging from very unstable to stable atmosphere. The AES algorithm was found to have advantages over a simple canonical ES and a Monte Carlo (MC) method which were used as benchmarks.
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.
On the application of evolutionary pattern search algorithms
Hart, W.E.
1997-02-01
This paper presents an experimental evaluation of evolutionary pattern search algorithms (EPSAs). Our experimental evaluation of EPSAs indicates that EPSAs can achieve similar performance to EAs on challenging global optimization problems. Additionally, we describe a stopping rule for EPSAs that reliably terminated them near a stationary point of the objective function. The ability for EPSAs to reliably terminate near stationary points offers a practical advantage over other EAs, which are typically stopped by heuristic stopping rules or simple bounds on the number of iterations. Our experiments also illustrate how the rate of the crossover operator can influence the tradeoff between the number of iterations before termination and the quality of the solution found by an EPSA.
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.
A hierarchical evolutionary algorithm for multiobjective optimization in IMRT
Holdsworth, Clay; Kim, Minsun; Liao, Jay; Phillips, Mark H.
2010-01-01
Purpose: The current inverse planning methods for intensity modulated radiation therapy (IMRT) are limited because they are not designed to explore the trade-offs between the competing objectives of tumor and normal tissues. The goal was to develop an efficient multiobjective optimization algorithm that was flexible enough to handle any form of objective function and that resulted in a set of Pareto optimal plans. Methods: A hierarchical evolutionary multiobjective algorithm designed to quickly generate a small diverse Pareto optimal set of IMRT plans that meet all clinical constraints and reflect the optimal trade-offs in any radiation therapy plan was developed. The top level of the hierarchical algorithm is a multiobjective evolutionary algorithm (MOEA). The genes of the individuals generated in the MOEA are the parameters that define the penalty function minimized during an accelerated deterministic IMRT optimization that represents the bottom level of the hierarchy. The MOEA incorporates clinical criteria to restrict the search space through protocol objectives and then uses Pareto optimality among the fitness objectives to select individuals. The population size is not fixed, but a specialized niche effect, domination advantage, is used to control the population and plan diversity. The number of fitness objectives is kept to a minimum for greater selective pressure, but the number of genes is expanded for flexibility that allows a better approximation of the Pareto front. Results: The MOEA improvements were evaluated for two example prostate cases with one target and two organs at risk (OARs). The population of plans generated by the modified MOEA was closer to the Pareto front than populations of plans generated using a standard genetic algorithm package. Statistical significance of the method was established by compiling the results of 25 multiobjective optimizations using each method. From these sets of 12–15 plans, any random plan selected from a MOEA
Multi-objective evolutionary algorithm for operating parallel reservoir system
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John
2009-10-01
SummaryThis paper applies a multi-objective evolutionary algorithm, the non-dominated sorting genetic algorithm (NSGA-II), to examine the operations of a multi-reservoir system in Taiwan. The Feitsui and Shihmen reservoirs are the most important water supply reservoirs in Northern Taiwan supplying the domestic and industrial water supply needs for over 7 million residents. A daily operational simulation model is developed to guide the releases of the reservoir system and then to calculate the shortage indices (SI) of both reservoirs over a long-term simulation period. The NSGA-II is used to minimize the SI values through identification of optimal joint operating strategies. Based on a 49 year data set, we demonstrate that better operational strategies would reduce shortage indices for both reservoirs. The results indicate that the NSGA-II provides a promising approach. The pareto-front optimal solutions identified operational compromises for the two reservoirs that would be expected to improve joint operations.
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.
The hierarchical fair competition (HFC) framework for sustainable evolutionary algorithms.
Hu, Jianjun; Goodman, Erik; Seo, Kisung; Fan, Zhun; Rosenberg, Rondal
2005-01-01
Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a
An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy.
Zhang, Yu-Xian; Qian, Xiao-Yi; Peng, Hui-Deng; Wang, Jian-Hui
2016-01-01
For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And H ε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy. PMID:27057159
An Allele Real-Coded Quantum Evolutionary Algorithm Based on Hybrid Updating Strategy
Zhang, Yu-Xian; Qian, Xiao-Yi; Peng, Hui-Deng; Wang, Jian-Hui
2016-01-01
For improving convergence rate and preventing prematurity in quantum evolutionary algorithm, an allele real-coded quantum evolutionary algorithm based on hybrid updating strategy is presented. The real variables are coded with probability superposition of allele. A hybrid updating strategy balancing the global search and local search is presented in which the superior allele is defined. On the basis of superior allele and inferior allele, a guided evolutionary process as well as updating allele with variable scale contraction is adopted. And Hε gate is introduced to prevent prematurity. Furthermore, the global convergence of proposed algorithm is proved by Markov chain. Finally, the proposed algorithm is compared with genetic algorithm, quantum evolutionary algorithm, and double chains quantum genetic algorithm in solving continuous optimization problem, and the experimental results verify the advantages on convergence rate and search accuracy. PMID:27057159
GX-Means: A model-based divide and merge algorithm for geospatial image clustering
Vatsavai, Raju; Symons, Christopher T; Chandola, Varun; Jun, Goo
2011-01-01
One of the practical issues in clustering is the specification of the appropriate number of clusters, which is not obvious when analyzing geospatial datasets, partly because they are huge (both in size and spatial extent) and high dimensional. In this paper we present a computationally efficient model-based split and merge clustering algorithm that incrementally finds model parameters and the number of clusters. Additionally, we attempt to provide insights into this problem and other data mining challenges that are encountered when clustering geospatial data. The basic algorithm we present is similar to the G-means and X-means algorithms; however, our proposed approach avoids certain limitations of these well-known clustering algorithms that are pertinent when dealing with geospatial data. We compare the performance of our approach with the G-means and X-means algorithms. Experimental evaluation on simulated data and on multispectral and hyperspectral remotely sensed image data demonstrates the effectiveness of our algorithm.
A multiagent evolutionary algorithm for constraint satisfaction problems.
Liu, Jing; Zhong, Weicai; Jiao, Licheng
2006-02-01
With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, we divide CSPs into two types, namely, permutation CSPs and nonpermutation CSPs. According to their characteristics, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that the multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. Theoretical analyzes show that MAEA-CSPs has a linear space complexity and converges to the global optimum. The first part of the experiments uses 250 benchmark binary CSPs and 79 graph coloring problems from the DIMACS challenge to test the performance of MAEA-CSPs for nonpermutation CSPs. MAEA-CSPs is compared with six well-defined algorithms and the effect of the parameters is analyzed systematically. The second part of the experiments uses a classical CSP, n-queen problems, and a more practical case, job-shop scheduling problems (JSPs), to test the performance of MAEA-CSPs for permutation CSPs. The scalability of MAEA-CSPs along n for n-queen problems is studied with great care. The results show that MAEA-CSPs achieves good performance when n increases from 10(4) to 10(7), and has a linear time complexity. Even for 10(7)-queen problems, MAEA-CSPs finds the solutions by only 150 seconds. For JSPs, 59 benchmark problems are used, and good performance is also obtained. PMID:16468566
Optimizing quantum gas production by an evolutionary algorithm
NASA Astrophysics Data System (ADS)
Lausch, T.; Hohmann, M.; Kindermann, F.; Mayer, D.; Schmidt, F.; Widera, A.
2016-05-01
We report on the application of an evolutionary algorithm (EA) to enhance performance of an ultra-cold quantum gas experiment. The production of a ^{87}rubidium Bose-Einstein condensate (BEC) can be divided into fundamental cooling steps, specifically magneto-optical trapping of cold atoms, loading of atoms to a far-detuned crossed dipole trap, and finally the process of evaporative cooling. The EA is applied separately for each of these steps with a particular definition for the feedback, the so-called fitness. We discuss the principles of an EA and implement an enhancement called differential evolution. Analyzing the reasons for the EA to improve, e.g., the atomic loading rates and increase the BEC phase-space density, yields an optimal parameter set for the BEC production and enables us to reduce the BEC production time significantly. Furthermore, we focus on how additional information about the experiment and optimization possibilities can be extracted and how the correlations revealed allow for further improvement. Our results illustrate that EAs are powerful optimization tools for complex experiments and exemplify that the application yields useful information on the dependence of these experiments on the optimized parameters.
EVO—Evolutionary algorithm for crystal structure prediction
NASA Astrophysics Data System (ADS)
Bahmann, Silvia; Kortus, Jens
2013-06-01
We present EVO—an evolution strategy designed for crystal structure search and prediction. The concept and main features of biological evolution such as creation of diversity and survival of the fittest have been transferred to crystal structure prediction. EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it. Running EVO with different numbers of carbon atoms per unit cell yields graphite as the lowest energy structure as well as a diamond-like structure, both in one run. Our implementation also supports the search for 2D structures and was able to find a boron sheet with structural features so far not considered in literature. Program summaryProgram title: EVO Catalogue identifier: AEOZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License version 3 No. of lines in distributed program, including test data, etc.: 23488 No. of bytes in distributed program, including test data, etc.: 1830122 Distribution format: tar.gz Programming language: Python. Computer: No limitations known. Operating system: Linux. RAM: Negligible compared to the requirements of the electronic structure programs used Classification: 7.8. External routines: Quantum ESPRESSO (http://www.quantum-espresso.org/), GULP (https://projects.ivec.org/gulp/) Nature of problem: Crystal structure search is a global optimisation problem in 3N+3 dimensions where N is the number of atoms in the unit cell. The high dimensional search space is accompanied by an unknown energy landscape. Solution method: Evolutionary algorithms transfer the main features of biological evolution to use them in global searches. The combination of the "survival of the fittest" (deterministic) and the
NASA Astrophysics Data System (ADS)
Marwati, Rini; Yulianti, Kartika; Pangestu, Herny Wulandari
2016-02-01
A fuzzy evolutionary algorithm is an integration of an evolutionary algorithm and a fuzzy system. In this paper, we present an application of a genetic algorithm to a fuzzy evolutionary algorithm to detect and to solve chromosomes conflict. A chromosome conflict is identified by existence of any two genes in a chromosome that has the same values as two genes in another chromosome. Based on this approach, we construct an algorithm to solve a lecture scheduling problem. Time codes, lecture codes, lecturer codes, and room codes are defined as genes. They are collected to become chromosomes. As a result, the conflicted schedule turns into chromosomes conflict. Built in the Delphi program, results show that the conflicted lecture schedule problem is solvable by this algorithm.
Allhoff, K. T.; Ritterskamp, D.; Rall, B. C.; Drossel, B.; Guill, C.
2015-01-01
The networks of predator-prey interactions in ecological systems are remarkably complex, but nevertheless surprisingly stable in terms of long term persistence of the system as a whole. In order to understand the mechanism driving the complexity and stability of such food webs, we developed an eco-evolutionary model in which new species emerge as modifications of existing ones and dynamic ecological interactions determine which species are viable. The food-web structure thereby emerges from the dynamical interplay between speciation and trophic interactions. The proposed model is less abstract than earlier evolutionary food web models in the sense that all three evolving traits have a clear biological meaning, namely the average body mass of the individuals, the preferred prey body mass, and the width of their potential prey body mass spectrum. We observed networks with a wide range of sizes and structures and high similarity to natural food webs. The model networks exhibit a continuous species turnover, but massive extinction waves that affect more than 50% of the network are not observed. PMID:26042870
NASA Astrophysics Data System (ADS)
Allhoff, K. T.; Ritterskamp, D.; Rall, B. C.; Drossel, B.; Guill, C.
2015-06-01
The networks of predator-prey interactions in ecological systems are remarkably complex, but nevertheless surprisingly stable in terms of long term persistence of the system as a whole. In order to understand the mechanism driving the complexity and stability of such food webs, we developed an eco-evolutionary model in which new species emerge as modifications of existing ones and dynamic ecological interactions determine which species are viable. The food-web structure thereby emerges from the dynamical interplay between speciation and trophic interactions. The proposed model is less abstract than earlier evolutionary food web models in the sense that all three evolving traits have a clear biological meaning, namely the average body mass of the individuals, the preferred prey body mass, and the width of their potential prey body mass spectrum. We observed networks with a wide range of sizes and structures and high similarity to natural food webs. The model networks exhibit a continuous species turnover, but massive extinction waves that affect more than 50% of the network are not observed.
NASA Astrophysics Data System (ADS)
Khair, Joseph Daniel
Communication requirements and demands on deployed systems are increasing daily. This increase is due to the desire for more capability, but also, due to the changing landscape of threats on remote vehicles. As such, it is important that we continue to find new and innovative ways to transmit data to and from these remote systems, consistent with this changing landscape. Specifically, this research shows that data can be transmitted to a remote system effectively and efficiently with a model-based approach using real-time updates, called Algorithmically Corrected Model-based Technique (ACMBT), resulting in substantial savings in communications overhead. To demonstrate this model-based data transmission technique, a hardware-based test fixture was designed and built. Execution and analysis software was created to perform a series of characterizations demonstrating the effectiveness of the new transmission method. The new approach was compared to a traditional transmission approach in the same environment, and the results were analyzed and presented. A Figure of Merit (FOM) was devised and presented to allow standardized comparison of traditional and proposed data transmission methodologies alongside bandwidth utilization metrics. The results of this research have successfully shown the model-based technique to be feasible. Additionally, this research has opened the trade space for future discussion and implementation of this technique.
NASA Astrophysics Data System (ADS)
Tang, Zhili
2016-06-01
This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.
Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding
Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the “dummy node” is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389
Path planning using a hybrid evolutionary algorithm based on tree structure encoding.
Ju, Ming-Yi; Wang, Siao-En; Guo, Jian-Horn
2014-01-01
A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the "dummy node" is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. PMID:24971389
Development of model-based fault diagnosis algorithms for MASCOTTE cryogenic test bench
NASA Astrophysics Data System (ADS)
Iannetti, A.; Marzat, J.; Piet-Lahanier, H.; Ordonneau, G.; Vingert, L.
2014-12-01
This article describes the on-going results of a fault diagnosis benchmark for a cryogenic rocket engine demonstrator. The benchmark consists in the use of classical model- based fault diagnosis methods to monitor the status of the cooling circuit of the MASCOTTE cryogenic bench. The algorithms developed are validated on real data from the last 2014 firing campaign (ATAC campaign). The objective of this demonstration is to find practical diagnosis alternatives to classical redline providing more flexible means of data exploitation in real time and for post processing.
Optimization of aeroelastic composite structures using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Manan, A.; Vio, G. A.; Harmin, M. Y.; Cooper, J. E.
2010-02-01
The flutter/divergence speed of a simple rectangular composite wing is maximized through the use of different ply orientations. Four different biologically inspired optimization algorithms (binary genetic algorithm, continuous genetic algorithm, particle swarm optimization, and ant colony optimization) and a simple meta-modeling approach are employed statistically on the same problem set. In terms of the best flutter speed, it was found that similar results were obtained using all of the methods, although the continuous methods gave better answers than the discrete methods. When the results were considered in terms of the statistical variation between different solutions, ant colony optimization gave estimates with much less scatter.
Evolutionary Algorithms Approach to the Solution of Damage Detection Problems
NASA Astrophysics Data System (ADS)
Salazar Pinto, Pedro Yoajim; Begambre, Oscar
2010-09-01
In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.
A multiobjective evolutionary algorithm to find community structures based on affinity propagation
NASA Astrophysics Data System (ADS)
Shang, Ronghua; Luo, Shuang; Zhang, Weitong; Stolkin, Rustam; Jiao, Licheng
2016-07-01
Community detection plays an important role in reflecting and understanding the topological structure of complex networks, and can be used to help mine the potential information in networks. This paper presents a Multiobjective Evolutionary Algorithm based on Affinity Propagation (APMOEA) which improves the accuracy of community detection. Firstly, APMOEA takes the method of affinity propagation (AP) to initially divide the network. To accelerate its convergence, the multiobjective evolutionary algorithm selects nondominated solutions from the preliminary partitioning results as its initial population. Secondly, the multiobjective evolutionary algorithm finds solutions approximating the true Pareto optimal front through constantly selecting nondominated solutions from the population after crossover and mutation in iterations, which overcomes the tendency of data clustering methods to fall into local optima. Finally, APMOEA uses an elitist strategy, called "external archive", to prevent degeneration during the process of searching using the multiobjective evolutionary algorithm. According to this strategy, the preliminary partitioning results obtained by AP will be archived and participate in the final selection of Pareto-optimal solutions. Experiments on benchmark test data, including both computer-generated networks and eight real-world networks, show that the proposed algorithm achieves more accurate results and has faster convergence speed compared with seven other state-of-art algorithms.
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.
Predicting land cover using GIS, Bayesian and evolutionary algorithm methods.
Aitkenhead, M J; Aalders, I H
2009-01-01
Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables. PMID:18079039
Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks
Abbasi, Mohammadjavad; Bin Abd Latiff, Muhammad Shafie
2014-01-01
Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm. PMID:24693247
Design of synthetic biological logic circuits based on evolutionary algorithm.
Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei
2013-08-01
The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose. PMID:23919952
A Comparative Study between Migration and Pair-Swap on Quantum-Inspired Evolutionary Algorithm
NASA Astrophysics Data System (ADS)
Imabeppu, Takahiro; Ono, Satoshi; Morishige, Ryota; Kurose, Motoyoshi; Nakayama, Shigeru
Quantum-inspired Evolutionary Algorithm (QEA) has been proposed as one of stochastic algorithms of evolutionary computation instead of a quantum algorithm. The authors have proposed Quantum-inspired Evolutionary Algorithm based on Pair Swap (QEAPS), which uses pair swap operator and does not group individuals in order to simplify QEA and reduce parameters in QEA. QEA and QEAPS imitationally use quantum bits as genes and superposition states in quantum computation. QEAPS has shown better search performance than QEA on knapsack problem, while eliminating parameters about immigration intervals and number of groups. However, QEAPS still has a parameter in common with QEA, a rotation angle unit, which is uncommon among other evolutionary computation algorithms. The rotation angle unit deeply affects exploitation and exploration control in QEA, but it has been unclear how the parameter influences QEAPS to behave. This paper aims to show that QEAPS involves few parameters and even those parameters can be adjusted easily. Experimental results, in knapsack problem and number partitioning problem which have different characteristics, have shown that QEAPS is competitive with other metaheuristics in search performance, and that QEAPS is robust against the parameter configuration and problem characteristics.
