Genetic algorithms as discovery programs
Hilliard, M.R.; Liepins, G.
1986-01-01
Genetic algorithms are mathematical counterparts to natural selection and gene recombination. As such, they have provided one of the few significant breakthroughs in machine learning. Used with appropriate reward functions and apportionment of credit, they have been successfully applied to gas pipeline operation, x-ray registration and mathematical optimization problems. This paper discusses the basics of genetic algorithms, describes a few successes, and reports on current progress at Oak Ridge National Laboratory in applications to set covering and simulated robots.
Dynamic Programming Algorithm vs. Genetic Algorithm: Which is Faster?
NASA Astrophysics Data System (ADS)
Petković, Dušan
The article compares two different approaches for the optimization problem of large join queries (LJQs). Almost all commercial database systems use a form of the dynamic programming algorithm to solve the ordering of join operations for large join queries, i.e. joins with more than dozen join operations. The property of the dynamic programming algorithm is that the execution time increases significantly in the case, where the number of join operations in a query is large. Genetic algorithms (GAs), as a data mining technique, have been shown as a promising technique in solving the ordering of join operations in LJQs. Using the existing implementation of GA, we compare the dynamic programming algorithm implemented in commercial database systems with the corresponding GA module. Our results show that the use of a genetic algorithm is a better solution for optimization of large join queries, i.e., that such a technique outperforms the implementations of the dynamic programming algorithm in conventional query optimization components for very large join queries.
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME
J. THEILER; ET AL
1999-06-01
The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.
Bardsiri, Mahshid Khatibi; Eftekhari, Mahdi; Mousavi, Reza
2015-01-01
In this study the problem of protein fold recognition, that is a classification task, is solved via a hybrid of evolutionary algorithms namely multi-gene Genetic Programming (GP) and Genetic Algorithm (GA). Our proposed method consists of two main stages and is performed on three datasets taken from the literature. Each dataset contains different feature groups and classes. In the first step, multi-gene GP is used for producing binary classifiers based on various feature groups for each class. Then, different classifiers obtained for each class are combined via weighted voting so that the weights are determined through GA. At the end of the first step, there is a separate binary classifier for each class. In the second stage, the obtained binary classifiers are combined via GA weighting in order to generate the overall classifier. The final obtained classifier is superior to the previous works found in the literature in terms of classification accuracy.
NASA Astrophysics Data System (ADS)
Niwa, Keiichi; Hayashida, Tomohiro; Sakawa, Masatoshi; Yang, Yishen
2010-10-01
We consider two-level programming problems in which there are one decision maker (the leader) at the upper level and two or more decision makers (the followers) at the lower level and decision variables of the leader and the followers are 0-1 variables. We assume that there is coordination among the followers while between the leader and the group of all the followers, there is no motivation to cooperate each other, and fuzzy goals for objective functions of the leader and the followers are introduced so as to take fuzziness of their judgments into consideration. The leader maximizes the degree of satisfaction (the value of the membership function) and the followers choose in concert in order to maximize a minimum among their degrees of satisfaction. We propose a modified computational method that solves problems related to the computational method based on the genetic algorithm (the existing method) for obtaining the Stackelberg solution. Specifically, the distributed genetic algorithm is introduced with respect to the upper level genetic algorithm, which handles decision variables for the leader in order to shorten the computational time of the existing method. Parallelization of the lower level genetic algorithm is also performed along with parallelization of the upper level genetic algorithm. In order to demonstrate the effectiveness of the proposed computational method, numerical experiments are carried out.
Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua
2013-01-01
Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.
NASA Astrophysics Data System (ADS)
Biswas, Papun; Chakraborti, Debjani
2010-10-01
This paper describes how the genetic algorithms (GAs) can be efficiently used to fuzzy goal programming (FGP) formulation of optimal power flow problems having multiple objectives. In the proposed approach, the different constraints, various relationships of optimal power flow calculations are fuzzily described. In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first for measuring the degree of achievement of the aspiration levels of the goals specified in the decision making context. Then, the achievement function for minimizing the regret for under-deviations from the highest membership value (unity) of the defined membership goals to the extent possible on the basis of priorities is constructed for optimal power flow problems. In the solution process, the GA method is employed to the FGP formulation of the problem for achievement of the highest membership value (unity) of the defined membership functions to the extent possible in the decision making environment. In the GA based solution search process, the conventional Roulette wheel selection scheme, arithmetic crossover and random mutation are taken into consideration to reach a satisfactory decision. The developed method has been tested on IEEE 6-generator 30-bus System. Numerical results show that this method is promising for handling uncertain constraints in practical power systems.
Genetic algorithms and MCML program for recovery of optical properties of homogeneous turbid media
Morales Cruzado, Beatriz; y Montiel, Sergio Vázquez; Atencio, José Alberto Delgado
2013-01-01
In this paper, we present and validate a new method for optical properties recovery of turbid media with slab geometry. This method is an iterative method that compares diffuse reflectance and transmittance, measured using integrating spheres, with those obtained using the known algorithm MCML. The search procedure is based in the evolution of a population due to selection of the best individual, i.e., using a genetic algorithm. This new method includes several corrections such as non-linear effects in integrating spheres measurements and loss of light due to the finite size of the sample. As a potential application and proof-of-principle experiment of this new method, we use this new algorithm in the recovery of optical properties of blood samples at different degrees of coagulation. PMID:23504404
NASA Astrophysics Data System (ADS)
Gladwin, D.; Stewart, P.; Stewart, J.