Evolutionary Processes in the Development of Errors in Subtraction Algorithms
ERIC Educational Resources Information Center
Fernandez, Ricardo Lopez; Garcia, Ana B. Sanchez
2008-01-01
The study of errors made in subtraction is a research subject approached from different theoretical premises that affect different components of the algorithmic process as triggers of their generation. In the following research an attempt has been made to investigate the typology and nature of errors which occur in subtractions and their evolution…
First principles prediction of amorphous phases using evolutionary algorithms.
Nahas, Suhas; Gaur, Anshu; Bhowmick, Somnath
2016-07-01
We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ∼2% of those reported by ab initio MD calculations and experimental studies. PMID:27394098
First principles prediction of amorphous phases using evolutionary algorithms
NASA Astrophysics Data System (ADS)
Nahas, Suhas; Gaur, Anshu; Bhowmick, Somnath
2016-07-01
We discuss the efficacy of evolutionary method for the purpose of structural analysis of amorphous solids. At present, ab initio molecular dynamics (MD) based melt-quench technique is used and this deterministic approach has proven to be successful to study amorphous materials. We show that a stochastic approach motivated by Darwinian evolution can also be used to simulate amorphous structures. Applying this method, in conjunction with density functional theory based electronic, ionic and cell relaxation, we re-investigate two well known amorphous semiconductors, namely silicon and indium gallium zinc oxide. We find that characteristic structural parameters like average bond length and bond angle are within ˜2% of those reported by ab initio MD calculations and experimental studies.
NASA Astrophysics Data System (ADS)
Xiao, Zhongliang
2012-04-01
In this paper, we set up a mathematical model to solve the problem of airport ground services. In this model, we set objective function of cost and time, and the purpose is making it minimized. Base on the analysis of scheduling characteristic, we use the multi-population co-evolutionary Memetic algorithm (MAMC) which is with the elitist strategy to realize the model. From the result we can see that our algorithm is better than the genetic algorithm in this problem and we can see that our algorithm is convergence. So we can summarize that it can be a better optimization to airport ground services problem.
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.
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
Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.
Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard
2012-06-01
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. PMID:22697525
Simulation of Biochemical Pathway Adaptability Using Evolutionary Algorithms
Bosl, W J
2005-01-26
The systems approach to genomics seeks quantitative and predictive descriptions of cells and organisms. However, both the theoretical and experimental methods necessary for such studies still need to be developed. We are far from understanding even the simplest collective behavior of biomolecules, cells or organisms. A key aspect to all biological problems, including environmental microbiology, evolution of infectious diseases, and the adaptation of cancer cells is the evolvability of genomes. This is particularly important for Genomes to Life missions, which tend to focus on the prospect of engineering microorganisms to achieve desired goals in environmental remediation and climate change mitigation, and energy production. All of these will require quantitative tools for understanding the evolvability of organisms. Laboratory biodefense goals will need quantitative tools for predicting complicated host-pathogen interactions and finding counter-measures. In this project, we seek to develop methods to simulate how external and internal signals cause the genetic apparatus to adapt and organize to produce complex biochemical systems to achieve survival. This project is specifically directed toward building a computational methodology for simulating the adaptability of genomes. This project investigated the feasibility of using a novel quantitative approach to studying the adaptability of genomes and biochemical pathways. This effort was intended to be the preliminary part of a larger, long-term effort between key leaders in computational and systems biology at Harvard University and LLNL, with Dr. Bosl as the lead PI. Scientific goals for the long-term project include the development and testing of new hypotheses to explain the observed adaptability of yeast biochemical pathways when the myosin-II gene is deleted and the development of a novel data-driven evolutionary computation as a way to connect exploratory computational simulation with hypothesis
On Polymorphic Circuits and Their Design Using Evolutionary Algorithms
NASA Technical Reports Server (NTRS)
Stoica, Adrian; Zebulum, Ricardo; Keymeulen, Didier; Lohn, Jason; Clancy, Daniel (Technical Monitor)
2002-01-01
This paper introduces the concept of polymorphic electronics (polytronics) - referring to electronics with superimposed built-in functionality. A function change does not require switches/reconfiguration as in traditional approaches. Instead the change comes from modifications in the characteristics of devices involved in the circuit, in response to controls such as temperature, power supply voltage (VDD), control signals, light, etc. The paper illustrates polytronic circuits in which the control is done by temperature, morphing signals, and VDD respectively. Polytronic circuits are obtained by evolutionary design/evolvable hardware techniques. These techniques are ideal for the polytronics design, a new area that lacks design guidelines, know-how,- yet the requirements/objectives are easy to specify and test. The circuits are evolved/synthesized in two different modes. The first mode explores an unstructured space, in which transistors can be interconnected freely in any arrangement (in simulations only). The second mode uses a Field Programmable Transistor Array (FPTA) model, and the circuit topology is sought as a mapping onto a programmable architecture (these experiments are performed both in simulations and on FPTA chips). The experiments demonstrated the synthesis. of polytronic circuits by evolution. The capacity of storing/hiding "extra" functions provides for watermark/invisible functionality, thus polytronics may find uses in intelligence/security applications.
Kwan, Mei-Po; Xiao, Ningchuan; Ding, Guoxiang
2015-01-01
Due to the complexity and multidimensional characteristics of human activities, assessing the similarity of human activity patterns and classifying individuals with similar patterns remains highly challenging. This paper presents a new and unique methodology for evaluating the similarity among individual activity patterns. It conceptualizes multidimensional sequence alignment (MDSA) as a multiobjective optimization problem, and solves this problem with an evolutionary algorithm. The study utilizes sequence alignment to code multiple facets of human activities into multidimensional sequences, and to treat similarity assessment as a multiobjective optimization problem that aims to minimize the alignment cost for all dimensions simultaneously. A multiobjective optimization evolutionary algorithm (MOEA) is used to generate a diverse set of optimal or near-optimal alignment solutions. Evolutionary operators are specifically designed for this problem, and a local search method also is incorporated to improve the search ability of the algorithm. We demonstrate the effectiveness of our method by comparing it with a popular existing method called ClustalG using a set of 50 sequences. The results indicate that our method outperforms the existing method for most of our selected cases. The multiobjective evolutionary algorithm presented in this paper provides an effective approach for assessing activity pattern similarity, and a foundation for identifying distinctive groups of individuals with similar activity patterns. PMID:26190858
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 task 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.,
NASA Astrophysics Data System (ADS)
Tang, Y.; Reed, P.; Wagner, T.
2005-12-01
This study provides the first comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools- relative effectiveness in calibrating integrated hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (??-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study assesses the performances of these three evolutionary multiobjective algorithms using a formal metrics-based methodology. This study uses two phases of testing to compare the algorithms- performances. In the first phase, this study uses a suite of standard computer science test problems to validate the algorithms- abilities to perform global search effectively, efficiently, and reliably. The second phase of testing compares the algorithms- performances for a computationally intensive multiobjective integrated hydrologic model calibration application for the Shale Hills watershed located within the Valley and Ridge province of the Susquehanna River Basin in north central Pennsylvania. The Shale Hills test case demonstrates the computational challenges posed by the paradigmatic shift in environmental and water resources simulation tools towards highly nonlinear physical models that seek to holistically simulate the water cycle. Specifically, the Shale Hills test case is an excellent test for the three EMO algorithms due to the large number of continuous decision variables, the increased computational demands posed by the simulating fully-coupled hydrologic processes, and the highly multimodal nature of the search space. A challenge and contribution of this work is the development of a comprehensive methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques.
NASA Astrophysics Data System (ADS)
Patz, Mark David
A non-intrusive buried object classifier for a ground penetrating radar (GPR) system is developed. Various GPR data sets and the implemented processing are described. A model based inversion algorithm that utilizes correlation methodology for target classification is introduced. Experimental data was collected with a continuous wave GPR. Synthetic data was generated with a newly developed software package that implements mathematical models to predict the electromagnetic returns from an underground object. Sample targets and geometries were chosen to produce nine configurations/scenarios for analysis. The real measurement sets for each configuration and the synthetic sets for a family of similar configurations were imaged with the same state-of-the-art signal processing algorithms. The imaged results for the real data measurements were correlated with the imaged results for the synthetic data sets to produce performance measurements, thus producing a procedure that provides a non-invasive assessment of the object and medium determined by the synthetic data set that maximally correlated with the real data return. Synthetic results and experiment results showed good correlations. For the synthetic data, a mathematical model was developed for electromagnetic returns from an object shape (i.e., cylinder, parallelepiped, sphere) composed of a uniform construction (i.e., metal, wood, plastic, clay) within a uniform dielectric material (i.e., air, sand, loam, clay, water). This model was then implemented within a software package, thus providing the ability to generate simulated measurements from any combination of object, construction, and dielectric.
NASA Astrophysics Data System (ADS)
Della Mora, S.; Boschi, L.; Becker, T. W.; Giardini, D.
2010-12-01
The wavelength spectrum of three-dimensional (3D) heterogeneity naturally reflects the nature of Earth dynamics, and is in its own right an important constraint for geodynamical modeling. The Earth's spectrum has been usually evaluated indirectly, on the basis of previously derived tomographic models. If the geographic distribution of seismic heterogeneities is neglected, however, one can invert global seismic data directly to find the spectrum of the Earth. Inverting for the spectrum is in principle (fewer unknowns) cheaper and robust than inverting for the 3D structure of a planet: this should allow us to constrain planetary structure at smaller scales than by current 3D models. Based on the work of Gudmundsson and coworkers in the early 1990s, we have developed a linear algorithm for surface waves. The spectra we obtain are in qualitative agreement with results from 3D tomography, but the resolving power is generally lower, due to the simplifications required to linearise the ``spectral'' inversion. To overcome this problem, we performed full nonlinear inversions of synthetically generated and real datasets, and compare the obtained spectra with the input and tomographic models respectively. The inversions are calculated on a distributed memory parallel nodes cluster, employing the MPI package. An evolutionary strategy approach is used to explore the parameter space, using the PIKAIA software. The first preliminary results show a resolving power higher than that of linearised inversion. This confirms that the approximations required in the linear formulation affect the solution quality, and suggests that the nonlinear approach might effectively help to constrain the heterogeneity spectrum more robustly than currently possible.
Hybrid evolutionary algorithms for network-centric command and control
NASA Astrophysics Data System (ADS)
Khosla, Deepak; Nichols, Tom
2006-05-01
Network-centric force optimization is the problem of threat engagement and dynamic Weapon-Target Allocation (WTA) across the force. The goal is to allocate and schedule defensive weapon resources over a given period of time so as to achieve certain battle management objectives subject to resource and temporal constraints. The problem addresses in this paper is one of dynamic WTA and involves optimization across both resources (weapons) and time. We henceforth refer to this problem as the Weapon Allocation and Scheduling problem (WAS). This paper addresses and solves the WAS problem for two separate battle management objectives: (1) Threat Kill Maximization (TKM), and (2) Asset Survival Maximization (ASM). Henceforth, the WAS problems for the above objectives are referred to as the WAS-TKM and WAS-ASM, respectively. Both WAS problems are NP-complete problem and belong to a class of multiple-resource-constrained optimal scheduling problems. While the above objectives appear to be intuitively similar from a battle management perspective, the two optimal scheduling problems are quite different in their complexity. We present a hybrid genetic algorithm (GA) that is a combination of a traditional genetic algorithm and a simulated annealing-type algorithm for solving these problems. The hybrid GA approach proposed here uses a simulated annealing-type heuristics to compute the fitness of a GA-selected population. This step also optimizes the temporal dimension (scheduling) under resource and temporal constraints and is significantly different for the WAS-TKM and WAS-ASM problems. The proposed method provides schedules that are near optimal in short cycle times and have minimal perturbation from one cycle to the next.
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.
Evolutionary Design of Rule Changing Artificial Society Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Wu, Yun; Kanoh, Hitoshi
Socioeconomic phenomena, cultural progress and political organization have recently been studied by creating artificial societies consisting of simulated agents. In this paper we propose a new method to design action rules of agents in artificial society that can realize given requests using genetic algorithms (GAs). In this paper we propose an efficient method for designing the action rules of agents that will constitute an artificial society that meets a specified demand by using a GAs. In the proposed method, each chromosome in the GA population represents a candidate set of action rules and the number of rule iterations. While a conventional method applies distinct rules in order of precedence, the present method applies a set of rules repeatedly for a certain period. The present method is aiming at both firm evolution of agent population and continuous action by that. Experimental results using the artificial society proved that the present method can generate artificial society which fills a demand in high probability.
Runtime Analysis of (1+1) Evolutionary Algorithm for a TSP Instance
NASA Astrophysics Data System (ADS)
Zhang, Yu Shan; Hao, Zhi Feng
Evolutionary Algorithms (EAs) have been used widely and successfully in solving a famous classical combinatorial optimization problem-the traveling salesman problem (TSP). There are lots of experimental results concerning the TSP. However, relatively few theoretical results on the runtime analysis of EAs on the TSP are available. This paper conducts a runtime analysis of a simple Evolutionary Algorithm called (1+1) EA on a TSP instance. We represent a tour as a string of integer, and randomly choose 2-opt and 3-opt operator as the mutation operator at each iteration. The expected runtime of (1+1) EA on this TSP instance is proved to be O(n 4), which is tighter than O(n 6 + (1/ρ)nln n) of (1+1) MMAA (Max-Min ant algorithms). It is also shown that the selection of mutation operator is very important in (1+1) EA.
On source models for 192Ir HDR brachytherapy dosimetry using model based algorithms
NASA Astrophysics Data System (ADS)
Pantelis, Evaggelos; Zourari, Kyveli; Zoros, Emmanouil; Lahanas, Vasileios; Karaiskos, Pantelis; Papagiannis, Panagiotis
2016-06-01
A source model is a prerequisite of all model based dose calculation algorithms. Besides direct simulation, the use of pre-calculated phase space files (phsp source models) and parameterized phsp source models has been proposed for Monte Carlo (MC) to promote efficiency and ease of implementation in obtaining photon energy, position and direction. In this work, a phsp file for a generic 192Ir source design (Ballester et al 2015) is obtained from MC simulation. This is used to configure a parameterized phsp source model comprising appropriate probability density functions (PDFs) and a sampling procedure. According to phsp data analysis 15.6% of the generated photons are absorbed within the source, and 90.4% of the emergent photons are primary. The PDFs for sampling photon energy and direction relative to the source long axis, depend on the position of photon emergence. Photons emerge mainly from the cylindrical source surface with a constant probability over ±0.1 cm from the center of the 0.35 cm long source core, and only 1.7% and 0.2% emerge from the source tip and drive wire, respectively. Based on these findings, an analytical parameterized source model is prepared for the calculation of the PDFs from data of source geometry and materials, without the need for a phsp file. The PDFs from the analytical parameterized source model are in close agreement with those employed in the parameterized phsp source model. This agreement prompted the proposal of a purely analytical source model based on isotropic emission of photons generated homogeneously within the source core with energy sampled from the 192Ir spectrum, and the assignment of a weight according to attenuation within the source. Comparison of single source dosimetry data obtained from detailed MC simulation and the proposed analytical source model show agreement better than 2% except for points lying close to the source longitudinal axis.
On source models for (192)Ir HDR brachytherapy dosimetry using model based algorithms.
Pantelis, Evaggelos; Zourari, Kyveli; Zoros, Emmanouil; Lahanas, Vasileios; Karaiskos, Pantelis; Papagiannis, Panagiotis
2016-06-01
A source model is a prerequisite of all model based dose calculation algorithms. Besides direct simulation, the use of pre-calculated phase space files (phsp source models) and parameterized phsp source models has been proposed for Monte Carlo (MC) to promote efficiency and ease of implementation in obtaining photon energy, position and direction. In this work, a phsp file for a generic (192)Ir source design (Ballester et al 2015) is obtained from MC simulation. This is used to configure a parameterized phsp source model comprising appropriate probability density functions (PDFs) and a sampling procedure. According to phsp data analysis 15.6% of the generated photons are absorbed within the source, and 90.4% of the emergent photons are primary. The PDFs for sampling photon energy and direction relative to the source long axis, depend on the position of photon emergence. Photons emerge mainly from the cylindrical source surface with a constant probability over ±0.1 cm from the center of the 0.35 cm long source core, and only 1.7% and 0.2% emerge from the source tip and drive wire, respectively. Based on these findings, an analytical parameterized source model is prepared for the calculation of the PDFs from data of source geometry and materials, without the need for a phsp file. The PDFs from the analytical parameterized source model are in close agreement with those employed in the parameterized phsp source model. This agreement prompted the proposal of a purely analytical source model based on isotropic emission of photons generated homogeneously within the source core with energy sampled from the (192)Ir spectrum, and the assignment of a weight according to attenuation within the source. Comparison of single source dosimetry data obtained from detailed MC simulation and the proposed analytical source model show agreement better than 2% except for points lying close to the source longitudinal axis. PMID:27191179
NASA Astrophysics Data System (ADS)
Tang, Y.; Reed, P.; Wagener, T.
2005-11-01
This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ɛ-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated model application in the Shale Hills watershed in Pennsylvania. A challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 is an excellent benchmark algorithm for multiobjective hydrologic model calibration. SPEA2 attained competitive to superior results for most of the problems tested in this study. ɛ-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration.
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
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.
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
Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS
Barker, Jeffrey W.; Aarabi, Ardalan; Huppert, Theodore J.
2013-01-01
Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3–5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5–9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present. PMID:24009999
NASA Astrophysics Data System (ADS)
Malusek, Alexandr; Magnusson, Maria; Sandborg, Michael; Westin, Robin; Alm Carlsson, Gudrun
2014-03-01
Better knowledge of elemental composition of patient tissues may improve the accuracy of absorbed dose delivery in brachytherapy. Deficiencies of water-based protocols have been recognized and work is ongoing to implement patient-specific radiation treatment protocols. A model based iterative image reconstruction algorithm DIRA has been developed by the authors to automatically decompose patient tissues to two or three base components via dual-energy computed tomography. Performance of an updated version of DIRA was evaluated for the determination of prostate calcification. A computer simulation using an anthropomorphic phantom showed that the mass fraction of calcium in the prostate tissue was determined with accuracy better than 9%. The calculated mass fraction was little affected by the choice of the material triplet for the surrounding soft tissue. Relative differences between true and approximated values of linear attenuation coefficient and mass energy absorption coefficient for the prostate tissue were less than 6% for photon energies from 1 keV to 2 MeV. The results indicate that DIRA has the potential to improve the accuracy of dose delivery in brachytherapy despite the fact that base material triplets only approximate surrounding soft tissues.
Convergence of a discretized self-adaptive evolutionary algorithm on multi-dimensional problems.
Hart, William Eugene; DeLaurentis, John Morse
2003-08-01
We consider the convergence properties of a non-elitist self-adaptive evolutionary strategy (ES) on multi-dimensional problems. In particular, we apply our recent convergence theory for a discretized (1,{lambda})-ES to design a related (1,{lambda})-ES that converges on a class of seperable, unimodal multi-dimensional problems. The distinguishing feature of self-adaptive evolutionary algorithms (EAs) is that the control parameters (like mutation step lengths) are evolved by the evolutionary algorithm. Thus the control parameters are adapted in an implicit manner that relies on the evolutionary dynamics to ensure that more effective control parameters are propagated during the search. Self-adaptation is a central feature of EAs like evolutionary stategies (ES) and evolutionary programming (EP), which are applied to continuous design spaces. Rudolph summarizes theoretical results concerning self-adaptive EAs and notes that the theoretical underpinnings for these methods are essentially unexplored. In particular, convergence theories that ensure convergence to a limit point on continuous spaces have only been developed by Rudolph, Hart, DeLaurentis and Ferguson, and Auger et al. In this paper, we illustrate how our analysis of a (1,{lambda})-ES for one-dimensional unimodal functions can be used to ensure convergence of a related ES on multidimensional functions. This (1,{lambda})-ES randomly selects a search dimension in each iteration, along which points generated. For a general class of separable functions, our analysis shows that the ES searches along each dimension independently, and thus this ES converges to the (global) minimum.
Learning deterministic finite automata with a smart state labeling evolutionary algorithm.
Lucas, Simon M; Reynolds, T Jeff
2005-07-01
Learning a Deterministic Finite Automaton (DFA) from a training set of labeled strings is a hard task that has been much studied within the machine learning community. It is equivalent to learning a regular language by example and has applications in language modeling. In this paper, we describe a novel evolutionary method for learning DFA that evolves only the transition matrix and uses a simple deterministic procedure to optimally assign state labels. We compare its performance with the Evidence Driven State Merging (EDSM) algorithm, one of the most powerful known DFA learning algorithms. We present results on random DFA induction problems of varying target size and training set density. We also studythe effects of noisy training data on the evolutionary approach and on EDSM. On noise-free data, we find that our evolutionary method outperforms EDSM on small sparse data sets. In the case of noisy training data, we find that our evolutionary method consistently outperforms EDSM, as well as other significant methods submitted to two recent competitions. PMID:16013754
THE APPLICATION OF AN EVOLUTIONARY ALGORITHM TO THE OPTIMIZATION OF A MESOSCALE METEOROLOGICAL MODEL
Werth, D.; O'Steen, L.
2008-02-11
We show that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root mean square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). We find that the optimization can be efficient with relatively modest computer resources, thus operational implementation is possible. The optimization efficiency, however, is found to depend strongly on the procedure used to perturb the 'child' parameters relative to their 'parents' within the evolutionary algorithm. In addition, the meteorological variables included in the rms error and their weighting are found to be an important factor with respect to finding the global optimum.
An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints
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
NASA Astrophysics Data System (ADS)
Woodley, Robert; Lindahl, Eric; Barker, Joseph
2007-04-01
A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance; whether during operations or simulations using models of humans. Many missions and environments challenge human capabilities (e.g., combat stress, waiting, fatigue from long duty hours or tour of duty). This paper presents a team selection algorithm based on an evolutionary algorithm. The main difference between this and the standard EA is that a new form of objective function is used that incorporates the beliefs and uncertainties of the data. Preliminary results show that this selection algorithm will be very beneficial for very large data sets with multiple constraints and uncertainties. This algorithm will be utilized in a military unit selection tool.
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.
NASA Astrophysics Data System (ADS)
Lee, Kyoung Jin
Understanding and modeling seismic wave propagation is important in regional and exploration seismology. Ray tracing is a powerful and popular method for this purpose. Wavefront construction (WFC) method handles wavefronts instead of individual rays, thereby controlling proper ray density on the wavefront. By adaptively controlling rays over a wavefront, it efficiently models wave propagation. Algorithms for a quasi-P wave wavefront construction method and a new coordinate system used to generate wavefront construction mesh are proposed and tested for numerical properties and modeling capabilities. Traveltimes, amplitudes, and other parameters, which can be used for seismic imaging such as migrations and synthetic seismograms, are computed from the wavefront construction method. Modeling with wavefront construction code is applied to anisotropic media as well as isotropic media. Synthetic seismograms are computed using the wavefront construction method as a new way of generating synthetics. To incorporate layered velocity models, the model based interpolation (MBI) ray tracing method, which is designed to take advantage of the wavefront construction method as well as conventional ray tracing methods, is proposed and experimental codes are developed for it. Many wavefront construction codes are limited to smoothed velocity models for handling complicated problems in layered velocity models and the conventional ray tracing methods suffer from the inability to control ray density during wave propagation. By interpolating the wavefront near model boundaries, it is possible to handle the layered velocity model as well as overcome ray density control problems in conventional methods. The test results revealed this new method can be an effective modeling tool for accurate and effective computing.
NASA Astrophysics Data System (ADS)
Tang, Y.; Reed, P.; Wagener, T.
2006-05-01
This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ɛ-NSGAII), the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA), and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). This study uses three test cases to compare the algorithms' performances: (1) a standardized test function suite from the computer science literature, (2) a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3) a computationally intensive integrated surface-subsurface model application in the Shale Hills watershed in Pennsylvania. One challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 attained competitive to superior results for most of the problems tested in this study. The primary strengths of the SPEA2 algorithm lie in its search reliability and its diversity preservation operator. The biggest challenge in maximizing the performance of SPEA2 lies in specifying an effective archive size without a priori knowledge of the Pareto set. In practice, this would require significant trial-and-error analysis, which is problematic for more complex, computationally intensive calibration applications. ɛ-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration. ɛ-NSGAII's primary strength lies in its ease-of-use due to its dynamic population sizing and archiving which lead to rapid convergence to very high quality solutions with minimal user input. MOSCEM-UA is best suited for hydrologic model calibration applications that have small parameter sets
Technology Transfer Automated Retrieval System (TEKTRAN)
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...
NASA Astrophysics Data System (ADS)
Zatarain Salazar, Jazmin; Reed, Patrick M.; Herman, Jonathan D.; Giuliani, Matteo; Castelletti, Andrea
2016-06-01
Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ɛ-MOEA, the auto-adaptive Borg MOEA, and ɛ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.
Metabolic flux estimation--a self-adaptive evolutionary algorithm with singular value decomposition.
Yang, Jing; Wongsa, Sarawan; Kadirkamanathan, Visakan; Billings, Stephen A; Wright, Phillip C
2007-01-01
Metabolic flux analysis is important for metabolic system regulation and intracellular pathway identification. A popular approach for intracellular flux estimation involves using 13C tracer experiments to label states that can be measured by nuclear magnetic resonance spectrometry or gas chromatography mass spectrometry. However, the bilinear balance equations derived from 13C tracer experiments and the noisy measurements require a nonlinear optimization approach to obtain the optimal solution. In this paper, the flux quantification problem is formulated as an error-minimization problem with equality and inequality constraints through the 13C balance and stoichiometric equations. The stoichiometric constraints are transformed to a null space by singular value decomposition. Self-adaptive evolutionary algorithms are then introduced for flux quantification. The performance of the evolutionary algorithm is compared with ordinary least squares estimation by the simulation of the central pentose phosphate pathway. The proposed algorithm is also applied to the central metabolism of Corynebacterium glutamicum under lysine-producing conditions. A comparison between the results from the proposed algorithm and data from the literature is given. The complexity of a metabolic system with bidirectional reactions is also investigated by analyzing the fluctuations in the flux estimates when available measurements are varied. PMID:17277420
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.
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. PMID:26289628
Tuning of MEMS Gyroscope using Evolutionary Algorithm and "Switched Drive-Angle" Method
NASA Technical Reports Server (NTRS)
Keymeulen, Didier; Ferguson, Michael I.; Breuer, Luke; Peay, Chris; Oks, Boris; Cheng, Yen; Kim, Dennis; MacDonald, Eric; Foor, David; Terrile, Rich; Yee, Karl
2006-01-01
We propose a tuning method for Micro-Electro-Mechanical Systems (MEMS) gyroscopes based on evolutionary computation that has the capacity to efficiently increase the sensitivity of MEMS gyroscopes through tuning and, furthermore, to find the optimally tuned configuration for this state of increased sensitivity. We present the results of an experiment to determine the speed and efficiency of an evolutionary algorithm applied to electrostatic tuning of MEMS micro gyros. The MEMS gyro used in this experiment is a pyrex post resonator gyro (PRG) in a closed-loop control system. A measure of the quality of tuning is given by the difference in resonant frequencies, or frequency split, for the two orthogonal rocking axes. The current implementation of the closed-loop platform is able to measure and attain a relative stability in the sub-millihertz range, leading to a reduction of the frequency split to less than 100 mHz.
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
A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry.
Makin, Alexis D J; Bertamini, Marco; Jones, Andrew; Holmes, Tim; Zanker, Johannes M
2016-03-01
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 Self-adaptive Evolutionary Algorithm for Multi-objective Optimization
NASA Astrophysics Data System (ADS)
Cao, Ruifen; Li, Guoli; Wu, Yican
Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. The paper proposes a self-adaptive evolutionary algorithm (called SEA) for multi-objective optimization. In the SEA, the probability of crossover and mutation,P c and P m , are varied depending on the fitness values of the solutions. Fitness assignment of SEA realizes the twin goals of maintaining diversity in the population and guiding the population to the true Pareto Front; fitness value of individual not only depends on improved density estimation but also depends on non-dominated rank. The density estimation can keep diversity in all instances including when scalars of all objectives are much different from each other. SEA is compared against the Non-dominated Sorting Genetic Algorithm (NSGA-II) on a set of test problems introduced by the MOEA community. Simulated results show that SEA is as effective as NSGA-II in most of test functions, but when scalar of objectives are much different from each other, SEA has better distribution of non-dominated solutions.
A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry
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
Creating ensembles of oblique decision trees with evolutionary algorithms and sampling
Cantu-Paz, Erick; Kamath, Chandrika
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.
Searching for the Optimal Working Point of the MEIC at JLab Using an Evolutionary Algorithm
Balsa Terzic, Matthew Kramer, Colin Jarvis
2011-03-01
The Medium-energy Electron Ion Collider (MEIC), a proposed medium-energy ring-ring electron-ion collider based on CEBAF at Jefferson Lab. The collider luminosity and stability are sensitive to the choice of a working point - the betatron and synchrotron tunes of the two colliding beams. Therefore, a careful selection of the working point is essential for stable operation of the collider, as well as for achieving high luminosity. Here we describe a novel approach for locating an optimal working point based on evolutionary algorithm techniques.
A new evolutionary algorithm with structure mutation for the maximum balanced biclique problem.
Yuan, Bo; Li, Bin; Chen, Huanhuan; Yao, Xin
2015-05-01
The maximum balanced biclique problem (MBBP), an NP-hard combinatorial optimization problem, has been attracting more attention in recent years. Existing node-deletion-based algorithms usually fail to find high-quality solutions due to their easy stagnation in local optima, especially when the scale of the problem grows large. In this paper, a new algorithm for the MBBP, evolutionary algorithm with structure mutation (EA/SM), is proposed. In the EA/SM framework, local search complemented with a repair-assisted restart process is adopted. A new mutation operator, SM, is proposed to enhance the exploration during the local search process. The SM can change the structure of solutions dynamically while keeping their size (fitness) and the feasibility unchanged. It implements a kind of large mutation in the structure space of MBBP to help the algorithm escape from local optima. An MBBP-specific local search operator is designed to improve the quality of solutions efficiently; besides, a new repair-assisted restart process is introduced, in which the Marchiori's heuristic repair is modified to repair every new solution reinitialized by an estimation of distribution algorithm (EDA)-like process. The proposed algorithm is evaluated on a large set of benchmark graphs with various scales and densities. Experimental results show that: 1) EA/SM produces significantly better results than the state-of-the-art heuristic algorithms; 2) it also outperforms a repair-based EDA and a repair-based genetic algorithm on all benchmark graphs; and 3) the advantages of EA/SM are mainly due to the introduction of the new SM operator and the new repair-assisted restart process. PMID:25137737
Implementation of a Fractional Model-Based Fault Detection Algorithm into a PLC Controller
NASA Astrophysics Data System (ADS)
Kopka, Ryszard
2014-12-01
This paper presents results related to the implementation of model-based fault detection and diagnosis procedures into a typical PLC controller. To construct the mathematical model and to implement the PID regulator, a non-integer order differential/integral calculation was used. Such an approach allows for more exact control of the process and more precise modelling. This is very crucial in model-based diagnostic methods. The theoretical results were verified on a real object in the form of a supercapacitor connected to a PLC controller by a dedicated electronic circuit controlled directly from the PLC outputs.
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
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
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
A Cooperative Co-Evolutionary Genetic Algorithm for Tree Scoring and Ancestral Genome Inference.
Gao, Nan; Zhang, Yan; Feng, Bing; Tang, Jijun
2015-01-01
Recent advances of technology have made it easy to obtain and compare whole genomes. Rearrangements of genomes through operations such as reversals and transpositions are rare events that enable researchers to reconstruct deep evolutionary history among species. Some of the popular methods need to search a large tree space for the best scored tree, thus it is desirable to have a fast and accurate method that can score a given tree efficiently. During the tree scoring procedure, the genomic structures of internal tree nodes are also provided, which provide important information for inferring ancestral genomes and for modeling the evolutionary processes. However, computing tree scores and ancestral genomes are very difficult and a lot of researchers have to rely on heuristic methods which have various disadvantages. In this paper, we describe the first genetic algorithm for tree scoring and ancestor inference, which uses a fitness function considering co-evolution, adopts different initial seeding methods to initialize the first population pool, and utilizes a sorting-based approach to realize evolution. Our extensive experiments show that compared with other existing algorithms, this new method is more accurate and can infer ancestral genomes that are much closer to the true ancestors. PMID:26671797
An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.
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
David J. Muth Jr.
2006-09-01
This paper examines the use of graph based evolutionary algorithms (GBEAs) to find multiple acceptable solutions for heat transfer in engineering systems during the optimization process. GBEAs are a type of evolutionary algorithm (EA) in which a topology, or geography, is imposed on an evolving population of solutions. The rates at which solutions can spread within the population are controlled by the choice of topology. As in nature geography can be used to develop and sustain diversity within the solution population. Altering the choice of graph can create a more or less diverse population of potential solutions. The choice of graph can also affect the convergence rate for the EA and the number of mating events required for convergence. The engineering system examined in this paper is a biomass fueled cookstove used in developing nations for household cooking. In this cookstove wood is combusted in a small combustion chamber and the resulting hot gases are utilized to heat the stove’s cooking surface. The spatial temperature profile of the cooking surface is determined by a series of baffles that direct the flow of hot gases. The optimization goal is to find baffle configurations that provide an even temperature distribution on the cooking surface. Often in engineering, the goal of optimization is not to find the single optimum solution but rather to identify a number of good solutions that can be used as a starting point for detailed engineering design. Because of this a key aspect of evolutionary optimization is the diversity of the solutions found. The key conclusion in this paper is that GBEA’s can be used to create multiple good solutions needed to support engineering design.
Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933
Log-linear model based behavior selection method for artificial fish swarm algorithm.
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm
Huang, Zhehuang; Chen, Yidong
2015-01-01
Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm. PMID:25691895
NASA Astrophysics Data System (ADS)
Wu, J.; Yang, Y.; Wu, J.
2011-12-01
In this study, a new hybrid multi-objective evolutionary algorithm (MOEA), the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), is proposed for the management of groundwater resources under variable density conditions. Relatively few MOEAs can possess global search ability contenting with intensified search in local area. Moreover, the overall searching ability of tabu search (TS) based MOEAs is very sensitive to neighborhood step size. The NPTSGA is developed on the thought of integrating genetic algorithm (GA) with a TS based MOEA, niched Pareto tabu search (NPTS), which helps to alleviate both of the above difficulties. Here, the global search ability of the NPTS is improved by the diversification of candidate solutions arose from the evolving genetic algorithm population. Furthermore, the proposed methodology coupled with a density-dependent groundwater flow and solute transport simulator, SEAWAT, is developed and its performance is evaluated through a synthetic seawater intrusion management problem. Optimization results indicate that the NPTSGA offers a tradeoff between the two conflicting objectives. A key conclusion of this study is that the NPTSGA can balance the tradeoff between the intensification of nondomination and the diversification of near Pareto-optimal solutions and is a stable and robust method for implementing the multi-objective design of variable-density groundwater resources.
Ishibuchi, Hisao; Sudo, Takahiko; Nojima, Yusuke
2016-01-01
In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a ([Formula: see text])ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the ([Formula: see text])ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm. PMID:27026888
Grand-canonical evolutionary algorithm for the prediction of two-dimensional materials
NASA Astrophysics Data System (ADS)
Revard, Benjamin C.; Tipton, William W.; Yesypenko, Anna; Hennig, Richard G.
2016-02-01
Single-layer materials represent a new materials class with properties that are potentially transformative for applications in nanoelectronics and solar-energy harvesting. With the goal of discovering novel two-dimensional (2D) materials with unusual compositions and structures, we have developed a grand-canonical evolutionary algorithm that searches the structure and composition space while constraining the thickness of the structures. Coupling the algorithm to first-principles total-energy methods, we show that this approach can successfully identify known 2D materials and find low-energy ones. We present the details of the algorithm, including suitable objective functions, and illustrate its potential with a study of the Sn-S and C-Si binary materials systems. The algorithm identifies several 2D structures of InP, recovers known 2D structures in the binary Sn-S and C-Si systems, and finds two 1D Si defects in graphene with formation energies below that of isolated substitutional Si atoms.
An Evolutionary Algorithm with Double-Level Archives for Multiobjective Optimization.