2011-02-01
This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control
Constraints in Genetic Programming
NASA Technical Reports Server (NTRS)
Janikow, Cezary Z.
1996-01-01
Genetic programming refers to a class of genetic algorithms utilizing generic representation in the form of program trees. For a particular application, one needs to provide the set of functions, whose compositions determine the space of program structures being evolved, and the set of terminals, which determine the space of specific instances of those programs. The algorithm searches the space for the best program for a given problem, applying evolutionary mechanisms borrowed from nature. Genetic algorithms have shown great capabilities in approximately solving optimization problems which could not be approximated or solved with other methods. Genetic programming extends their capabilities to deal with a broader variety of problems. However, it also extends the size of the search space, which often becomes too large to be effectively searched even by evolutionary methods. Therefore, our objective is to utilize problem constraints, if such can be identified, to restrict this space. In this publication, we propose a generic constraint specification language, powerful enough for a broad class of problem constraints. This language has two elements -- one reduces only the number of program instances, the other reduces both the space of program structures as well as their instances. With this language, we define the minimal set of complete constraints, and a set of operators guaranteeing offspring validity from valid parents. We also show that these operators are not less efficient than the standard genetic programming operators if one preprocesses the constraints - the necessary mechanisms are identified.
Genetic Algorithms and Local Search
NASA Technical Reports Server (NTRS)
Whitley, Darrell
1996-01-01
The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.
NASA Astrophysics Data System (ADS)
Qiu, J. P.; Niu, D. X.
Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.
NASA Astrophysics Data System (ADS)
Chen, Zheng; Mi, Chris Chunting; Xiong, Rui; Xu, Jun; You, Chenwen
2014-02-01
This paper introduces an online and intelligent energy management controller to improve the fuel economy of a power-split plug-in hybrid electric vehicle (PHEV). Based on analytic analysis between fuel-rate and battery current at different driveline power and vehicle speed, quadratic equations are applied to simulate the relationship between battery current and vehicle fuel-rate. The power threshold at which engine is turned on is optimized by genetic algorithm (GA) based on vehicle fuel-rate, battery state of charge (SOC) and driveline power demand. The optimal battery current when the engine is on is calculated using quadratic programming (QP) method. The proposed algorithm can control the battery current effectively, which makes the engine work more efficiently and thus reduce the fuel-consumption. Moreover, the controller is still applicable when the battery is unhealthy. Numerical simulations validated the feasibility of the proposed controller.
Mousavi, Seyed Mahmoud; Husseinzadeh, Danial; Alikhani, Sadegh
2014-04-01
Efficient models are required to predict the optimum values of ozone concentration in different levels of its precursors' concentrations and temperatures. A novel model based on the application of a genetic programming (GP) optimization is presented in this article. Ozone precursors' concentrations and run time average temperature have been chosen as model's parameters. Generalization performances of two different homemade models based on genetic programming and genetic algorithm (GA), which can be used for calculating theoretical ozone concentration, are compared with conventional semi-empirical model performance. Experimental data of Mashhad city ambient air have been employed to investigate the prediction ability of properly trained GP, GA, and conventional semi-empirical models. It is clearly demonstrated that the in-house algorithm which is used for the model based on GP, provides better generalization performance over the model optimized with GA and the conventional semi-empirical ones. The proposed model is found accurate enough and can be used for urban air ozone concentration prediction.
NASA Astrophysics Data System (ADS)
Paino, A.; Keller, J.; Popescu, M.; Stone, K.
2014-06-01
In this paper we present an approach that uses Genetic Programming (GP) to evolve novel feature extraction algorithms for greyscale images. Our motivation is to create an automated method of building new feature extraction algorithms for images that are competitive with commonly used human-engineered features, such as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). The evolved feature extraction algorithms are functions defined over the image space, and each produces a real-valued feature vector of variable length. Each evolved feature extractor breaks up the given image into a set of cells centered on every pixel, performs evolved operations on each cell, and then combines the results of those operations for every cell using an evolved operator. Using this method, the algorithm is flexible enough to reproduce both LBP and HOG features. The dataset we use to train and test our approach consists of a large number of pre-segmented image "chips" taken from a Forward Looking Infrared Imagery (FLIR) camera mounted on the hood of a moving vehicle. The goal is to classify each image chip as either containing or not containing a buried object. To this end, we define the fitness of a candidate solution as the cross-fold validation accuracy of the features generated by said candidate solution when used in conjunction with a Support Vector Machine (SVM) classifier. In order to validate our approach, we compare the classification accuracy of an SVM trained using our evolved features with the accuracy of an SVM trained using mainstream feature extraction algorithms, including LBP and HOG.
Scheduling with genetic algorithms
NASA Technical Reports Server (NTRS)
Fennel, Theron R.; Underbrink, A. J., Jr.; Williams, George P. W., Jr.
1994-01-01
In many domains, scheduling a sequence of jobs is an important function contributing to the overall efficiency of the operation. At Boeing, we develop schedules for many different domains, including assembly of military and commercial aircraft, weapons systems, and space vehicles. Boeing is under contract to develop scheduling systems for the Space Station Payload Planning System (PPS) and Payload Operations and Integration Center (POIC). These applications require that we respect certain sequencing restrictions among the jobs to be scheduled while at the same time assigning resources to the jobs. We call this general problem scheduling and resource allocation. Genetic algorithms (GA's) offer a search method that uses a population of solutions and benefits from intrinsic parallelism to search the problem space rapidly, producing near-optimal solutions. Good intermediate solutions are probabalistically recombined to produce better offspring (based upon some application specific measure of solution fitness, e.g., minimum flowtime, or schedule completeness). Also, at any point in the search, any intermediate solution can be accepted as a final solution; allowing the search to proceed longer usually produces a better solution while terminating the search at virtually any time may yield an acceptable solution. Many processes are constrained by restrictions of sequence among the individual jobs. For a specific job, other jobs must be completed beforehand. While there are obviously many other constraints on processes, it is these on which we focussed for this research: how to allocate crews to jobs while satisfying job precedence requirements and personnel, and tooling and fixture (or, more generally, resource) requirements.
Messy genetic algorithms: Recent developments
Kargupta, H.
1996-09-01
Messy genetic algorithms define a rare class of algorithms that realize the need for detecting appropriate relations among members of the search domain in optimization. This paper reviews earlier works in messy genetic algorithms and describes some recent developments. It also describes the gene expression messy GA (GEMGA)--an {Omicron}({Lambda}{sup {kappa}}({ell}{sup 2} + {kappa})) sample complexity algorithm for the class of order-{kappa} delineable problems (problems that can be solved by considering no higher than order-{kappa} relations) of size {ell} and alphabet size {Lambda}. Experimental results are presented to demonstrate the scalability of the GEMGA.
Genetic Algorithm Approaches for Actuator Placement
NASA Technical Reports Server (NTRS)
Crossley, William A.
2000-01-01
This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.
Problem solving with genetic algorithms and Splicer
NASA Technical Reports Server (NTRS)
Bayer, Steven E.; Wang, Lui
1991-01-01
Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.
Genetic Algorithm for Optimization: Preprocessor and Algorithm
NASA Technical Reports Server (NTRS)
Sen, S. K.; Shaykhian, Gholam A.
2006-01-01
Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.
Learning Intelligent Genetic Algorithms Using Japanese Nonograms
ERIC Educational Resources Information Center
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
New Results in Astrodynamics Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.
1998-01-01
Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.
Excursion-Set-Mediated Genetic Algorithm
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.
Cao, Buwen; Luo, Jiawei; Liang, Cheng; Wang, Shulin; Song, Dan
2015-10-01
The identification of protein complexes in protein-protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.
Saving Resources with Plagues in Genetic Algorithms
de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T
2004-06-15
The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.
Fuzzy Information Retrieval Using Genetic Algorithms and Relevance Feedback.
ERIC Educational Resources Information Center
Petry, Frederick E.; And Others
1993-01-01
Describes an approach that combines concepts from information retrieval, fuzzy set theory, and genetic programing to improve weighted Boolean query formulation via relevance feedback. Highlights include background on information retrieval systems; genetic algorithms; subproblem formulation; and preliminary results based on a testbed. (Contains 12…
Genetic-Algorithm Tool For Search And Optimization
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven
1995-01-01
SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.
NASA Astrophysics Data System (ADS)
Hsu, Chih-Ming
2014-12-01
Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.
Genetic algorithms for route discovery.
Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy
2006-12-01
Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making. PMID:17186801
Algorithmic advances in stochastic programming
Morton, D.P.
1993-07-01
Practical planning problems with deterministic forecasts of inherently uncertain parameters often yield unsatisfactory solutions. Stochastic programming formulations allow uncertain parameters to be modeled as random variables with known distributions, but the size of the resulting mathematical programs can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We consider two classes of decomposition-based stochastic programming algorithms. The first type of algorithm addresses problems with a ``manageable`` number of scenarios. The second class incorporates Monte Carlo sampling within a decomposition algorithm. We develop and empirically study an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs within a prespecified tolerance. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of ``real-world`` multistage stochastic hydroelectric scheduling problems. Recently, there has been an increased focus on decomposition-based algorithms that use sampling within the optimization framework. These approaches hold much promise for solving stochastic programs with many scenarios. A critical component of such algorithms is a stopping criterion to ensure the quality of the solution. With this as motivation, we develop a stopping rule theory for algorithms in which bounds on the optimal objective function value are estimated by sampling. Rules are provided for selecting sample sizes and terminating the algorithm under which asymptotic validity of confidence interval statements for the quality of the proposed solution can be verified. Issues associated with the application of this theory to two sampling-based algorithms are considered, and preliminary empirical coverage results are presented.
Genetic algorithms at UC Davis/LLNL
Vemuri, V.R.
1993-12-31
A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.
Genetic algorithms and supernovae type Ia analysis
Bogdanos, Charalampos; Nesseris, Savvas E-mail: nesseris@nbi.dk
2009-05-15
We introduce genetic algorithms as a means to analyze supernovae type Ia data and extract model-independent constraints on the evolution of the Dark Energy equation of state w(z) {identical_to} P{sub DE}/{rho}{sub DE}. Specifically, we will give a brief introduction to the genetic algorithms along with some simple examples to illustrate their advantages and finally we will apply them to the supernovae type Ia data. We find that genetic algorithms can lead to results in line with already established parametric and non-parametric reconstruction methods and could be used as a complementary way of treating SNIa data. As a non-parametric method, genetic algorithms provide a model-independent way to analyze data and can minimize bias due to premature choice of a dark energy model.
Adaptive sensor fusion using genetic algorithms
Fitzgerald, D.S.; Adams, D.G.
1994-08-01
Past attempts at sensor fusion have used some form of Boolean logic to combine the sensor information. As an alteniative, an adaptive ``fuzzy`` sensor fusion technique is described in this paper. This technique exploits the robust capabilities of fuzzy logic in the decision process as well as the optimization features of the genetic algorithm. This paper presents a brief background on fuzzy logic and genetic algorithms and how they are used in an online implementation of adaptive sensor fusion.