Chen, Ni; Chen, Wei-Neng; Gong, Yue-Jiao; Zhan, Zhi-Hui; Zhang, Jun; Li, Yun; Tan, Yu-Song
2015-09-01
Existing multiobjective evolutionary algorithms (MOEAs) tackle a multiobjective problem either as a whole or as several decomposed single-objective sub-problems. Though the problem decomposition approach generally converges faster through optimizing all the sub-problems simultaneously, there are two issues not fully addressed, i.e., distribution of solutions often depends on a priori problem decomposition, and the lack of population diversity among sub-problems. In this paper, a MOEA with double-level archives is developed. The algorithm takes advantages of both the multiobjective-problem-level and the sub-problem-level approaches by introducing two types of archives, i.e., the global archive and the sub-archive. In each generation, self-reproduction with the global archive and cross-reproduction between the global archive and sub-archives both breed new individuals. The global archive and sub-archives communicate through cross-reproduction, and are updated using the reproduced individuals. Such a framework thus retains fast convergence, and at the same time handles solution distribution along Pareto front (PF) with scalability. To test the performance of the proposed algorithm, experiments are conducted on both the widely used benchmarks and a set of truly disconnected problems. The results verify that, compared with state-of-the-art MOEAs, the proposed algorithm offers competitive advantages in distance to the PF, solution coverage, and search speed. PMID:25343775
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
Analysis of (1+1) evolutionary algorithm and randomized local search with memory.
Sung, Chi Wan; Yuen, Shiu Yin
2011-01-01
This paper considers the scenario of the (1+1) evolutionary algorithm (EA) and randomized local search (RLS) with memory. Previously explored solutions are stored in memory until an improvement in fitness is obtained; then the stored information is discarded. This results in two new algorithms: (1+1) EA-m (with a raw list and hash table option) and RLS-m+ (and RLS-m if the function is a priori known to be unimodal). These two algorithms can be regarded as very simple forms of tabu search. Rigorous theoretical analysis of the expected time to find the globally optimal solutions for these algorithms is conducted for both unimodal and multimodal functions. A unified mathematical framework, involving the new concept of spatially invariant neighborhood, is proposed. Under this framework, both (1+1) EA with standard uniform mutation and RLS can be considered as particular instances and in the most general cases, all functions can be considered to be unimodal. Under this framework, it is found that for unimodal functions, the improvement by memory assistance is always positive but at most by one half. For multimodal functions, the improvement is significant; for functions with gaps and another hard function, the order of growth is reduced; for at least one example function, the order can change from exponential to polynomial. Empirical results, with a reasonable fitness evaluation time assumption, verify that (1+1) EA-m and RLS-m+ are superior to their conventional counterparts. Both new algorithms are promising for use in a memetic algorithm. In particular, RLS-m+ makes the previously impractical RLS practical, and surprisingly, does not require any extra memory in actual implementation. PMID:20868262
NASA Astrophysics Data System (ADS)
Sofiev, M.; Vira, J.; Kouznetsov, R.; Prank, M.; Soares, J.; Genikhovich, E.
2015-11-01
The paper presents the transport module of the System for Integrated modeLling of Atmospheric coMposition SILAM v.5 based on the advection algorithm of Michael Galperin. This advection routine, so far weakly presented in the international literature, is positively defined, stable at any Courant number, and efficient computationally. We present the rigorous description of its original version, along with several updates that improve its monotonicity and shape preservation, allowing for applications to long-living species in conditions of complex atmospheric flows. The scheme is connected with other parts of the model in a way that preserves the sub-grid mass distribution information that is a cornerstone of the advection algorithm. The other parts include the previously developed vertical diffusion algorithm combined with dry deposition, a meteorological pre-processor, and chemical transformation modules. The quality of the advection routine is evaluated using a large set of tests. The original approach has been previously compared with several classic algorithms widely used in operational dispersion models. The basic tests were repeated for the updated scheme and extended with real-wind simulations and demanding global 2-D tests recently suggested in the literature, which allowed one to position the scheme with regard to sophisticated state-of-the-art approaches. The advection scheme performance was fully comparable with other algorithms, with a modest computational cost. This work was the last project of Dr. Sci. Michael Galperin, who passed away on 18 March 2008.
Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies
NASA Astrophysics Data System (ADS)
Cantu-Paz, Erick; Kamath, Chandrika
2000-10-01
Decision tress have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis- parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learned is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction wiht deterministic hill-climbing and the use of simulated annealing. In this paper, we use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. We demonstrate our technique on a synthetic data set, and then we apply it to a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology. In addition, we describe our experiences with several split evaluation criteria. Our results suggest that, in some cases, the evolutionary approach is faster and more accurate than existing oblique decision tree algorithms. However, for our astronomical data, the accuracy is not significantly different than the axis-parallel trees.
NASA Technical Reports Server (NTRS)
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
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
An evolutionary algorithm for the segmentation of muscles and bones of the lower limb.
NASA Astrophysics Data System (ADS)
Lpez, Marco A.; Braidot, A.; Sattler, Anbal; Schira, Claudia; Uriburu, E.
2016-04-01
In the field of medical image segmentation, muscles segmentation is a problem that has not been fully resolved yet. This is due to the fact that the basic assumption of image segmentation, which asserts that a visual distinction should ex- ist between the different structures to be identified, is infringed. As the tissue composition of two different muscles is the same, it becomes extremely difficult to distinguish one another if they are near. We have developed an evolutionary algorithm which selects the set and the sequence of morphological operators that better segments muscles and bones from an MRI image. The achieved results shows that the developed algorithm presents average sensitivity values close to 75% in the segmentation of the different processed muscles and bones. It also presents average specificity values close to 93% for the same structures. Furthermore, the algorithm can identify muscles that are closely located through the path from their origin point to their insertions, with very low error values (below 7%) .
Meta-Model Based Optimisation Algorithms for Robust Optimization of 3D Forging Sequences
Fourment, Lionel
2007-04-07
In order to handle costly and complex 3D metal forming optimization problems, we develop a new optimization algorithm that allows finding satisfactory solutions within less than 50 iterations (/function evaluation) in the presence of local extrema. It is based on the sequential approximation of the problem objective function by the Meshless Finite Difference Method (MFDM). This changing meta-model allows taking into account the gradient information, if available, or not. It can be easily extended to take into account uncertainties on the optimization parameters. This new algorithm is first evaluated on analytic functions, before being applied to a 3D forging benchmark, the preform tool shape optimization that allows minimizing the potential of fold formation during the two-stepped forging sequence.
Development of a real-time model based safety monitoring algorithm for the SSME
NASA Astrophysics Data System (ADS)
Norman, A. M.; Maram, J.; Coleman, P.; D'Valentine, M.; Steffens, A.
1992-07-01
A safety monitoring system for the SSME incorporating a real time model of the engine has been developed for LeRC as a task of the LeRC Life Prediction for Rocket Engines contract, NAS3-25884. This paper describes the development of the algorithm and model to date, their capabilities and limitations, results of simulation tests, lessons learned, and the plans for implementation and test of the system.
A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
Chichilnisky, E. J.; Simoncelli, Eero P.
2013-01-01
We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call “binary pursuit”. The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth. PMID:23671583
Blind watermark algorithm on 3D motion model based on wavelet transform
NASA Astrophysics Data System (ADS)
Qi, Hu; Zhai, Lang
2013-12-01
With the continuous development of 3D vision technology, digital watermark technology, as the best choice for copyright protection, has fused with it gradually. This paper proposed a blind watermark plan of 3D motion model based on wavelet transform, and made it loaded into the Vega real-time visual simulation system. Firstly, put 3D model into affine transform, and take the distance from the center of gravity to the vertex of 3D object in order to generate a one-dimensional discrete signal; then make this signal into wavelet transform to change its frequency coefficients and embed watermark, finally generate 3D motion model with watermarking. In fixed affine space, achieve the robustness in translation, revolving and proportion transforms. The results show that this approach has better performances not only in robustness, but also in watermark- invisibility.
NASA Technical Reports Server (NTRS)
Pulliam, T. H.; Nemec, M.; Holst, T.; Zingg, D. W.; Kwak, Dochan (Technical Monitor)
2002-01-01
A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.
Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui
2010-02-01
In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems. PMID:20180252
XTALOPT version r7: An open-source evolutionary algorithm for crystal structure prediction
NASA Astrophysics Data System (ADS)
Lonie, David C.; Zurek, Eva
2011-10-01
A new version of XTALOPT, a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XTALOPT website, http://xtalopt.openmolecules.net. The new version now supports four external geometry optimization codes (VASP, GULP, PWSCF, and CASTEP), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the user's workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well.
Optimized sound diffusers based on sonic crystals using a multiobjective evolutionary algorithm.
Redondo, J; Sánchez-Pérez, J V; Blasco, X; Herrero, J M; Vorländer, M
2016-05-01
Sonic crystals have been demonstrated to be good candidates to substitute for conventional diffusers in order to overcome the need for extremely thick structures when low frequencies have to be scattered, however, their performance is limited to a narrow band. In this work, multiobjective evolutionary algorithms are used to extend the bandwidth to the whole low frequency range. The results show that diffusion can be significantly increased. Several cost functions are considered in the paper, on the one hand to illustrate the flexibility of the optimization and on the other hand to demonstrate the problems associated with the use of certain cost functions. A study of the robustness of the optimized diffusers is also presented, introducing a parameter that can help to choose among the best candidates. Finally, the advantages of the use of multiobjective optimization in comparison with conventional optimizations are discussed. PMID:27250173
Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm
NASA Astrophysics Data System (ADS)
Wu, Xiaolan; Grubesic, Tony H.
2010-12-01
Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.
Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection
Offman, Marc N; Tournier, Alexander L; Bates, Paul A
2008-01-01
Background Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection. Results In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed. Conclusion This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA. PMID:18673557
NASA Astrophysics Data System (ADS)
Rocha, M. C.; Saraiva, J. T.
2012-10-01
The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
NASA Astrophysics Data System (ADS)
Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.
2015-12-01
This study contributes a diagnostic assessment of multiobjective evolutionary algorithm (MOEA) search on a set of water resources problem formulations with different configurations of constraints. Unlike constraints in classical optimization modeling, constraints within MOEA simulation-optimization represent limits on acceptable performance that delineate whether solutions within the search problem are feasible. Constraints are relevant because of the emergent pressures on water resources systems: increasing public awareness of their sustainability, coupled with regulatory pressures on water management agencies. In this study, we test several state-of-the-art MOEAs that utilize restricted tournament selection for constraint handling on varying configurations of water resources planning problems. For example, a problem that has no constraints on performance levels will be compared with a problem with several severe constraints, and a problem with constraints that have less severe values on the constraint thresholds. One such problem, Lower Rio Grande Valley (LRGV) portfolio planning, has been solved with a suite of constraints that ensure high reliability, low cost variability, and acceptable performance in a single year severe drought. But to date, it is unclear whether or not the constraints are negatively affecting MOEAs' ability to solve the problem effectively. Two categories of results are explored. The first category uses control maps of algorithm performance to determine if the algorithm's performance is sensitive to user-defined parameters. The second category uses run-time performance metrics to determine the time required for the algorithm to reach sufficient levels of convergence and diversity on the solution sets. Our work exploring the effect of constraints will better enable practitioners to define MOEA problem formulations for real-world systems, especially when stakeholders are concerned with achieving fixed levels of performance according to one or
MOEA/D-ACO: a multiobjective evolutionary algorithm using decomposition and AntColony.
Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto
2013-12-01
Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA) based on decomposition (MOEA/D), this paper proposes a multiobjective EA, i.e., MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes a multiobjective optimization problem into a number of single-objective optimization problems. Each ant (i.e., agent) is responsible for solving one subproblem. All the ants are divided into a few groups, and each ant has several neighboring ants. An ant group maintains a pheromone matrix, and an individual ant has a heuristic information matrix. During the search, each ant also records the best solution found so far for its subproblem. To construct a new solution, an ant combines information from its group's pheromone matrix, its own heuristic information matrix, and its current solution. An ant checks the new solutions constructed by itself and its neighbors, and updates its current solution if it has found a better one in terms of its own objective. Extensive experiments have been conducted in this paper to study and compare MOEA/D-ACO with other algorithms on two sets of test problems. On the multiobjective 0-1 knapsack problem,MOEA/D-ACO outperforms the MOEA/D with conventional genetic operators and local search on all the nine test instances. We also demonstrate that the heuristic information matrices in MOEA/D-ACO are crucial to the good performance of MOEA/D-ACO for the knapsack problem. On the biobjective traveling salesman problem, MOEA/D-ACO performs much better than the BicriterionAnt on all the 12 test instances. We also evaluate the effects of grouping, neighborhood, and the location information of current solutions on the performance of MOEA/D-ACO. The work in this paper shows that reactive search optimization scheme, i.e., the "learning while optimizing" principle, is effective in improving multiobjective optimization algorithms. PMID:23757576
NASA Astrophysics Data System (ADS)
Franke, Timothy; Weadon, Tim; Ford, John; Garcia-Sanz, Mario
2015-10-01
Various forms of measurement errors limit telescope tracking performance in practice. A new method for identifying the correcting coefficients for encoder interpolation error is developed. The algorithm corrects the encoder measurement by identifying a harmonic model of the system and using that model to compute the necessary correction parameters. The approach improves upon others by explicitly modeling the unknown dynamics of the structure and controller and by not requiring a separate system identification to be performed. Experience gained from pin-pointing the source of encoder error on the Green Bank Radio Telescope (GBT) is presented. Several tell-tale indicators of encoder error are discussed. Experimental data from the telescope, tested with two different encoders, are presented. Demonstration of the identification methodology on the GBT as well as details of its implementation are discussed. A root mean square tracking error reduction from 0.68 arc seconds to 0.21 arc sec was achieved by changing encoders and was further reduced to 0.10 arc sec with the calibration algorithm. In particular, the ubiquity of this error source is shown and how, by careful correction, it is possible to go beyond the advertised accuracy of an encoder.
NASA Astrophysics Data System (ADS)
Liu, Jintao; Chen, Xi; Zhang, Xingnan; Hoagland, Kyle D.
2012-04-01
Recently developed hillslope storage dynamics theory can represent the essential physical behavior of a natural system by accounting explicitly for the plan shape of a hillslope in an elegant and simple way. As a result, this theory is promising for improving catchment-scale hydrologic modeling. In this study, grid digital elevation model (DEM) based algorithms for determination of hillslope geometric characteristics (e.g., hillslope units and width functions in hillslope storage dynamics models) are presented. This study further develops a method for hillslope partitioning, established by Fan and Bras (1998), by applying it on a grid network. On the basis of hillslope unit derivation, a flow distance transforms method (TD∞) is suggested in order to decrease the systematic error of grid DEM-based flow distance calculation caused by flow direction approximation to streamlines. Hillslope width transfer functions are then derived to convert the probability density functions of flow distance into hillslope width functions. These algorithms are applied and evaluated on five abstract hillslopes, and detailed tests and analyses are carried out by comparing the derivation results with theoretical width functions. The results demonstrate that the TD∞ improves estimations of the flow distance and thus hillslope width function. As the proposed procedures are further applied in a natural catchment, we find that the natural hillslope width function can be well fitted by the Gaussian function. This finding is very important for applying the newly developed hillslope storage dynamics models in a real catchment.
NASA Astrophysics Data System (ADS)
Reed, P. M.; Kollat, J. B.
2005-12-01
This study demonstrates the effectiveness of a modified version of Deb's Non-Dominated Sorted Genetic Algorithm II (NSGAII), which the authors have named the Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (Epsilon-NSGAII), at solving a four objective long-term groundwater monitoring (LTM) design test case. The Epsilon-NSGAII incorporates prior theoretical competent evolutionary algorithm (EA) design concepts and epsilon-dominance archiving to improve the original NSGAII's efficiency, reliability, and ease-of-use. This algorithm eliminates much of the traditional trial-and-error parameterization associated with evolutionary multi-objective optimization (EMO) through epsilon-dominance archiving, dynamic population sizing, and automatic termination. The effectiveness and reliability of the new algorithm is compared to the original NSGAII as well as two other benchmark multi-objective evolutionary algorithms (MOEAs), the Epsilon-Dominance Multi-Objective Evolutionary Algorithm (Epsilon-MOEA) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). These MOEAs have been selected because they have been demonstrated to be highly effective at solving numerous multi-objective problems. The results presented in this study indicate superior performance of the Epsilon-NSGAII in terms of the hypervolume indicator, unary Epsilon-indicator, and first-order empirical attainment function metrics. In addition, the runtime metric results indicate that the diversity and convergence dynamics of the Epsilon-NSGAII are competitive to superior relative to the SPEA2, with both algorithms greatly outperforming the NSGAII and Epsilon-MOEA in terms of these metrics. The improvements in performance of the Epsilon-NSGAII over its parent algorithm the NSGAII demonstrate that the application of Epsilon-dominance archiving, dynamic population sizing with archive injection, and automatic termination greatly improve algorithm efficiency and reliability. In addition, the usability of
Bowen, J.; Dozier, G.
1996-12-31
This paper introduces a hybrid evolutionary hill-climbing algorithm that quickly solves (Constraint Satisfaction Problems (CSPs)). This hybrid uses opportunistic arc and path revision in an interleaved fashion to reduce the size of the search space and to realize when to quit if a CSP is based on an inconsistent constraint network. This hybrid outperforms a well known hill-climbing algorithm, the Iterative Descent Method, on a test suite of 750 randomly generated CSPs.
Geometric model-based fitting algorithm for orientation-selective PELDOR data
NASA Astrophysics Data System (ADS)
Abdullin, Dinar; Hagelueken, Gregor; Hunter, Robert I.; Smith, Graham M.; Schiemann, Olav
2015-03-01
Pulsed electron-electron double resonance (PELDOR or DEER) spectroscopy is frequently used to determine distances between spin centres in biomacromolecular systems. Experiments where mutual orientations of the spin pair are selectively excited provide the so-called orientation-selective PELDOR data. This data is characterised by the orientation dependence of the modulation depth parameter and of the dipolar frequencies. This dependence has to be taken into account in the data analysis in order to extract distance distributions accurately from the experimental time traces. In this work, a fitting algorithm for such data analysis is discussed. The approach is tested on PELDOR data-sets from the literature and is compared with the previous results.
An estimation of generalized bradley-terry models based on the em algorithm.
Fujimoto, Yu; Hino, Hideitsu; Murata, Noboru
2011-06-01
The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate. PMID:21395441
Application of a multi-objective evolutionary algorithm to the spacecraft stationkeeping problem
NASA Astrophysics Data System (ADS)
Myers, Philip L.; Spencer, David B.
2016-10-01
Satellite operations are becoming an increasingly private industry, requiring increased profitability. Efficient and safe operation of satellites in orbit will ensure longer lasting and more profitable satellite services. This paper focuses on the use of a multi-objective evolutionary algorithm to schedule the maneuvers of a hypothetical satellite operating at geosynchronous altitude, by seeking to minimize the propellant consumed through the execution of stationkeeping maneuvers and the time the satellite is displaced from its desired orbital plane. Minimization of the time out of place increases the operational availability and minimizing the propellant usage which allows the spacecraft to operate longer. North-South stationkeeping was studied in this paper, through the use of a set of orbit inclination change maneuvers each year. Two cases for the maximum number of maneuvers to be executed were considered, with four and five maneuvers per year. The results delivered by the algorithm provide maneuver schedules which require 40-100 m/s of total Δv for two years of operation, with the satellite maintaining the satellite's orbital plane to within 0.1° between 84% and 96% of the two years being modeled.
Double-layer evolutionary algorithm for distributed optimization of particle detection on the Grid
NASA Astrophysics Data System (ADS)
Padée, Adam; Kurek, Krzysztof; Zaremba, Krzysztof
2013-08-01
Reconstruction of particle tracks from information collected by position-sensitive detectors is an important procedure in HEP experiments. It is usually controlled by a set of numerical parameters which have to be manually optimized. This paper proposes an automatic approach to this task by utilizing evolutionary algorithm (EA) operating on both real-valued and binary representations. Because of computational complexity of the task a special distributed architecture of the algorithm is proposed, designed to be run in grid environment. It is two-level hierarchical hybrid utilizing asynchronous master-slave EA on the level of clusters and island model EA on the level of the grid. The technical aspects of usage of production grid infrastructure are covered, including communication protocols on both levels. The paper deals also with the problem of heterogeneity of the resources, presenting efficiency tests on a benchmark function. These tests confirm that even relatively small islands (clusters) can be beneficial to the optimization process when connected to the larger ones. Finally a real-life usage example is presented, which is an optimization of track reconstruction in Large Angle Spectrometer of NA-58 COMPASS experiment held at CERN, using a sample of Monte Carlo simulated data. The overall reconstruction efficiency gain, achieved by the proposed method, is more than 4%, compared to the manually optimized parameters.
NASA Astrophysics Data System (ADS)
Sarkar, K.; Topsakal, M.; Wentzcovitch, R. M.
2015-12-01
We attempt to achieve the accuracy of full-potential linearized augmented-plane-wave (FLAPW) method, as implemented in the WIEN2k code, at the favorable computational efficiency of the projector augmented wave (PAW) method for ab initio calculations of solids. For decades, PAW datasets have been generated by manually choosing its parameters and by visually inspecting its logarithmic derivatives, partial wave, and projector basis set. In addition to being tedious and error-prone, this procedure is inadequate because it is impractical to manually explore the full parameter space, as an infinite number of PAW parameter sets for a given augmentation radius can be generated maintaining all the constraints on logarithmic derivatives and basis sets. Performance verification of all plausible solutions against FLAPW is also impractical. Here we report the development of a hybrid algorithm to construct optimized PAW basis sets that can closely reproduce FLAPW results from zero to ultra-high pressures. The approach applies evolutionary computing (EC) to generate optimum PAW parameter sets using the ATOMPAW code. We have the Quantum ESPRESSO distribution to generate equation of state (EOS) to be compared with WIEN2k EOSs set as target. Softer PAW potentials reproducing yet more closely FLAPW EOSs can be found with this method. We demonstrate its working principles and workability by optimizing PAW basis functions for carbon, magnesium, aluminum, silicon, calcium, and iron atoms. The algorithm requires minimal user intervention in a sense that there is no requirement of visual inspection of logarithmic derivatives or of projector functions.
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.
Combining evolutionary algorithms with oblique decision trees to detect bent double galaxies
Cantu-Paz, E; Kamath, C
2000-06-22
Decision trees have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis-parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learnt is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction with deterministic hill climbing and the use of simulated annealing. In this paper, they use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. They demonstrate the technique on a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology, and describe their experiences with several split evaluation criteria.
Deconvolution of γ-spectra variably affected by space radiation using an evolutionary algorithm
NASA Astrophysics Data System (ADS)
McClanahan, Timothy P.; Loew, Murray H.; Trombka, Jacob I.; Evans, Larry G.
2007-09-01
An evolutionary algorithm (ES) for automated deconvolution of γ-ray spectra is described that fits peak shape morphologies typical of spectra acquired from variably radiation damaged γ-ray detectors. Space radiation effects significantly impair semi-conductor γ-ray detector efficiency and induce variable degrees of nuclide peak broadening, distortion in spectra. Mars Odyssey Gamma-ray spectrometer data are used to demonstrate applicability of described algorithms for three degrees of radiation damage. ES methods accurately identify and quantify the discrete set of nuclide peaks in an arbitrary spectrum using a nuclide library. A novel method of constraining peak low energy tails, broadened by detector radiation damage, reduces the peak shape model from six parameters to four yielding a significant minimization of model complexity. Benefits of this approach include the simple implementation of highly specific parameter constraints that appropriately define feasible solution spaces. Methods describe peak low energy tailing descriptors as a continuum of low energy peak tailing curves representing increasing degrees of radiation damage. Curves are addressable by a single real valued parameter. Results illustrate the use of methods to simply describe relative radiation dosimetry using this parameter. Analysis of degraded spectra indicates method sensitivity to low and high levels of space radiation damage prior to and post MO-GRS detector annealings.
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.
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
NASA Astrophysics Data System (ADS)
Polverino, Pierpaolo; Esposito, Angelo; Pianese, Cesare; Ludwig, Bastian; Iwanschitz, Boris; Mai, Andreas
2016-02-01
In the current energetic scenario, Solid Oxide Fuel Cells (SOFCs) exhibit appealing features which make them suitable for environmental-friendly power production, especially for stationary applications. An example is represented by micro-combined heat and power (μ-CHP) generation units based on SOFC stacks, which are able to produce electric and thermal power with high efficiency and low pollutant and greenhouse gases emissions. However, the main limitations to their diffusion into the mass market consist in high maintenance and production costs and short lifetime. To improve these aspects, the current research activity focuses on the development of robust and generalizable diagnostic techniques, aimed at detecting and isolating faults within the entire system (i.e. SOFC stack and balance of plant). Coupled with appropriate recovery strategies, diagnosis can prevent undesired system shutdowns during faulty conditions, with consequent lifetime increase and maintenance costs reduction. This paper deals with the on-line experimental validation of a model-based diagnostic algorithm applied to a pre-commercial SOFC system. The proposed algorithm exploits a Fault Signature Matrix based on a Fault Tree Analysis and improved through fault simulations. The algorithm is characterized on the considered system and it is validated by means of experimental induction of faulty states in controlled conditions.
Chen, Wei-Chen; Maitra, Ranjan
2011-01-01
We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithm (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.
NASA Astrophysics Data System (ADS)
Manzano-Agugliaro, F.; San-Antonio-Gómez, C.; López, S.; Montoya, F. G.; Gil, C.
2013-08-01
When historical map data are compared with modern cartography, the old map coordinates must be transformed to the current system. However, historical data often exhibit heterogeneous quality. In calculating the transformation parameters between the historical and modern maps, it is often necessary to discard highly uncertain data. An optimal balance between the objectives of minimising the transformation error and eliminating as few points as possible can be achieved by generating a Pareto front of solutions using evolutionary genetic algorithms. The aim of this paper is to assess the performance of evolutionary algorithms in determining the accuracy of historical maps in regard to modern cartography. When applied to the 1787 Tomas Lopez map, the use of evolutionary algorithms reduces the linear error by 40% while eliminating only 2% of the data points. The main conclusion of this paper is that evolutionary algorithms provide a promising alternative for the transformation of historical map coordinates and determining the accuracy of historical maps in regard to modern cartography, particularly when the positional quality of the data points used cannot be assured.
A possibilistic approach to rotorcraft design through a multi-objective evolutionary algorithm
NASA Astrophysics Data System (ADS)
Chae, Han Gil
Most of the engineering design processes in use today in the field may be considered as a series of successive decision making steps. The decision maker uses information at hand, determines the direction of the procedure, and generates information for the next step and/or other decision makers. However, the information is often incomplete, especially in the early stages of the design process of a complex system. As the complexity of the system increases, uncertainties eventually become unmanageable using traditional tools. In such a case, the tools and analysis values need to be "softened" to account for the designer's intuition. One of the methods that deals with issues of intuition and incompleteness is possibility theory. Through the use of possibility theory coupled with fuzzy inference, the uncertainties estimated by the intuition of the designer are quantified for design problems. By involving quantified uncertainties in the tools, the solutions can represent a possible set, instead of a crisp spot, for predefined levels of certainty. From a different point of view, it is a well known fact that engineering design is a multi-objective problem or a set of such problems. The decision maker aims to find satisfactory solutions, sometimes compromising the objectives that conflict with each other. Once the candidates of possible solutions are generated, a satisfactory solution can be found by various decision-making techniques. A number of multi-objective evolutionary algorithms (MOEAs) have been developed, and can be found in the literature, which are capable of generating alternative solutions and evaluating multiple sets of solutions in one single execution of an algorithm. One of the MOEA techniques that has been proven to be very successful for this class of problems is the strength Pareto evolutionary algorithm (SPEA) which falls under the dominance-based category of methods. The Pareto dominance that is used in SPEA, however, is not enough to account for the
Energy efficient model based algorithm for control of building HVAC systems.
Kirubakaran, V; Sahu, Chinmay; Radhakrishnan, T K; Sivakumaran, N
2015-11-01
Energy efficient designs are receiving increasing attention in various fields of engineering. Heating ventilation and air conditioning (HVAC) control system designs involve improved energy usage with an acceptable relaxation in thermal comfort. In this paper, real time data from a building HVAC system provided by BuildingLAB is considered. A resistor-capacitor (RC) framework for representing thermal dynamics of the building is estimated using particle swarm optimization (PSO) algorithm. With objective costs as thermal comfort (deviation of room temperature from required temperature) and energy measure (Ecm) explicit MPC design for this building model is executed based on its state space representation of the supply water temperature (input)/room temperature (output) dynamics. The controllers are subjected to servo tracking and external disturbance (ambient temperature) is provided from the real time data during closed loop control. The control strategies are ported on a PIC32mx series microcontroller platform. The building model is implemented in MATLAB and hardware in loop (HIL) testing of the strategies is executed over a USB port. Results indicate that compared to traditional proportional integral (PI) controllers, the explicit MPC's improve both energy efficiency and thermal comfort significantly. PMID:25869418
A simple model based magnet sorting algorithm for planar hybrid undulators
Rakowsky, G.
2010-05-23
Various magnet sorting strategies have been used to optimize undulator performance, ranging from intuitive pairing of high- and low-strength magnets, to full 3D FEM simulation with 3-axis Helmholtz coil magnet data. In the extreme, swapping magnets in a full field model to minimize trajectory wander and rms phase error can be time consuming. This paper presents a simpler approach, extending the field error signature concept to obtain trajectory displacement, kick angle and phase error signatures for each component of magnetization error from a Radia model of a short hybrid-PM undulator. We demonstrate that steering errors and phase errors are essentially decoupled and scalable from measured X, Y and Z components of magnetization. Then, for any given sequence of magnets, rms trajectory and phase errors are obtained from simple cumulative sums of the scaled displacements and phase errors. The cost function (a weighted sum of these errors) is then minimized by swapping magnets, using one's favorite optimization algorithm. This approach was applied recently at NSLS to a short in-vacuum undulator, which required no subsequent trajectory or phase shimming. Trajectory and phase signatures are also obtained for some mechanical errors, to guide 'virtual shimming' and specifying mechanical tolerances. Some simple inhomogeneities are modeled to assess their error contributions.
Dietrich, Arne; Haider, Hilde
2015-08-01
Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before. PMID:25304474
NASA Astrophysics Data System (ADS)
Gair, Jonathan R.; Porter, Edward K.
2009-11-01
We describe a hybrid evolutionary algorithm that can simultaneously search for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The algorithm mixes evolutionary computation, Metropolis-Hastings methods and Nested Sampling. The inspiral of SMBHBs presents an interesting problem for gravitational wave data analysis since, due to the LISA response function, the sources have a bi-modal sky solution. We show here that it is possible not only to detect multiple SMBHBs in the data stream, but also to investigate simultaneously all the various modes of the global solution. In all cases, the algorithm returns parameter determinations within 5σ (as estimated from the Fisher matrix) of the true answer, for both the actual and antipodal sky solutions.
NASA Astrophysics Data System (ADS)
Tom, Brian C.
Intensity Modulated Radiation Therapy (IMRT) has enjoyed success in the clinic by achieving dose escalation to the target while sparing nearby critical structures. For DMLC plans, regularization is introduced in order to smooth the fluence maps. In this dissertation, regularization is used to smooth the fluence profiles. Since SMLC plans have a limited number of intensity levels, smoothing is not a problem. However, in many treatment planning systems, the plans are optimized with beam weights that are continuous. Only after the optimization is complete is when the fluence maps are quantized. This dissertation will study the effects, if any, of quantizing the beam weights. In order to study both smoothing DMLC plans and the quantization of SMLC plans, a multi-objective evolutionary algorithm is employed as the optimization method. The main advantages of using these stochastic algorithms is that the beam weights can be represented either in binary or real strings. Clearly, a binary representation is suited for SMLC delivery (discrete intensity levels), while a real representation is more suited for DMLC. Further, in the case of real beam weights, multi-objective evolutionary algorithms can handle conflicting objective functions very well. In fact, regularization can be thought of as having two competing functions: to maintain fidelity to the data, and smoothing the data. The main disadvantage of regularization is the need to specify the regularization parameter, which controls how important the two objectives are relative to one another. Multi-objective evolutionary algorithms do not need such a parameter. In addition, such algorithms yield a set of solutions, each solution representing differing importance factors of the two (or more) objective functions. Multi-objective evolutionary algorithms can thus be used to study the effects of quantizing the beam weights for SMLC delivery systems as well studying how regularization can reduce the difference between the
Evolutionary Design of one-dimensional Rule Changing cellular automata using genetic algorithms
NASA Astrophysics Data System (ADS)
Yun, Wu; Kanoh, Hitoshi
In this paper we propose a new method to obtain transition rules of one-dimensional two-state cellular automata (CAs) using genetic algorithms (GAs). CAs have the advantages of producing complex systems from the interaction of simple elements, and have attracted increased research interest. However, the difficulty of designing CAs' transition rules to perform a particular task has severely limited their applications. The evolutionary design of CA rules has been studied by the EVCA group in detail. A GA was used to evolve CAs for two tasks: density classification and synchronization problems. That GA was shown to have discovered rules that gave rise to sophisticated emergent computational strategies. Sipper has studied a cellular programming algorithm for 2-state non-uniform CAs, in which each cell may contain a different rule. Meanwhile, Land and Belew proved that the perfect two-state rule for performing the density classification task does not exist. However, Fuks´ showed that a pair of human written rules performs the task perfectly when the size of neighborhood is one. In this paper, we consider a pair of rules and the number of rule iterations as a chromosome, whereas the EVCA group considers a rule as a chromosome. The present method is meant to reduce the complexity of a given problem by dividing the problem into smaller ones and assigning a distinct rule to each one. Experimental results for the two tasks prove that our method is more efficient than a conventional method. Some of the obtained rules agree with the human written rules shown by Fuks´. We also grouped 1000 rules with high fitness into 4 classes according to the Langton's λ parameter. The rules obtained by the proposed method belong to Class- I, II, III or IV, whereas most of the rules by the conventional method belong to Class-IV only. This result shows that the combination of simple rules can perform complex tasks.
NASA Astrophysics Data System (ADS)
Asouti, V. G.; Giannakoglou, K. C.
2012-07-01
This article presents a solution method to the unit commitment problem with probabilistic unit failures and repairs, which is based on evolutionary algorithms and Monte Carlo simulations. Regarding the latter, thousands of availability-unavailability trial time patterns along the scheduling horizon are generated. The objective function to be minimised is the expected total operating cost, computed after adapting any candidate solution, i.e. any series of generating/non-generating (ON/OFF) unit states, to the availability-unavailability patterns and performing evaluations by considering fuel, start-up and shutdown costs as well as the cost for buying electricity from external resources, if necessary. The proposed method introduces a new efficient chromosome representation: the decision variables are integer IDs corresponding to the binary-to-decimal converted ON/OFF (1/0) scenarios that cover the demand in each hour. In contrast to previous methods using binary strings as chromosomes, the new chromosome must be penalised only if any of the constraints regarding start-up, shutdown and ramp times cannot be met, chromosome repair is avoided and, consequently, the dispatch problems are solved once in the preparatory phase instead of during the evolution. For all these reasons, with or without probabilistic outages, the proposed algorithm has much lower CPU cost. In addition, if probabilistic outages are taken into account, a hierarchical evaluation scheme offers extra noticeable gain in CPU cost: the population members are approximately pre-evaluated using a small 'representative' set of the Monte Carlo simulations and only a few top population members undergo evaluations through the full Monte Carlo simulations. The hierarchical scheme makes the proposed method about one order of magnitude faster than its conventional counterpart.
Exploring PtSO4 and PdSO4 phases: an evolutionary algorithm based investigation.
Sharma, Hom; Sharma, Vinit; Huan, Tran Doan
2015-07-21
Metal sulfate formation is one of the major challenges to the emission aftertreatment catalysts. Unlike the incredibly sulfation prone nature of Pd to form PdSO4, no experimental evidence exists for PtSO4 formation. Given the mystery of nonexistence of PtSO4, we explore PtSO4 using a combined approach of an evolutionary algorithm based search technique and quantum mechanical computations. Experimentally known PdSO4 is considered for the comparison and validation of our results. We predict many possible low-energy phases of PtSO4 and PdSO4 at 0 K, which are further investigated in a wide range of temperature-pressure conditions. An entirely new low-energy (tetragonal P42/m) structure of PtSO4 and PdSO4 is predicted, which appears to be the most stable phase of PtSO4 and a competing phase of the experimentally known monoclinic C2/c phase of PdSO4. Phase stability at a finite temperature is further examined and verified by Gibbs free energy calculations of sulfates towards their possible decomposition products. Finally, temperature-pressure phase diagrams are computationally established for both PtSO4 and PdSO4. PMID:26103206
NASA Astrophysics Data System (ADS)
Ong, Zhiyang; Lo, Andy Hao-Wei; Berryman, Matthew; Abbott, Derek
2005-12-01
The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.
NASA Astrophysics Data System (ADS)
Holdsworth, C. H.; Corwin, D.; Stewart, R. D.; Rockne, R.; Trister, A. D.; Swanson, K. R.; Phillips, M.