Genetic algorithms and the immune system
Forrest, S. . Dept. of Computer Science); Perelson, A.S. )
1990-01-01
Using genetic algorithm techniques we introduce a model to examine the hypothesis that antibody and T cell receptor genes evolved so as to encode the information needed to recognize schemas that characterize common pathogens. We have implemented the algorithm on the Connection Machine for 16,384 64-bit antigens and 512 64-bit antibodies. 8 refs.
Texture segmentation by genetic programming.
Song, Andy; Ciesielski, Vic
2008-01-01
This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
Genetic algorithms based on genetic grammar
NASA Astrophysics Data System (ADS)
Hofestaedt, Ralf; Mueller, Hermann
1992-08-01
The cell represents the basic unit of life. It can be interpreted as a chemical machine which can solve special problems. The present knowledge we have of molecular biology allows the characterization of the metabolism as a processing method. This method is an evolutionary product which has been developed over millions of years. First we will present the analyzed features of the metabolism. Then we will go on to compare this processing method with methods which are discussed in computer science. The comparison shows that there is no method in the field of computer science which uses all the metabolic features. This is the reason why we formalize the metabolic processing method. In this paper we choose to use a grammatical formalism. A genetic grammar is the basis of the metabolic system which represents the metabolic processing method. The basic unit of this system (logic unit) will be shown. This allows the discussion of the complexity of realizing the metabolic system in hardware.
Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
An investigation of messy genetic algorithms
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley
1990-01-01
Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris; Norvig, Peter (Technical Monitor)
2000-01-01
We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.
Robot path planning using a genetic algorithm
NASA Technical Reports Server (NTRS)
Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu
1988-01-01
Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.
Genetic algorithm used in interference filter's design
NASA Astrophysics Data System (ADS)
Li, Jinsong; Fang, Ying; Gao, Xiumin
2009-11-01
An approach for designing of interference filter is presented by using genetic algorithm (here after refer to as GA) here. We use GA to design band stop filter and narrow-band filter. Interference filter designed here can calculate the optimal reflectivity or transmission rate. Evaluation function used in our genetic algorithm is different from the others before. Using characteristic matrix to calculate the photonic band gap of one-dimensional photonic crystal is similar to electronic structure of doped. If the evaluation is sensitive to the deviation of photonic crystal structure, the approach by genetic algorithm is effective. A summary and explains towards some uncompleted issues are given at the end of this paper.
Facial Composite System Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zahradníková, Barbora; Duchovičová, Soňa; Schreiber, Peter
2014-12-01
The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.
Genetic Algorithms for Digital Quantum Simulations
NASA Astrophysics Data System (ADS)
Las Heras, U.; Alvarez-Rodriguez, U.; Solano, E.; Sanz, M.
2016-06-01
We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.
Calculating petal tools by using genetic algorithms.
González-García, Jorge; Cordero-Dávila, Alberto; Leal-Cabrera, Irce; Robledo-Sánchez, Carlos Ignacio; Santiago-Alvarado, Agustin
2006-08-20
To pass from a spherical surface to a conic one, it is possible to use a petal tool or a small solid tool that is placed at different time intervals at several radial zones of the glass. Genetic algorithms are applied to calculate the angular sizes of the incomplete annular tools that make up the petal tools. We also present the desired wear results carried out with the petal tool that was designed on the basis of the dwell times of complete annular tools. These dwell times are calculated by using base functions that are generated with annular tools and by applying the genetic algorithms.
Applying a Genetic Algorithm to Reconfigurable Hardware
NASA Technical Reports Server (NTRS)
Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim
2004-01-01
This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.
The Applications of Genetic Algorithms in Medicine.
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-11-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.]. PMID:26676060
The Applications of Genetic Algorithms in Medicine
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-01-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060
Cognitive radio resource allocation based on coupled chaotic genetic algorithm
NASA Astrophysics Data System (ADS)
Zu, Yun-Xiao; Zhou, Jie; Zeng, Chang-Chang
2010-11-01
A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.
Genetic programming techniques for thin-wire antennas
NASA Astrophysics Data System (ADS)
O'Donnell, Terry H.
2007-04-01
Simple genetic algorithm optimizations often utilize fixed-length chromosomes containing a predefined set of parameters to be optimized. While such algorithms have successfully created electrically small narrow-band and large wide-band military antennas, they require the antenna designer to have a fairly concrete antenna representation prior to exercising the genetic algorithm. In this research we investigate the use of genetic programming (GP) techniques to "program" the design of simple thin-wire antennas. Genetic programming techniques offer the potential to create random, multi-arm, multi-dimension antennas from variable length, tree-like chromosomes. We present a new genetic programming paradigm for creating multi-branched, thin-wire, genetic antennas and describe how GP commands are represented and decoded into physical antenna structures. We present preliminary results obtained from this algorithm showing evolutions along Pareto fronts representing antenna electrical size, VSWR, and antenna quality factor (Q).
Dynamic causal modeling with genetic algorithms.
Pyka, M; Heider, D; Hauke, S; Kircher, T; Jansen, A
2011-01-15
In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques.
Convergence properties of simple genetic algorithms
NASA Technical Reports Server (NTRS)
Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.
1974-01-01
The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.
MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
Genetic Algorithms for Multiple-Choice Problems
NASA Astrophysics Data System (ADS)
Aickelin, Uwe
2010-04-01
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.