2012-12-01
We demonstrate a patient-specific method of adaptive IMRT treatment for glioblastoma using a multiobjective evolutionary algorithm (MOEA). The MOEA generates spatially optimized dose distributions using an iterative dialogue between the MOEA and a mathematical model of tumor cell proliferation, diffusion and response. Dose distributions optimized on a weekly basis using biological metrics have the potential to substantially improve and individualize treatment outcomes. Optimized dose distributions were generated using three different decision criteria for the tumor and compared with plans utilizing standard dose of 1.8 Gy/fraction to the CTV (T2-visible MRI region plus a 2.5 cm margin). The sets of optimal dose distributions generated using the MOEA approach the Pareto Front (the set of IMRT plans that delineate optimal tradeoffs amongst the clinical goals of tumor control and normal tissue sparing). MOEA optimized doses demonstrated superior performance as judged by three biological metrics according to simulated results. The predicted number of reproductively viable cells 12 weeks after treatment was found to be the best target objective for use in the MOEA.
Analysis of high resolution FTIR spectra from synchrotron sources using evolutionary algorithms
NASA Astrophysics Data System (ADS)
van Wijngaarden, Jennifer; Desmond, Durell; Leo Meerts, W.
2015-09-01
Room temperature Fourier transform infrared spectra of the four-membered heterocycle trimethylene sulfide were collected with a resolution of 0.00096 cm-1 using synchrotron radiation from the Canadian Light Source from 500 to 560 cm-1. The in-plane ring deformation mode (ν13) at ∼529 cm-1 exhibits dense rotational structure due to the presence of ring inversion tunneling and leads to a doubling of all transitions. Preliminary analysis of the experimental spectrum was pursued via traditional methods involving assignment of quantum numbers to individual transitions in order to conduct least squares fitting to determine the spectroscopic parameters. Following this approach, the assignment of 2358 transitions led to the experimental determination of an effective Hamiltonian. This model describes transitions in the P and R branches to J‧ = 60 and Ka‧ = 10 that connect the tunneling split ground and vibrationally excited states of the ν13 band although a small number of low intensity features remained unassigned. The use of evolutionary algorithms (EA) for automated assignment was explored in tandem and yielded a set of spectroscopic constants that re-create this complex experimental spectrum to a similar degree. The EA routine was also applied to the previously well-understood ring puckering vibration of another four-membered ring, azetidine (Zaporozan et al., 2010). This test provided further evidence of the robust nature of the EA method when applied to spectra for which the underlying physics is well understood.
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.
NASA Astrophysics Data System (ADS)
Friedel, Michael; Buscema, Massimo
2016-04-01
Aquatic ecosystem models can potentially be used to understand the influence of stresses on catchment resource quality. Given that catchment responses are functions of natural and anthropogenic stresses reflected in sparse and spatiotemporal biological, physical, and chemical measurements, an ecosystem is difficult to model using statistical or numerical methods. We propose an artificial adaptive systems approach to model ecosystems. First, an unsupervised machine-learning (ML) network is trained using the set of available sparse and disparate data variables. Second, an evolutionary algorithm with genetic doping is applied to reduce the number of ecosystem variables to an optimal set. Third, the optimal set of ecosystem variables is used to retrain the ML network. Fourth, a stochastic cross-validation approach is applied to quantify and compare the nonlinear uncertainty in selected predictions of the original and reduced models. Results are presented for aquatic ecosystems (tens of thousands of square kilometers) undergoing landscape change in the USA: Upper Illinois River Basin and Central Colorado Assessment Project Area, and Southland region, NZ.
NASA Astrophysics Data System (ADS)
Sarjaš, Andrej; Chowdhury, Amor; Svečko, Rajko
2016-09-01
This paper presents the synthesis of an optimal robust controller design using the polynomial pole placement technique and multi-criteria optimisation procedure via an evolutionary computation algorithm - differential evolution. The main idea of the design is to provide a reliable fixed-order robust controller structure and an efficient closed-loop performance with a preselected nominally characteristic polynomial. The multi-criteria objective functions have quasi-convex properties that significantly improve convergence and the regularity of the optimal/sub-optimal solution. The fundamental aim of the proposed design is to optimise those quasi-convex functions with fixed closed-loop characteristic polynomials, the properties of which are unrelated and hard to present within formal algebraic frameworks. The objective functions are derived from different closed-loop criteria, such as robustness with metric ?∞, time performance indexes, controller structures, stability properties, etc. Finally, the design results from the example verify the efficiency of the controller design and also indicate broader possibilities for different optimisation criteria and control structures.
Parameter extraction from experimental PEFC data using an evolutionary optimization algorithm
NASA Astrophysics Data System (ADS)
Zaglio, M.; Schuler, G.; Wokaun, A.; Mantzaras, J.; Büchi, F. N.
2011-05-01
The accurate characterization of the parameters related to the charge and water transport in the ionomer membrane of polymer electrolyte fuel cells (PEFC) is highly important for the understanding and interpretation of the overall cell behavior. Despite the big efforts to experimentally determine these parameters, a large scatter of data is reported in the literature, due to the inherent experimental difficulties. Likewise, the porosity and tortuosity of the gas diffusion layers affect the membrane water content and the local cell performance, but the published data are usually measured ex-situ, not accounting for the effect of clamping pressure. Using a quasi two-dimensional model and experimental current density data from a linear cell of technical size, a multiparameter optimization procedure based on an evolutionary algorithm has been applied to determine eight material properties highly influencing the cell performance. The optimization procedure converges towards a well defined solution and the resulting parameter values are compared to those available in the literature. The quality of the set of parameters extracted by the optimization procedure is assessed by a sensitivity analysis.
Optimization of thin noise barrier designs using Evolutionary Algorithms and a Dual BEM Formulation
NASA Astrophysics Data System (ADS)
Toledo, R.; Aznárez, J. J.; Maeso, O.; Greiner, D.
2015-01-01
This work aims at assessing the acoustic efficiency of different thin noise barrier models. These designs frequently feature complex profiles and their implementation in shape optimization processes may not always be easy in terms of determining their topological feasibility. A methodology to conduct both overall shape and top edge optimizations of thin cross section acoustic barriers by idealizing them as profiles with null boundary thickness is proposed. This procedure is based on the maximization of the insertion loss of candidate profiles proposed by an evolutionary algorithm. The special nature of these sorts of barriers makes necessary the implementation of a complementary formulation to the classical Boundary Element Method (BEM). Numerical simulations of the barriers' performance are conducted by using a 2D Dual BEM code in eight different barrier configurations (covering overall shaped and top edge configurations; spline curved and polynomial shaped based designs; rigid and noise absorbing boundaries materials). While results are achieved by using a specific receivers' scheme, the influence of the receivers' location on the acoustic performance is previously addressed. With the purpose of testing the methodology here presented, a numerical model validation on the basis of experimental results from a scale model test [34] is conducted. Results obtained show the usefulness of representing complex thin barrier configurations as null boundary thickness-like models.
NASA Astrophysics Data System (ADS)
Smith, R.; Kasprzyk, J. R.; Zagona, E. A.
2013-12-01
Population growth and climate change, combined with difficulties in building new infrastructure, motivate portfolio-based solutions to ensuring sufficient water supply. Powerful simulation models with graphical user interfaces (GUI) are often used to evaluate infrastructure portfolios; these GUI based models require manual modification of the system parameters, such as reservoir operation rules, water transfer schemes, or system capacities. Multiobjective evolutionary algorithm (MOEA) based optimization can be employed to balance multiple objectives and automatically suggest designs for infrastructure systems, but MOEA based decision support typically uses a fixed problem formulation (i.e., a single set of objectives, decisions, and constraints). This presentation suggests a dynamic framework for linking GUI-based infrastructure models with MOEA search. The framework begins with an initial formulation which is solved using a MOEA. Then, stakeholders can interact with candidate solutions, viewing their properties in the GUI model. This is followed by changes in the formulation which represent users' evolving understanding of exigent system properties. Our case study is built using RiverWare, an object-oriented, data-centered model that facilitates the representation of a diverse array of water resources systems. Results suggest that assumptions within the initial MOEA search are violated after investigating tradeoffs and reveal how formulations should be modified to better capture stakeholders' preferences.
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.
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.
NASA Astrophysics Data System (ADS)
Timoshenko, Janis; Anspoks, Andris; Kalinko, Aleksandr; Kuzmin, Alexei
2016-05-01
Extended x-ray absorption fine structure (EXAFS) spectroscopy combined with reverse Monte Carlo (RMC) and evolutionary algorithm (EA) modelling is used to advance the understanding of the local structure and lattice dynamics of copper nitride (Cu3N). The RMC/EA-EXAFS method provides a possibility to probe correlations in the motion of neighboring atoms and allows us to analyze the influence of anisotropic motion of copper atoms in Cu3N.
NASA Astrophysics Data System (ADS)
Sánchez-Escobar, Juan Jaime; Barbosa Santillán, Liliana Ibeth
2015-09-01
This paper describes the use of a hybrid evolutionary optimization algorithm (HEOA) for computing the wavefront aberration from real interferometric data. By finding the near-optimal solution to an optimization problem, this algorithm calculates the Zernike polynomial expansion coefficients from a Fizeau interferogram, showing the validity for the reconstruction of the wavefront aberration. The proposed HEOA incorporates the advantages of both a multimember evolution strategy and locally weighted linear regression in order to minimize an objective function while avoiding premature convergence to a local minimum. The numerical results demonstrate that our HEOA is robust for analyzing real interferograms degraded by noise.
NASA Astrophysics Data System (ADS)
Tsoukalas, Ioannis; Kossieris, Panagiotis; Efstratiadis, Andreas; Makropoulos, Christos
2015-04-01
In water resources optimization problems, the calculation of the objective function usually presumes to first run a simulation model and then evaluate its outputs. In several cases, however, long simulation times may pose significant barriers to the optimization procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required by the problem's complexity. A promising novel strategy to address these shortcomings is the use of surrogate modelling techniques within global optimization algorithms. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SE-EAS) algorithm that couples the strengths of surrogate modelling with the effectiveness and efficiency of the EAS method. The algorithm combines three different optimization approaches (evolutionary search, simulated annealing and the downhill simplex search scheme), in which key decisions are partially guided by numerical approximations of the objective function. The performance of the proposed algorithm is benchmarked against other surrogate-assisted algorithms, in both theoretical and practical applications (i.e. test functions and hydrological calibration problems, respectively), within a limited budget of trials (from 100 to 1000). Results reveal the significant potential of using SE-EAS in challenging optimization problems, involving time-consuming simulations.
NASA Astrophysics Data System (ADS)
Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.
2016-04-01
Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the
Holmes, Tim; Zanker, Johannes M.
2013-01-01
Studying aesthetic preference is notoriously difficult because it targets individual experience. Eye movements provide a rich source of behavioral measures that directly reflect subjective choice. To determine individual preferences for simple composition rules we here use fixation duration as the fitness measure in a Gaze Driven Evolutionary Algorithm (GDEA), which has been demonstrated as a tool to identify aesthetic preferences (Holmes and Zanker, 2012). In the present study, the GDEA was used to investigate the preferred combination of color and shape which have been promoted in the Bauhaus arts school. We used the same three shapes (square, circle, triangle) used by Kandinsky (1923), with the three color palette from the original experiment (A), an extended seven color palette (B), and eight different shape orientation (C). Participants were instructed to look for their preferred circle, triangle or square in displays with eight stimuli of different shapes, colors and rotations, in an attempt to test for a strong preference for red squares, yellow triangles and blue circles in such an unbiased experimental design and with an extended set of possible combinations. We Tested six participants extensively on the different conditions and found consistent preferences for color-shape combinations for individuals, but little evidence at the group level for clear color/shape preference consistent with Kandinsky's claims, apart from some weak link between yellow and triangles. Our findings suggest substantial inter-individual differences in the presence of stable individual associations of color and shapes, but also that these associations are robust within a single individual. These individual differences go some way toward challenging the claims of the universal preference for color/shape combinations proposed by Kandinsky, but also indicate that a much larger sample size would be needed to confidently reject that hypothesis. Moreover, these experiments highlight the
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
NASA Astrophysics Data System (ADS)
Ma, Zhanshan (Sam)
Competition, cooperation and communication are the three fundamental relationships upon which natural selection acts in the evolution of life. Evolutionary game theory (EGT) is a 'marriage' between game theory and Darwin's evolution theory; it gains additional modeling power and flexibility by adopting population dynamics theory. In EGT, natural selection acts as optimization agents and produces inherent strategies, which eliminates some essential assumptions in traditional game theory such as rationality and allows more realistic modeling of many problems. Prisoner's Dilemma (PD) and Sir Philip Sidney (SPS) games are two well-known examples of EGT, which are formulated to study cooperation and communication, respectively. Despite its huge success, EGT exposes a certain degree of weakness in dealing with time-, space- and covariate-dependent (i.e., dynamic) uncertainty, vulnerability and deception. In this paper, I propose to extend EGT in two ways to overcome the weakness. First, I introduce survival analysis modeling to describe the lifetime or fitness of game players. This extension allows more flexible and powerful modeling of the dynamic uncertainty and vulnerability (collectively equivalent to the dynamic frailty in survival analysis). Secondly, I introduce agreement algorithms, which can be the Agreement algorithms in distributed computing (e.g., Byzantine Generals Problem [6][8], Dynamic Hybrid Fault Models [12]) or any algorithms that set and enforce the rules for players to determine their consensus. The second extension is particularly useful for modeling dynamic deception (e.g., asymmetric faults in fault tolerance and deception in animal communication). From a computational perspective, the extended evolutionary game theory (EEGT) modeling, when implemented in simulation, is equivalent to an optimization methodology that is similar to evolutionary computing approaches such as Genetic algorithms with dynamic populations [15][17].
Dunet, Vincent; Hachulla, Anne-Lise; Grimm, Jochen; Beigelman-Aubry, Catherine
2016-01-01
Background Model-based iterative reconstruction (MBIR) reduces image noise and improves image quality (IQ) but its influence on post-processing tools including maximal intensity projection (MIP) and minimal intensity projection (mIP) remains unknown. Purpose To evaluate the influence on IQ of MBIR on native, mIP, MIP axial and coronal reformats of reduced dose computed tomography (RD-CT) chest acquisition. Material and Methods Raw data of 50 patients, who underwent a standard dose CT (SD-CT) and a follow-up RD-CT with a CT dose index (CTDI) of 2–3 mGy, were reconstructed by MBIR and FBP. Native slices, 4-mm-thick MIP, and 3-mm-thick mIP axial and coronal reformats were generated. The relative IQ, subjective IQ, image noise, and number of artifacts were determined in order to compare different reconstructions of RD-CT with reference SD-CT. Results The lowest noise was observed with MBIR. RD-CT reconstructed by MBIR exhibited the best relative and subjective IQ on coronal view regardless of the post-processing tool. MBIR generated the lowest rate of artefacts on coronal mIP/MIP reformats and the highest one on axial reformats, mainly represented by distortions and stairsteps artifacts. Conclusion The MBIR algorithm reduces image noise but generates more artifacts than FBP on axial mIP and MIP reformats of RD-CT. Conversely, it significantly improves IQ on coronal views, without increasing artifacts, regardless of the post-processing technique.
NASA Astrophysics Data System (ADS)
Carlsson Tedgren, Åsa; Alm Carlsson, Gudrun
2013-04-01
Model-based dose calculation algorithms (MBDCAs), recently introduced in treatment planning systems (TPS) for brachytherapy, calculate tissue absorbed doses. In the TPS framework, doses have hereto been reported as dose to water and water may still be preferred as a dose specification medium. Dose to tissue medium Dmed then needs to be converted into dose to water in tissue Dw,med. Methods to calculate absorbed dose to differently sized water compartments/cavities inside tissue, infinitesimal (used for definition of absorbed dose), small, large or intermediate, are reviewed. Burlin theory is applied to estimate photon energies at which cavity sizes in the range 1 nm-10 mm can be considered small or large. Photon and electron energy spectra are calculated at 1 cm distance from the central axis in cylindrical phantoms of bone, muscle and adipose tissue for 20, 50, 300 keV photons and photons from 125I, 169Yb and 192Ir sources; ratios of mass-collision-stopping powers and mass energy absorption coefficients are calculated as applicable to convert Dmed into Dw,med for small and large cavities. Results show that 1-10 nm sized cavities are small at all investigated photon energies; 100 µm cavities are large only at photon energies <20 keV. A choice of an appropriate conversion coefficient Dw, med/Dmed is discussed in terms of the cavity size in relation to the size of important cellular targets. Free radicals from DNA bound water of nanometre dimensions contribute to DNA damage and cell killing and may be the most important water compartment in cells implying use of ratios of mass-collision-stopping powers for converting Dmed into Dw,med.
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.
NASA Astrophysics Data System (ADS)
Ramli, Razamin; Tein, Lim Huai
2016-08-01
A good work schedule can improve hospital operations by providing better coverage with appropriate staffing levels in managing nurse personnel. Hence, constructing the best nurse work schedule is the appropriate effort. In doing so, an improved selection operator in the Evolutionary Algorithm (EA) strategy for a nurse scheduling problem (NSP) is proposed. The smart and efficient scheduling procedures were considered. Computation of the performance of each potential solution or schedule was done through fitness evaluation. The best so far solution was obtained via special Maximax&Maximin (MM) parent selection operator embedded in the EA, which fulfilled all constraints considered in the NSP.
Yi, Jianbing; Yang, Xuan Li, Yan-Ran; Chen, Guoliang
2015-10-15
Purpose: Image-guided radiotherapy is an advanced 4D radiotherapy technique that has been developed in recent years. However, respiratory motion causes significant uncertainties in image-guided radiotherapy procedures. To address these issues, an innovative lung motion estimation model based on a robust point matching is proposed in this paper. Methods: An innovative robust point matching algorithm using dynamic point shifting is proposed to estimate patient-specific lung motion during free breathing from 4D computed tomography data. The correspondence of the landmark points is determined from the Euclidean distance between the landmark points and the similarity between the local images that are centered at points at the same time. To ensure that the points in the source image correspond to the points in the target image during other phases, the virtual target points are first created and shifted based on the similarity between the local image centered at the source point and the local image centered at the virtual target point. Second, the target points are shifted by the constrained inverse function mapping the target points to the virtual target points. The source point set and shifted target point set are used to estimate the transformation function between the source image and target image. Results: The performances of the authors’ method are evaluated on two publicly available DIR-lab and POPI-model lung datasets. For computing target registration errors on 750 landmark points in six phases of the DIR-lab dataset and 37 landmark points in ten phases of the POPI-model dataset, the mean and standard deviation by the authors’ method are 1.11 and 1.11 mm, but they are 2.33 and 2.32 mm without considering image intensity, and 1.17 and 1.19 mm with sliding conditions. For the two phases of maximum inhalation and maximum exhalation in the DIR-lab dataset with 300 landmark points of each case, the mean and standard deviation of target registration errors on the
NASA Astrophysics Data System (ADS)
Cody, B. M.; Gonzalez-Nicolas, A.; Bau, D. A.
2011-12-01
Carbon capture and storage (CCS) has been proposed as a method of reducing global carbon dioxide (CO2) emissions. Although CCS has the potential to greatly retard greenhouse gas loading to the atmosphere while cleaner, more sustainable energy solutions are developed, there is a possibility that sequestered CO2 may leak and intrude into and adversely affect groundwater resources. It has been reported [1] that, while CO2 intrusion typically does not directly threaten underground drinking water resources, it may cause secondary effects, such as the mobilization of hazardous inorganic constituents present in aquifer minerals and changes in pH values. These risks must be fully understood and minimized before CCS project implementation. Combined management of project resources and leakage risk is crucial for the implementation of CCS. In this work, we present a method of: (a) minimizing the total CCS cost, the summation of major project costs with the cost associated with CO2 leakage; and (b) maximizing the mass of injected CO2, for a given proposed sequestration site. Optimization decision variables include the number of CO2 injection wells, injection rates, and injection well locations. The capital and operational costs of injection wells are directly related to injection well depth, location, injection flow rate, and injection duration. The cost of leakage is directly related to the mass of CO2 leaked through weak areas, such as abandoned oil wells, in the cap rock layers overlying the injected formation. Additional constraints on fluid overpressure caused by CO2 injection are imposed to maintain predefined effective stress levels that prevent cap rock fracturing. Here, both mass leakage and fluid overpressure are estimated using two semi-analytical models based upon work by [2,3]. A multi-objective evolutionary algorithm coupled with these semi-analytical leakage flow models is used to determine Pareto-optimal trade-off sets giving minimum total cost vs. maximum mass
NASA Astrophysics Data System (ADS)
Marghany, M.
2015-06-01
Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.
NASA Astrophysics Data System (ADS)
Gosálvez, M. A.; Ferrando, N.; Xing, Y.; Pal, Prem; Sato, K.; Cerdá, J.; Gadea, R.
2011-06-01
An evolutionary algorithm is presented for the automated calibration of the continuous cellular automaton for the simulation of isotropic and anisotropic wet chemical etching of silicon in as many as 31 widely different and technologically relevant etchants, including KOH, KOH+IPA, TMAH and TMAH+Triton, in various concentrations and temperatures. Based on state-of-the-art evolutionary operators, we implement a robust algorithm for the simultaneous optimization of roughly 150 microscopic removal rates based on the minimization of a cost function with four quantitative error measures, including (i) the error between simulated and experimental macroscopic etch rates for numerous surface orientations all over the unit sphere, (ii) the error due to underetching asymmetries and floor corrugation features observed in simulated silicon samples masked using a circular pattern, (iii) the error associated with departures from a step-flow-based hierarchy in the values of the microscopic removal rates, and (iv) the error associated with deviations from a step-flow-based clustering of the microscopic removal rates. For the first time, we present the calibration and successful simulation of two technologically relevant CMOS compatible etchants, namely TMAH and, especially, TMAH+Triton, providing several comparisons between simulated and experimental MEMS structures based on multi-step etching in these etchants.
Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data
Bywater, Robert P.
2016-01-01
Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before. PMID:26963911
Mutual information image registration based on improved bee evolutionary genetic algorithm
NASA Astrophysics Data System (ADS)
Xu, Gang; Tu, Jingzhi
2009-07-01
In recent years, the mutual information is regarded as a more efficient similarity metrics in the image registration. According to the features of mutual information image registration, the Bee Evolution Genetic Algorithm (BEGA) is chosen for optimizing parameters, which imitates swarm mating. Besides, we try our best adaptively set the initial parameters to improve the BEGA. The programming result shows the wonderful precision of the algorithm.
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.
Alagar, Ananda Giri Babu; Kadirampatti Mani, Ganesh; Karunakaran, Kaviarasu
2016-01-01
Small fields smaller than 4 × 4 cm2 are used in stereotactic and conformal treatments where heterogeneity is normally present. Since dose calculation accuracy in both small fields and heterogeneity often involves more discrepancy, algorithms used by treatment planning systems (TPS) should be evaluated for achieving better treatment results. This report aims at evaluating accuracy of four model-based algorithms, X-ray Voxel Monte Carlo (XVMC) from Monaco, Superposition (SP) from CMS-Xio, AcurosXB (AXB) and analytical anisotropic algorithm (AAA) from Eclipse are tested against the measurement. Measurements are done using Exradin W1 plastic scintillator in Solid Water phantom with heterogeneities like air, lung, bone, and aluminum, irradiated with 6 and 15 MV photons of square field size ranging from 1 to 4 cm2. Each heterogeneity is introduced individually at two different depths from depth-of-dose maximum (Dmax), one setup being nearer and another farther from the Dmax. The central axis percentage depth-dose (CADD) curve for each setup is measured separately and compared with the TPS algorithm calculated for the same setup. The percentage normalized root mean squared deviation (%NRMSD) is calculated, which represents the whole CADD curve's deviation against the measured. It is found that for air and lung heterogeneity, for both 6 and 15 MV, all algorithms show maximum deviation for field size 1 × 1 cm2 and gradually reduce when field size increases, except for AAA. For aluminum and bone, all algorithms' deviations are less for 15 MV irrespective of setup. In all heterogeneity setups, 1 × 1 cm2 field showed maximum deviation, except in 6MV bone setup. All algorithms in the study, irrespective of energy and field size, when any heterogeneity is nearer to Dmax, the dose deviation is higher compared to the same heterogeneity far from the Dmax. Also, all algorithms show maximum deviation in lower-density materials compared to high-density materials. PMID:26894345
Holdsworth, Clay; Kim, Minsun; Liao, Jay; Phillips, Mark
2012-01-01
Purpose: To evaluate how a more flexible and thorough multiobjective search of feasible IMRT plans affects performance in IMRT optimization. Methods: A multiobjective evolutionary algorithm (MOEA) was used as a tool to investigate how expanding the search space to include a wider range of penalty functions affects the quality of the set of IMRT plans produced. The MOEA uses a population of IMRT plans to generate new IMRT plans through deterministic minimization of recombined penalty functions that are weighted sums of multiple, tissue-specific objective functions. The quality of the generated plans are judged by an independent set of nonconvex, clinically relevant decision criteria, and all dominated plans are eliminated. As this process repeats itself, better plans are produced so that the population of IMRT plans will approach the Pareto front. Three different approaches were used to explore the effects of expanding the search space. First, the evolutionary algorithm used genetic optimization principles to search by simultaneously optimizing both the weights and tissue-specific dose parameters in penalty functions. Second, penalty function parameters were individually optimized for each voxel in all organs at risk (OARs) in the MOEA. Finally, a heuristic voxel-specific improvement (VSI) algorithm that can be used on any IMRT plan was developed that incrementally improves voxel-specific penalty function parameters for all structures (OARs and targets). Different approaches were compared using the concept of domination comparison applied to the sets of plans obtained by multiobjective optimization. Results: MOEA optimizations that simultaneously searched both importance weights and dose parameters generated sets of IMRT plans that were superior to sets of plans produced when either type of parameter was fixed for four example prostate plans. The amount of improvement increased with greater overlap between OARs and targets. Allowing the MOEA to search for voxel
Kyriacou, Theocharis
2012-04-01
A biologically inspired model of head direction cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the host's location in the environment. The head direction system thus acts as a biological compass (though not a magnetic one) for its host. Head direction cells are influenced in different ways by idiothetic (host-centred) and allothetic (not host-centred) cues. The model presented here uses the visual, vestibular and kinesthetic inputs that are simulated by robot sensors. Real robot-sensor data has been used in order to train the model's artificial neural network connections. The main contribution of this paper lies in the use of an evolutionary algorithm in order to determine the values of parameters that determine the behaviour of the model. More importantly, the objective function of the evolutionary strategy used takes into consideration quantitative biological observations reported in the literature. PMID:21785973
Srinivasan, D.; Tettamanzi, A.G.B.
1997-02-01
An integrated framework for modeling and evaluating the economic impacts of environmental dispatching and fuel switching is presented in this paper. It explores the potential for operational changes in utility commitment and dispatching to achieve least cost operation while complying to rigorous environmental standards. The work reported here employs a heuristics-guided evolutionary algorithm to solve this multiobjective constrained optimization problem, and provides the decision maker a whole range of alternatives along the Pareto-optimal frontier. Heuristics are used to ensure the feasibility of each solution, and to reduce the computation time. The capabilities of this approach are illustrated via tests on a 19-unit system. Various emission compliance strategies are considered to reveal the economic trade-offs that come into play.
NASA Astrophysics Data System (ADS)
Al-Aqtash, Nabil; Sabirianov, Renat
2014-03-01
The evolutionary algorithm coupled with the first-principles Density Functional Theory (DFT) method is used to identify the global energy minimum structure of Fe3Se4. The structure is processed by free-energy based evolutionary crystal structure optimization algorithms, as implemented USPEX and XtalOpt codes, which predict structure of the system solely based on the chemical formula without prior experimental information. This is very challenging task for verifying the validity of this approach on Fe3Se4 structure. Fe3Se4 has highly anisotropic structure, and its structure demonstrates ordering of vacancies that makes the system ``open'', i.e. breaking traditional coordination rules. By using USPEX and XtalOpt we identify the global minimum of Fe3Se4 structure. The randomly generated initial population had 20 structures. The enthalpy (tolerance of 0.002 eV), and space group were used for niching. The enthalpy of the lowest energy structure, out of 700 generated structures that were generated, is (-81.126 eV). Bulk Fe3Se4 has a monoclinic structure with a space group of I2/m and a = 6.208Å, b = 3.541Å, and c = 11.281Å. The crystal structure and the lattice parameters of Fe3Se4 optimized from our calculations are similar to the experimental existing structure parameters. Fe3Se4 exhibits large magnetocrystalline anisotropy of 6x106 erg/cm3 and coercivity up to 40kOe due to its unusual properties.
Zhou, Jianzhong; Song, Lixiang; Kursan, Suncana; Liu, Yi
2015-05-01
A two-dimensional coupled water quality model is developed for modeling the flow-mass transport in shallow water. To simulate shallow flows on complex topography with wetting and drying, an unstructured grid, well-balanced, finite volume algorithm is proposed for numerical resolution of a modified formulation of two-dimensional shallow water equations. The slope-limited linear reconstruction method is used to achieve second-order accuracy in space. The algorithm adopts a HLLC-based integrated solver to compute the flow and mass transport fluxes simultaneously, and uses Hancock's predictor-corrector scheme for efficient time stepping as well as second-order temporal accuracy. The continuity and momentum equations are updated in both wet and dry cells. A new hybrid method, which can preserve the well-balanced property of the algorithm for simulations involving flooding and recession, is proposed for bed slope terms approximation. The effectiveness and robustness of the proposed algorithm are validated by the reasonable good agreement between numerical and reference results of several benchmark test cases. Results show that the proposed coupled flow-mass transport model can simulate complex flows and mass transport in shallow water. PMID:25686488
Xiao, Chuan-Le; Chen, Xiao-Zhou; Du, Yang-Li; Sun, Xuesong; Zhang, Gong; He, Qing-Yu
2013-01-01
Mass spectrometry has become one of the most important technologies in proteomic analysis. Tandem mass spectrometry (LC-MS/MS) is a major tool for the analysis of peptide mixtures from protein samples. The key step of MS data processing is the identification of peptides from experimental spectra by searching public sequence databases. Although a number of algorithms to identify peptides from MS/MS data have been already proposed, e.g. Sequest, OMSSA, X!Tandem, Mascot, etc., they are mainly based on statistical models considering only peak-matches between experimental and theoretical spectra, but not peak intensity information. Moreover, different algorithms gave different results from the same MS data, implying their probable incompleteness and questionable reproducibility. We developed a novel peptide identification algorithm, ProVerB, based on a binomial probability distribution model of protein tandem mass spectrometry combined with a new scoring function, making full use of peak intensity information and, thus, enhancing the ability of identification. Compared with Mascot, Sequest, and SQID, ProVerB identified significantly more peptides from LC-MS/MS data sets than the current algorithms at 1% False Discovery Rate (FDR) and provided more confident peptide identifications. ProVerB is also compatible with various platforms and experimental data sets, showing its robustness and versatility. The open-source program ProVerB is available at http://bioinformatics.jnu.edu.cn/software/proverb/ . PMID:23163785
Constructing large-scale genetic maps using an evolutionary strategy algorithm.
Mester, D; Ronin, Y; Minkov, D; Nevo, E; Korol, A
2003-01-01
This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with approximately 50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology. PMID:14704202
Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao
2015-01-01
Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly
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
Ingle, Atul; Varghese, Tomy
2014-01-01
Tissue stiffness estimation plays an important role in cancer detection and treatment. The presence of stiffer regions in healthy tissue can be used as an indicator for the possibility of pathological changes. Electrode vibration elastography involves tracking of a mechanical shear wave in tissue using radio-frequency ultrasound echoes. Based on appropriate assumptions on tissue elasticity, this approach provides a direct way of measuring tissue stiffness from shear wave velocity, and enabling visualization in the form of tissue stiffness maps. In this study, two algorithms for shear wave velocity reconstruction in an electrode vibration setup are presented. The first method models the wave arrival time data using a hidden Markov model whose hidden states are local wave velocities that are estimated using a particle filter implementation. This is compared to a direct optimization-based function fitting approach that uses sequential quadratic programming to estimate the unknown velocities and locations of interfaces. The mean shear wave velocities obtained using the two algorithms are within 10%of each other. Moreover, the Young’s modulus estimates obtained from an incompressibility assumption are within 15 kPa of those obtained from the true stiffness data obtained from mechanical testing. Based on visual inspection of the two filtering algorithms, the particle filtering method produces smoother velocity maps. PMID:25285187
NASA Astrophysics Data System (ADS)
Palacin, J.; Salleras, M.; Puig, M.; Samitier, J.; Marco, S.
2004-07-01
In this work, we approach the problem of extracting a dynamic multiport thermal compact model from thermal impedance transients of microsystems using genetic algorithms. The model takes the form of a unique RC network, using a thermal-electrical analogy. The model topology is codified in a binary chromosoma and nonlinear least squares is used for sizing their components. The compact model topology evolution is genetically controlled to obtain the RC network that minimizes the reconstruction error of the thermal impedance transients. As an example, the proposed methodology is applied to an innovative silicon microthruster and compared with random search and sequential forward selection.
On the Effects of Migration on the Fitness Distribution of Parallel Evolutionary Algorithms
Cantu-Paz, E.
2000-04-25
Migration of individuals between populations may increase the selection pressure. This has the desirable consequence of speeding up convergence, but it may result in an excessively rapid loss of variation that may cause the search to fail. This paper investigates the effects of migration on the distribution of fitness. It considers arbitrary migration rates and topologies with different number of neighbors, and it compares algorithms that are configured to have the same selection intensity. The results suggest that migration preserves more diversity as the number of neighbors of a deme increases.
NASA Astrophysics Data System (ADS)
Lu, Yujie; Zhu, Banghe; Darne, Chinmay; Tan, I.-Chih; Rasmussen, John C.; Sevick-Muraca, Eva M.
2011-12-01
The goal of preclinical fluorescence-enhanced optical tomography (FEOT) is to provide three-dimensional fluorophore distribution for a myriad of drug and disease discovery studies in small animals. Effective measurements, as well as fast and robust image reconstruction, are necessary for extensive applications. Compared to bioluminescence tomography (BLT), FEOT may result in improved image quality through higher detected photon count rates. However, background signals that arise from excitation illumination affect the reconstruction quality, especially when tissue fluorophore concentration is low and/or fluorescent target is located deeply in tissues. We show that near-infrared fluorescence (NIRF) imaging with an optimized filter configuration significantly reduces the background noise. Model-based reconstruction with a high-order approximation to the radiative transfer equation further improves the reconstruction quality compared to the diffusion approximation. Improvements in FEOT are demonstrated experimentally using a mouse-shaped phantom with targets of pico- and subpico-mole NIR fluorescent dye.
Hsieh, PingHsun; Woerner, August E; Wall, Jeffrey D; Lachance, Joseph; Tishkoff, Sarah A; Gutenkunst, Ryan N; Hammer, Michael F
2016-03-01
Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr. PMID:26888264
Hsieh, PingHsun; Woerner, August E.; Wall, Jeffrey D.; Lachance, Joseph; Tishkoff, Sarah A.; Gutenkunst, Ryan N.; Hammer, Michael F.
2016-01-01
Comparisons of whole-genome sequences from ancient and contemporary samples have pointed to several instances of archaic admixture through interbreeding between the ancestors of modern non-Africans and now extinct hominids such as Neanderthals and Denisovans. One implication of these findings is that some adaptive features in contemporary humans may have entered the population via gene flow with archaic forms in Eurasia. Within Africa, fossil evidence suggests that anatomically modern humans (AMH) and various archaic forms coexisted for much of the last 200,000 yr; however, the absence of ancient DNA in Africa has limited our ability to make a direct comparison between archaic and modern human genomes. Here, we use statistical inference based on high coverage whole-genome data (greater than 60×) from contemporary African Pygmy hunter-gatherers as an alternative means to study the evolutionary history of the genus Homo. Using whole-genome simulations that consider demographic histories that include both isolation and gene flow with neighboring farming populations, our inference method rejects the hypothesis that the ancestors of AMH were genetically isolated in Africa, thus providing the first whole genome-level evidence of African archaic admixture. Our inferences also suggest a complex human evolutionary history in Africa, which involves at least a single admixture event from an unknown archaic population into the ancestors of AMH, likely within the last 30,000 yr. PMID:26888264
NASA Astrophysics Data System (ADS)
Hu, Yan-Yan; Li, Dong-Sheng
2016-01-01
The hyperspectral images(HSI) consist of many closely spaced bands carrying the most object information. While due to its high dimensionality and high volume nature, it is hard to get satisfactory classification performance. In order to reduce HSI data dimensionality preparation for high classification accuracy, it is proposed to combine a band selection method of artificial immune systems (AIS) with a hybrid kernels support vector machine (SVM-HK) algorithm. In fact, after comparing different kernels for hyperspectral analysis, the approach mixed radial basis function kernel (RBF-K) with sigmoid kernel (Sig-K) and applied the optimized hybrid kernels in SVM classifiers. Then the SVM-HK algorithm used to induce the bands selection of an improved version of AIS. The AIS was composed of clonal selection and elite antibody mutation, including evaluation process with optional index factor (OIF). Experimental classification performance was on a San Diego Naval Base acquired by AVIRIS, the HRS dataset shows that the method is able to efficiently achieve bands redundancy removal while outperforming the traditional SVM classifier.