Genetic algorithms for the vehicle routing problem
NASA Astrophysics Data System (ADS)
Volna, Eva
2016-06-01
The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.
Routine Discovery of Complex Genetic Models using Genetic Algorithms
Moore, Jason H.; Hahn, Lance W.; Ritchie, Marylyn D.; Thornton, Tricia A.; White, Bill C.
2010-01-01
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes. PMID:20948983
Routine Discovery of Complex Genetic Models using Genetic Algorithms.
Moore, Jason H; Hahn, Lance W; Ritchie, Marylyn D; Thornton, Tricia A; White, Bill C
2004-02-01
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes.
Enhanced algorithms for stochastic programming
Krishna, A.S.
1993-09-01
In this dissertation, we present some of the recent advances made in solving two-stage stochastic linear programming problems of large size and complexity. Decomposition and sampling are two fundamental components of techniques to solve stochastic optimization problems. We describe improvements to the current techniques in both these areas. We studied different ways of using importance sampling techniques in the context of Stochastic programming, by varying the choice of approximation functions used in this method. We have concluded that approximating the recourse function by a computationally inexpensive piecewise-linear function is highly efficient. This reduced the problem from finding the mean of a computationally expensive functions to finding that of a computationally inexpensive function. Then we implemented various variance reduction techniques to estimate the mean of a piecewise-linear function. This method achieved similar variance reductions in orders of magnitude less time than, when we directly applied variance-reduction techniques directly on the given problem. In solving a stochastic linear program, the expected value problem is usually solved before a stochastic solution and also to speed-up the algorithm by making use of the information obtained from the solution of the expected value problem. We have devised a new decomposition scheme to improve the convergence of this algorithm.
Genetic algorithms for genetic neural nets. Research report
Sharp, D.H.; Reinitz, J.; Mjolsness, E.
1991-01-01
In contrast to most synthetic neural nets, biological neural networks have a strong component of genetic determination which acts before and during experiential learning. Three broad levels of phenomena are present: long-term evolution, involving crossover as well as point mutation; a developmental process mapping genetic information to a set of cells and their internal states of gene expression (genotype to phenotype); and the subsequent synaptogenesis. We describe a very simple mathematical idealization of these three levels which combines the crossover search method of genetic algorithms with the developmental models used in our previous work on 'genetic' or 'recursively generated' artificial neural nets and elaborated into a connectionist model of biological development. Despite incorporating all three levels (evolution on genes; development of cells; synapse formation) the model may actually be far cheaper to compute with than a comparable search directly in synaptic weight space.
Application of Genetic Algorithms in Seismic Tomography
NASA Astrophysics Data System (ADS)
Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos
2010-05-01
In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the
Genetic algorithms for minimal source reconstructions
Lewis, P.S.; Mosher, J.C.
1993-12-01
Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.
PSS Parameters Tuning Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Abdulrahim, M.; Almoula, Zakaria Fadl; Al-Hafid, Hafid
2008-10-01
Optimal tuning of power system stabilizer (PSS) parameters using genetic algorithm with single objective function is presented in this paper. A Single Machine Infinite Bus (SMIB) system is considered. The main objective of this research paper is to investigate the suitability of genetic algorithm for effective tuning of parameters of the power system stabilizer in a single machine infinite bus system. A conventional speed based lead-lag PSS is used. A simple and effective method of tuning the parameters of PSS is proposed which is posed as an optimization formulation by maximizing the damping of modes of oscillations of the SMIB system over a wide range of loading conditions and different system configurations. It is found that GA based PSS with single objective design shows improved dynamic performance over Conventional PSS over a wide range of operating conditions and different system parameters.
Predicting mining activity with parallel genetic algorithms
Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,
2005-01-01
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.
Genetic algorithms in adaptive fuzzy control
NASA Technical Reports Server (NTRS)
Karr, C. Lucas; Harper, Tony R.
1992-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.
Genetic Algorithms for Digital Quantum Simulations.
Las Heras, U; Alvarez-Rodriguez, U; Solano, E; Sanz, M
2016-06-10
We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors. PMID:27341220
Fashion sketch design by interactive genetic algorithms
NASA Astrophysics Data System (ADS)
Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.
2012-11-01
Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.
Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?
NASA Astrophysics Data System (ADS)
Jones, Erika
2015-04-01
Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.
A hybrid genetic algorithm for resolving closely spaced objects
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Lillo, W. E.; Schulenburg, N.
1995-01-01
A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.
Using a genetic algorithm to solve fluid-flow problems
Pryor, R.J. )
1990-06-01
Genetic algorithms are based on the mechanics of the natural selection and natural genetics processes. These algorithms are finding increasing application to a wide variety of engineering optimization and machine learning problems. In this paper, the authors demonstrate the use of a genetic algorithm to solve fluid flow problems. Specifically, the authors use the algorithm to solve the one-dimensional flow equations for a pipe.
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
Comparison of genetic algorithms with conjugate gradient methods
NASA Technical Reports Server (NTRS)
Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.
1972-01-01
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
Parallel Genetic Algorithm for Alpha Spectra Fitting
NASA Astrophysics Data System (ADS)
García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio
2005-01-01
We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.
Genetic algorithms for modelling and optimisation
NASA Astrophysics Data System (ADS)
McCall, John
2005-12-01
Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by natural evolution. They have been successfully applied to a wide range of real-world problems of significant complexity. This paper is intended as an introduction to GAs aimed at immunologists and mathematicians interested in immunology. We describe how to construct a GA and the main strands of GA theory before speculatively identifying possible applications of GAs to the study of immunology. An illustrative example of using a GA for a medical optimal control problem is provided. The paper also includes a brief account of the related area of artificial immune systems.
Dominant takeover regimes for genetic algorithms
NASA Technical Reports Server (NTRS)
Noever, David; Baskaran, Subbiah
1995-01-01
The genetic algorithm (GA) is a machine-based optimization routine which connects evolutionary learning to natural genetic laws. The present work addresses the problem of obtaining the dominant takeover regimes in the GA dynamics. Estimated GA run times are computed for slow and fast convergence in the limits of high and low fitness ratios. Using Euler's device for obtaining partial sums in closed forms, the result relaxes the previously held requirements for long time limits. Analytical solution reveal that appropriately accelerated regimes can mark the ascendancy of the most fit solution. In virtually all cases, the weak (logarithmic) dependence of convergence time on problem size demonstrates the potential for the GA to solve large N-P complete problems.
Genetic Network Programming with Reconstructed Individuals
NASA Astrophysics Data System (ADS)
Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro
A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.
Cognitive Radio — Genetic Algorithm Approach
NASA Astrophysics Data System (ADS)
Reddy, Y. B.
2005-03-01
Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.
Instrument design and optimization using genetic algorithms
Hoelzel, Robert; Bentley, Phillip M.; Fouquet, Peter
2006-10-15
This article describes the design of highly complex physical instruments by using a canonical genetic algorithm (GA). The procedure can be applied to all instrument designs where performance goals can be quantified. It is particularly suited to the optimization of instrument design where local optima in the performance figure of merit are prevalent. Here, a GA is used to evolve the design of the neutron spin-echo spectrometer WASP which is presently being constructed at the Institut Laue-Langevin, Grenoble, France. A comparison is made between this artificial intelligence approach and the traditional manual design methods. We demonstrate that the search of parameter space is more efficient when applying the genetic algorithm, and the GA produces a significantly better instrument design. Furthermore, it is found that the GA increases flexibility, by facilitating the reoptimization of the design after changes in boundary conditions during the design phase. The GA also allows the exploration of 'nonstandard' magnet coil geometries. We conclude that this technique constitutes a powerful complementary tool for the design and optimization of complex scientific apparatus, without replacing the careful thought processes employed in traditional design methods.
Optimisation of nonlinear motion cueing algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Asadi, Houshyar; Mohamed, Shady; Rahim Zadeh, Delpak; Nahavandi, Saeid
2015-04-01
Motion cueing algorithms (MCAs) are playing a significant role in driving simulators, aiming to deliver the most accurate human sensation to the simulator drivers compared with a real vehicle driver, without exceeding the physical limitations of the simulator. This paper provides the optimisation design of an MCA for a vehicle simulator, in order to find the most suitable washout algorithm parameters, while respecting all motion platform physical limitations, and minimising human perception error between real and simulator driver. One of the main limitations of the classical washout filters is that it is attuned by the worst-case scenario tuning method. This is based on trial and error, and is effected by driving and programmers experience, making this the most significant obstacle to full motion platform utilisation. This leads to inflexibility of the structure, production of false cues and makes the resulting simulator fail to suit all circumstances. In addition, the classical method does not take minimisation of human perception error and physical constraints into account. Production of motion cues and the impact of different parameters of classical washout filters on motion cues remain inaccessible for designers for this reason. The aim of this paper is to provide an optimisation method for tuning the MCA parameters, based on nonlinear filtering and genetic algorithms. This is done by taking vestibular sensation error into account between real and simulated cases, as well as main dynamic limitations, tilt coordination and correlation coefficient. Three additional compensatory linear blocks are integrated into the MCA, to be tuned in order to modify the performance of the filters successfully. The proposed optimised MCA is implemented in MATLAB/Simulink software packages. The results generated using the proposed method show increased performance in terms of human sensation, reference shape tracking and exploiting the platform more efficiently without reaching
A genetic algorithm to reduce stream channel cross section data
Berenbrock, C.
2006-01-01
A genetic algorithm (GA) was used to reduce cross section data for a hypothetical example consisting of 41 data points and for 10 cross sections on the Kootenai River. The number of data points for the Kootenai River cross sections ranged from about 500 to more than 2,500. The GA was applied to reduce the number of data points to a manageable dataset because most models and other software require fewer than 100 data points for management, manipulation, and analysis. Results indicated that the program successfully reduced the data. Fitness values from the genetic algorithm were lower (better) than those in a previous study that used standard procedures of reducing the cross section data. On average, fitnesses were 29 percent lower, and several were about 50 percent lower. Results also showed that cross sections produced by the genetic algorithm were representative of the original section and that near-optimal results could be obtained in a single run, even for large problems. Other data also can be reduced in a method similar to that for cross section data.
Multidisciplinary design optimization using genetic algorithms
NASA Technical Reports Server (NTRS)
Unal, Resit
1994-01-01
Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared
Genetic parts to program bacteria.
Voigt, Christopher A
2006-10-01
Genetic engineering is entering a new era, where microorganisms can be programmed using synthetic constructs of DNA encoding logic and operational commands. A toolbox of modular genetic parts is being developed, comprised of cell-based environmental sensors and genetic circuits. Systems have already been designed to be interconnected with each other and interfaced with the control of cellular processes. Engineering theory will provide a predictive framework to design operational multicomponent systems. On the basis of these developments, increasingly complex cellular machines are being constructed to build specialty chemicals, weave biomaterials, and to deliver therapeutics.