NASA Astrophysics Data System (ADS)
Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.
2014-12-01
As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a
NASA Astrophysics Data System (ADS)
Liu, Jiulong; Zhang, Xue; Zhang, Xiaoqun; Zhao, Hongkai; Gao, Yu; Thomas, David; Low, Daniel A.; Gao, Hao
2015-11-01
4D cone-beam computed tomography (4DCBCT) reconstructs a temporal sequence of CBCT images for the purpose of motion management or 4D treatment in radiotherapy. However the image reconstruction often involves the binning of projection data to each temporal phase, and therefore suffers from deteriorated image quality due to inaccurate or uneven binning in phase, e.g., under the non-periodic breathing. A 5D model has been developed as an accurate model of (periodic and non-periodic) respiratory motion. That is, given the measurements of breathing amplitude and its time derivative, the 5D model parametrizes the respiratory motion by three time-independent variables, i.e., one reference image and two vector fields. In this work we aim to develop a new 4DCBCT reconstruction method based on 5D model. Instead of reconstructing a temporal sequence of images after the projection binning, the new method reconstructs time-independent reference image and vector fields with no requirement of binning. The image reconstruction is formulated as a optimization problem with total-variation regularization on both reference image and vector fields, and the problem is solved by the proximal alternating minimization algorithm, during which the split Bregman method is used to reconstruct the reference image, and the Chambolle's duality-based algorithm is used to reconstruct the vector fields. The convergence analysis of the proposed algorithm is provided for this nonconvex problem. Validated by the simulation studies, the new method has significantly improved image reconstruction accuracy due to no binning and reduced number of unknowns via the use of the 5D model.
NASA Astrophysics Data System (ADS)
Knapmeyer-Endrun, B.; Hammer, C.
2014-12-01
The seismometers that the Apollo astronauts deployed on the Moon provide the only recordings of seismic events from any extra-terrestrial body so far. These lunar events are significantly different from ones recorded on Earth, in terms of both signal shape and source processes. Thus they are a valuable test case for any experiment in planetary seismology. In this study, we analyze Apollo 16 data with a single-station event detection and classification algorithm in view of NASA's upcoming InSight mission to Mars. InSight, scheduled for launch in early 2016, has the goal to investigate Mars' internal structure by deploying a seismometer on its surface. As the mission does not feature any orbiter, continuous data will be relayed to Earth at a reduced rate. Full range data will only be available by requesting specific time-windows within a few days after the receipt of the original transmission. We apply a recently introduced algorithm based on hidden Markov models that requires only a single example waveform of each event class for training appropriate models. After constructing the prototypes we detect and classify impacts and deep and shallow moonquakes. Initial results for 1972 (year of station installation with 8 months of data) indicate a high detection rate of over 95% for impacts, of which more than 80% are classified correctly. Deep moonquakes, which occur in large amounts, but often show only very weak signals, are detected with less certainty (~70%). As there is only one weak shallow moonquake covered, results for this event class are not statistically significant. Daily adjustments of the background noise model help to reduce false alarms, which are mainly erroneous deep moonquake detections, by about 25%. The algorithm enables us to classify events that were previously listed in the catalog without classification, and, through the combined use of long period and short period data, identify some unlisted local impacts as well as at least two yet unreported
Searching good strategies in evolutionary minority game using variable length genetic algorithm
NASA Astrophysics Data System (ADS)
Yang, Wei-Song; Wang, Bing-Hong; Wu, Yi-Lin; Xie, Yan-Bo
2004-08-01
We propose and study a new adaptation minority game for understanding the complex dynamical behavior characterized by agent interactions competing limited resource in many natural and social systems. We compare the strategy of agents in the model to chromosome in biology. In our model, the agents with poor performance during certain time period may modify their strategies via variable length genetic algorithm which consists of cut and splice operator, imitating similar processes in biology. The performances of the agents in our model are calculated for different parameter conditions and different evolution mechanism. It is found that the system may evolve into a much more ideal equilibrium state, which implies much stronger cooperation among agents and much more effective utilization of the social resources. It is also found that the distribution of the strategies held by agents will tend towards a state concentrating upon small m region.
NASA Astrophysics Data System (ADS)
Amian, M.; Setarehdan, S. Kamaledin; Yousefi, H.
2014-09-01
Functional Near infrared spectroscopy (fNIRS) is a newly noninvasive way to measure oxy hemoglobin and deoxy hemoglobin concentration changes of human brain. Relatively safe and affordable than other functional imaging techniques such as fMRI, it is widely used for some special applications such as infant examinations and pilot's brain monitoring. In such applications, fNIRS data sometimes suffer from undesirable movements of subject's head which called motion artifact and lead to a signal corruption. Motion artifact in fNIRS data may result in fallacy of concluding or diagnosis. In this work we try to reduce these artifacts by a novel Kalman filtering algorithm that is based on an autoregressive moving average (ARMA) model for fNIRS system. Our proposed method does not require to any additional hardware and sensor and also it does not need to whole data together that once were of ineluctable necessities in older algorithms such as adaptive filter and Wiener filtering. Results show that our approach is successful in cleaning contaminated fNIRS data.
Mei, Xue; Li, Zhenhua; Xu, Songsong; Guo, Xiaoyan
2014-01-01
Multimodality image registration and fusion has complementary significance for guiding dental implant surgery. As the needs of the different resolution image registration, we develop an improved Iterative Closest Point (ICP) algorithm that focuses on the registration of Cone Beam Computed Tomography (CT) image and high-resolution Blue-light scanner image. The proposed algorithm includes two major phases, coarse and precise registration. Firstly, for reducing the matching interference of human subjective factors, we extract feature points based on curvature characteristics and use the improved three point's translational transformation method to realize coarse registration. Then, the feature point set and reference point set, obtained by the initial registered transformation, are processed in the precise registration step. Even with the unsatisfactory initial values, this two steps registration method can guarantee the global convergence and the convergence precision. Experimental results demonstrate that the method has successfully realized the registration of the Cone Beam CT dental model and the blue-ray scanner model with higher accuracy. So the method could provide researching foundation for the relevant software development in terms of the registration of multi-modality medical data. PMID:24511309
2013-01-01
Background Proteins are essential biological molecules which play vital roles in nearly all biological processes. It is the tertiary structure of a protein that determines its functions. Therefore the prediction of a protein's tertiary structure based on its primary amino acid sequence has long been the most important and challenging subject in biochemistry, molecular biology and biophysics. In the past, the HP lattice model was one of the ab initio methods that many researchers used to forecast the protein structure. Although these kinds of simplified methods could not achieve high resolution, they provided a macrocosm-optimized protein structure. The model has been employed to investigate general principles of protein folding, and plays an important role in the prediction of protein structures. Methods In this paper, we present an improved evolutionary algorithm for the protein folding problem. We study the problem on the 3D FCC lattice HP model which has been widely used in previous research. Our focus is to develop evolutionary algorithms (EA) which are robust, easy to implement and can handle various energy functions. We propose to combine three different local search methods, including lattice rotation for crossover, K-site move for mutation, and generalized pull move; these form our key components to improve previous EA-based approaches. Results We have carried out experiments over several data sets which were used in previous research. The results of the experiments show that our approach is able to find optimal conformations which were not found by previous EA-based approaches. Conclusions We have investigated the geometric properties of the 3D FCC lattice and developed several local search techniques to improve traditional EA-based approaches to the protein folding problem. It is known that EA-based approaches are robust and can handle arbitrary energy functions. Our results further show that by extensive development of local searches, EA can also be very
NASA Astrophysics Data System (ADS)
Tang, Shihua; Li, Feida; Liu, Yintao; Lan, Lan; Zhou, Conglin; Huang, Qing
2015-12-01
With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods'. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.
NASA Astrophysics Data System (ADS)
An, Zhao; Zhounian, Lai; Peng, Wu; Linlin, Cao; Dazhuan, Wu
2016-07-01
This paper describes the shape optimization of a low specific speed centrifugal pump at the design point. The target pump has already been manually modified on the basis of empirical knowledge. A genetic algorithm (NSGA-II) with certain enhancements is adopted to improve its performance further with respect to two goals. In order to limit the number of design variables without losing geometric information, the impeller is parametrized using the Bézier curve and a B-spline. Numerical simulation based on a Reynolds averaged Navier-Stokes (RANS) turbulent model is done in parallel to evaluate the flow field. A back-propagating neural network is constructed as a surrogate for performance prediction to save computing time, while initial samples are selected according to an orthogonal array. Then global Pareto-optimal solutions are obtained and analysed. The results manifest that unexpected flow structures, such as the secondary flow on the meridian plane, have diminished or vanished in the optimized pump.
Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming.
Reynolds, R G; Zhu, S
2001-01-01
In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of Cultural algorithms (CAs). Since all natural languages contain a fuzzy component, the natural question is "Does this fuzzy representation facilitate the problem-solving process, within these systems". In order to investigate this question we use the CA framework of Reynolds (1996), CAs are a computational model of cultural evolution derived from and used to express basic anthropological models of culture and its development. A mathematical model of a full fuzzy CA is developed there. In it, the problem solving knowledge is represented using a fuzzy framework. Several theoretical results concerning its properties are presented. The model is then applied to the solution of a set of 12 difficult, benchmark problems in nonlinear real-valued function optimization. The performance of the full fuzzy model is compared with 8 other fuzzy and crisp architectures. The results suggest that a fuzzy approach can produce a statistically significant improvement in search efficiency over nonfuzzy versions for the entire set of functions, the then investigate the class of performance functions for which the full fuzzy system exhibits the greatest improvements over nonfuzzy systems. In general, these are functions which require some preliminary investigation in order to embark on an effective search. PMID:18244764
Beyer, Hans-Georg
2014-01-01
The convergence behaviors of so-called natural evolution strategies (NES) and of the information-geometric optimization (IGO) approach are considered. After a review of the NES/IGO ideas, which are based on information geometry, the implications of this philosophy w.r.t. optimization dynamics are investigated considering the optimization performance on the class of positive quadratic objective functions (the ellipsoid model). Exact differential equations describing the approach to the optimizer are derived and solved. It is rigorously shown that the original NES philosophy optimizing the expected value of the objective functions leads to very slow (i.e., sublinear) convergence toward the optimizer. This is the real reason why state of the art implementations of IGO algorithms optimize the expected value of transformed objective functions, for example, by utility functions based on ranking. It is shown that these utility functions are localized fitness functions that change during the IGO flow. The governing differential equations describing this flow are derived. In the case of convergence, the solutions to these equations exhibit an exponentially fast approach to the optimizer (i.e., linear convergence order). Furthermore, it is proven that the IGO philosophy leads to an adaptation of the covariance matrix that equals in the asymptotic limit-up to a scalar factor-the inverse of the Hessian of the objective function considered. PMID:24922548
NASA Astrophysics Data System (ADS)
Arnaout, A.; Fruhwirth, R.; Winter, M.; Esmael, B.; Thonhauser, G.
2012-04-01
The use of neural networks and advanced machine learning techniques in the oil & gas industry is a growing trend in the market. Especially in drilling oil & gas wells, prediction and monitoring different drilling parameters is an essential task to prevent serious problems like "Kick", "Lost Circulation" or "Stuck Pipe" among others. The hookload represents the weight load of the drill string at the crane hook. It is one of the most important parameters. During drilling the parameter "Weight on Bit" is controlled by the driller whereby the hookload is the only measure to monitor how much weight on bit is applied to the bit to generate the hole. Any changes in weight on bit will be directly reflected at the hookload. Furthermore any unwanted contact between the drill string and the wellbore - potentially leading to stuck pipe problem - will appear directly in the measurements of the hookload. Therefore comparison of the measured to the predicted hookload will not only give a clear idea on what is happening down-hole, it also enables the prediction of a number of important events that may cause problems in the borehole and yield in some - fortunately rare - cases in catastrophes like blow-outs. Heuristic models using highly sophisticated neural networks were designed for the hookload prediction; the training data sets were prepared in cooperation with drilling experts. Sensor measurements as well as a set of derived feature channels were used as input to the models. The contents of the final data set can be separated into (1) features based on rig operation states, (2) real-time sensors features and (3) features based on physics. A combination of novel neural network architecture - the Completely Connected Perceptron and parallel learning techniques which avoid trapping into local error minima - was used for building the models. In addition automatic network growing algorithms and highly sophisticated stopping criterions offer robust and efficient estimation of the
Guerra, J G; Rubiano, J G; Winter, G; Guerra, A G; Alonso, H; Arnedo, M A; Tejera, A; Gil, J M; Rodríguez, R; Martel, P; Bolivar, J P
2015-11-01
The determination in a sample of the activity concentration of a specific radionuclide by gamma spectrometry needs to know the full energy peak efficiency (FEPE) for the energy of interest. The difficulties related to the experimental calibration make it advisable to have alternative methods for FEPE determination, such as the simulation of the transport of photons in the crystal by the Monte Carlo method, which requires an accurate knowledge of the characteristics and geometry of the detector. The characterization process is mainly carried out by Canberra Industries Inc. using proprietary techniques and methodologies developed by that company. It is a costly procedure (due to shipping and to the cost of the process itself) and for some research laboratories an alternative in situ procedure can be very useful. The main goal of this paper is to find an alternative to this costly characterization process, by establishing a method for optimizing the parameters of characterizing the detector, through a computational procedure which could be reproduced at a standard research lab. This method consists in the determination of the detector geometric parameters by using Monte Carlo simulation in parallel with an optimization process, based on evolutionary algorithms, starting from a set of reference FEPEs determined experimentally or computationally. The proposed method has proven to be effective and simple to implement. It provides a set of characterization parameters which it has been successfully validated for different source-detector geometries, and also for a wide range of environmental samples and certified materials. PMID:26188622
NASA Astrophysics Data System (ADS)
Smith, R.; Kasprzyk, J. R.; Zagona, E. A.
2015-12-01
Instead of building new infrastructure to increase their supply reliability, water resource managers are often tasked with better management of current systems. The managers often have existing simulation models that aid their planning, and lack methods for efficiently generating and evaluating planning alternatives. This presentation discusses how multiobjective evolutionary algorithm (MOEA) decision support can be used with the sophisticated water infrastructure model, RiverWare, in highly constrained water planning environments. We first discuss a study that performed a many-objective tradeoff analysis of water supply in the Tarrant Regional Water District (TRWD) in Texas. RiverWare is combined with the Borg MOEA to solve a seven objective problem that includes systemwide performance objectives and individual reservoir storage reliability. Decisions within the formulation balance supply in multiple reservoirs and control pumping between the eastern and western parts of the system. The RiverWare simulation model is forced by two stochastic hydrology scenarios to inform how management changes in wet versus dry conditions. The second part of the presentation suggests how a broader set of RiverWare-MOEA studies can inform tradeoffs in other systems, especially in political situations where multiple actors are in conflict over finite water resources. By incorporating quantitative representations of diverse parties' objectives during the search for solutions, MOEAs may provide support for negotiations and lead to more widely beneficial water management outcomes.
NASA Astrophysics Data System (ADS)
Grimminck, Dennis L. A. G.; Polman, Ben J. W.; Kentgens, Arno P. M.; Leo Meerts, W.
2011-08-01
A fast and accurate fit program is presented for deconvolution of one-dimensional solid-state quadrupolar NMR spectra of powdered materials. Computational costs of the synthesis of theoretical spectra are reduced by the use of libraries containing simulated time/frequency domain data. These libraries are calculated once and with the use of second-party simulation software readily available in the NMR community, to ensure a maximum flexibility and accuracy with respect to experimental conditions. EASY-GOING deconvolution ( EGdeconv) is equipped with evolutionary algorithms that provide robust many-parameter fitting and offers efficient parallellised computing. The program supports quantification of relative chemical site abundances and (dis)order in the solid-state by incorporation of (extended) Czjzek and order parameter models. To illustrate EGdeconv's current capabilities, we provide three case studies. Given the program's simple concept it allows a straightforward extension to include other NMR interactions. The program is available as is for 64-bit Linux operating systems.
NASA Astrophysics Data System (ADS)
Ott, Julien G.; Becce, Fabio; Monnin, Pascal; Schmidt, Sabine; Bochud, François O.; Verdun, Francis R.
2014-08-01
The state of the art to describe image quality in medical imaging is to assess the performance of an observer conducting a task of clinical interest. This can be done by using a model observer leading to a figure of merit such as the signal-to-noise ratio (SNR). Using the non-prewhitening (NPW) model observer, we objectively characterised the evolution of its figure of merit in various acquisition conditions. The NPW model observer usually requires the use of the modulation transfer function (MTF) as well as noise power spectra. However, although the computation of the MTF poses no problem when dealing with the traditional filtered back-projection (FBP) algorithm, this is not the case when using iterative reconstruction (IR) algorithms, such as adaptive statistical iterative reconstruction (ASIR) or model-based iterative reconstruction (MBIR). Given that the target transfer function (TTF) had already shown it could accurately express the system resolution even with non-linear algorithms, we decided to tune the NPW model observer, replacing the standard MTF by the TTF. It was estimated using a custom-made phantom containing cylindrical inserts surrounded by water. The contrast differences between the inserts and water were plotted for each acquisition condition. Then, mathematical transformations were performed leading to the TTF. As expected, the first results showed a dependency of the image contrast and noise levels on the TTF for both ASIR and MBIR. Moreover, FBP also proved to be dependent of the contrast and noise when using the lung kernel. Those results were then introduced in the NPW model observer. We observed an enhancement of SNR every time we switched from FBP to ASIR to MBIR. IR algorithms greatly improve image quality, especially in low-dose conditions. Based on our results, the use of MBIR could lead to further dose reduction in several clinical applications.