MM Algorithms for Geometric and Signomial Programming.
Lange, Kenneth; Zhou, Hua
2014-02-01
This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.
Genetic algorithm and particle swarm optimization combined with Powell method
NASA Astrophysics Data System (ADS)
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
PDE Nozzle Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Billings, Dana; Turner, James E. (Technical Monitor)
2000-01-01
Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.
Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling
NASA Technical Reports Server (NTRS)
Lohn, Jason; Colombano, Silvano
1997-01-01
We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.
Inversion for seismic anisotropy using genetic algorithms
Horne, S. Univ. of Edinburgh . Dept. of Geology and Geophysics); MacBeth, C. . Dept. of Geology and Geophysics)
1994-11-01
A general inversion scheme based on a genetic algorithm is developed to invert seismic observations for anisotropic parameters. The technique is applied to the inversion of shear-wave observations from two azimuthal VSP data sets from the Conoco test site in Oklahoma. Horizontal polarizations and time-delays are inverted for hexagonal and orthorhombic symmetries. The model solutions are consistent with previous studies using trial and error matching of full waveform synthetics. The shear-wave splitting observations suggest the presence of a shear-wave line singularity and are consistent with a dipping fracture system which is known to exist at the test site. Application of the inversion scheme prior to full waveform modeling demonstrates that a considerable saving in time is possible while retaining the same degree of accuracy.
Spacecraft Attitude Maneuver Planning Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Kornfeld, Richard P.
2004-01-01
A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.
The potential of genetic algorithms for conceptual design of rotor systems
NASA Technical Reports Server (NTRS)
Crossley, William A.; Wells, Valana L.; Laananen, David H.
1993-01-01
The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.
A Parallel Genetic Algorithm for Automated Electronic Circuit Design
NASA Technical Reports Server (NTRS)
Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris
2000-01-01
issues in the GA, it is possible to have idle processors. However, as long as the load at each processing node is similar, the processors are kept busy nearly all of the time. In applying GAs to circuit design, a suitable genetic representation 'is that of a circuit-construction program. We discuss one such circuit-construction programming language and show how evolution can generate useful analog circuit designs. This language has the desirable property that virtually all sets of combinations of primitives result in valid circuit graphs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. For example, a figure shows an 85 dB amplifier design evolved by our system, and another figure shows the performance of that circuit (gain and frequency response). In all tasks, our system is able to generate circuits that achieve the target specifications.
Specific optimization of genetic algorithm on special algebras
NASA Astrophysics Data System (ADS)
Habiballa, Hashim; Novak, Vilem; Dyba, Martin; Schenk, Jiri
2016-06-01
Searching for complex finite algebras can be succesfully done by the means of genetic algorithm as we showed in former works. This genetic algorithm needs specific optimization of crossover and mutation. We present details about these optimizations which are already implemented in software application for this task - EQCreator.
A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates
ERIC Educational Resources Information Center
Venables, Anne; Tan, Grace
2007-01-01
Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…
A Test of Genetic Algorithms in Relevance Feedback.
ERIC Educational Resources Information Center
Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de
2002-01-01
Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…
Improving Search Properties in Genetic Programming
NASA Technical Reports Server (NTRS)
Janikow, Cezary Z.; DeWeese, Scott
1997-01-01
With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also
Advanced optimization of permanent magnet wigglers using a genetic algorithm
Hajima, Ryoichi
1995-12-31
In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.
Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
Genetic algorithm-based form error evaluation
NASA Astrophysics Data System (ADS)
Cui, Changcai; Li, Bing; Huang, Fugui; Zhang, Rencheng
2007-07-01
Form error evaluation of geometrical products is a nonlinear optimization problem, for which a solution has been attempted by different methods with some complexity. A genetic algorithm (GA) was developed to deal with the problem, which was proved simple to understand and realize, and its key techniques have been investigated in detail. Firstly, the fitness function of GA was discussed emphatically as a bridge between GA and the concrete problems to be solved. Secondly, the real numbers-based representation of the desired solutions in the continual space optimization problem was discussed. Thirdly, many improved evolutionary strategies of GA were described on emphasis. These evolutionary strategies were the selection operation of 'odd number selection plus roulette wheel selection', the crossover operation of 'arithmetic crossover between near relatives and far relatives' and the mutation operation of 'adaptive Gaussian' mutation. After evolutions from generation to generation with the evolutionary strategies, the initial population produced stochastically around the least-squared solutions of the problem would be updated and improved iteratively till the best chromosome or individual of GA appeared. Finally, some examples were given to verify the evolutionary method. Experimental results show that the GA-based method can find desired solutions that are superior to the least-squared solutions except for a few examples in which the GA-based method can obtain similar results to those by the least-squared method. Compared with other optimization techniques, the GA-based method can obtain almost equal results but with less complicated models and computation time.
OPC recipe optimization using genetic algorithm
NASA Astrophysics Data System (ADS)
Asthana, Abhishek; Wilkinson, Bill; Power, Dave
2016-03-01
Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.
Lunar Habitat Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.
2007-01-01
Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.
Closed Loop System Identification with Genetic Algorithms
NASA Technical Reports Server (NTRS)
Whorton, Mark S.
2004-01-01
High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.
Geophysical Inversion through Hierarchical Genetic Algorithm Scheme
NASA Astrophysics Data System (ADS)
Furman, Alex; Huisman, Johan A.
2010-05-01
Geophysical investigation is a powerful tool that allows non-invasive and non-destructive mapping of subsurface states and properties. However, non-uniqueness associated with the inversion process halts these methods from becoming of more quantitative use. One major direction researchers are going is constraining the inverse problem by hydrological observations and models. An alternative to the commonly used direct inversion methods are global optimization schemes (such as genetic algorithms and Monte Carlo Markov Chain methods). However, the major limitation here is the desired high resolution of the tomographic image, which leads to a large number of parameters and an unreasonably high computational effort when using global optimization schemes. One way to overcome these problems is to combine the advantages of both direct and global inversion methods through hierarchical inversion. That is, starting the inversion with relatively coarse resolution of parameters, achieving good inversion using one of the two inversion schemes (global or direct), and then refining the resolution and applying a combination of global and direct inversion schemes for the whole domain or locally. In this work we explore through synthetic case studies the option of using a global optimization scheme for inversion of electrical resistivity tomography data through hierarchical refinement of the model resolution.
Camera calibration using a genetic algorithm
NASA Astrophysics Data System (ADS)
Hui, Nirmal Baran; Pratihar, Dilip Kumar
2008-12-01
An autonomous robot will have to detect moving obstacles online before it can plan its collision-free path, while navigating in a dynamic environment. The robot collects information about the environment with the help of a camera and determines the inputs for its motion planner through image analysis. The present article deals with issues related to camera calibration and online image processing. The problem of camera calibration is treated as an optimization problem and solved using a genetic algorithm so as to achieve minimum distorted image plane error. The calibrated vision system is then utilized for the detection and identification of the objects by analysing the images collected at regular intervals. For image processing, five different operations, such as median filtering, thresholding, perimeter estimation, labelling and size filtering, have been carried out. To show the effectiveness of the developed camera-based vision system, inputs of the motion planner of a navigating robot are calculated for two different cases. It is observed that online detection of the shapes and configurations of the obstacles is possible by using the vision system developed.
Genetic Network Programming with Intron-Like Nodes
NASA Astrophysics Data System (ADS)
Mabu, Shingo; Chen, Yan; Eto, Shinji; Shimada, Kaoru; Hirasawa, Kotaro
Recently, Genetic Network Programming (GNP) has been proposed, which is an extension of Genetic Algorithm(GA) and Genetic Programming(GP). GNP can make compact programs and can memorize the past history in it implicitly, because it expresses the solution by directed graphs and therefore, it can reuse the nodes. In this research, intron-like nodes are introduced for improving the performance of GNP. The aim of introducing intron-like nodes is to use every node as much as possible. It is found from simulations that the intron-like nodes are useful for improving the training speed and generalization ability.
Successful technical trading agents using genetic programming.
Othling, Andrew S.; Kelly, John A.; Pryor, Richard J.; Farnsworth, Grant V.
2004-10-01
Genetic programming (GP) has proved to be a highly versatile and useful tool for identifying relationships in data for which a more precise theoretical construct is unavailable. In this project, we use a GP search to develop trading strategies for agent based economic models. These strategies use stock prices and technical indicators, such as the moving average convergence/divergence and various exponentially weighted moving averages, to generate buy and sell signals. We analyze the effect of complexity constraints on the strategies as well as the relative performance of various indicators. We also present innovations in the classical genetic programming algorithm that appear to improve convergence for this problem. Technical strategies developed by our GP algorithm can be used to control the behavior of agents in economic simulation packages, such as ASPEN-D, adding variety to the current market fundamentals approach. The exploitation of arbitrage opportunities by technical analysts may help increase the efficiency of the simulated stock market, as it does in the real world. By improving the behavior of simulated stock markets, we can better estimate the effects of shocks to the economy due to terrorism or natural disasters.
Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
Template learning of cellular neural network using genetic programming.
Radwan, Elsayed; Tazaki, Eiichiro
2004-08-01
A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.
Koza, J R
1994-01-01
The recently-developed genetic programming paradigm is used to evolve a computer program to classify a given protein segment as being a transmembrane domain or non-transmembrane area of the protein. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic ingredients and then genetically breeds the population of programs using the Darwinian principle of survival of the fittest and an analog of the naturally occurring genetic operation of crossover (sexual recombination). Automatic function definition enables genetic programming to dynamically create subroutines dynamically during the run. Genetic programming is given a training set of differently-sized protein segments and their correct classification (but no biochemical knowledge, such as hydrophobicity values). Correlation is used as the fitness measure to drive the evolutionary process. The best genetically-evolved program achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that reported for four other algorithms reported at the First International Conference on Intelligent Systems for Molecular Biology. Our genetically evolved program is an instance of an algorithm discovered by an automated learning paradigm that is superior to that written by human investigators.
NASA Astrophysics Data System (ADS)
López-Medina, Mario E.; Vázquez-Montiel, Sergio; Herrera-Vázquez, Joel
2008-04-01
The Genetic Algorithms, GAs, are a method of global optimization that we use in the stage of optimization in the design of optical systems. In the case of optical design and optimization, the efficiency and convergence speed of GAs are related with merit function, crossover operator, and mutation operator. In this study we present a comparison between several genetic algorithms implementations using different optical systems, like achromatic cemented doublet, air spaced doublet and telescopes. We do the comparison varying the type of design parameters and the number of parameters to be optimized. We also implement the GAs using discreet parameters with binary chains and with continuous parameter using real numbers in the chromosome; analyzing the differences in the time taken to find the solution and the precision in the results between discreet and continuous parameters. Additionally, we use different merit function to optimize the same optical system. We present the obtained results in tables, graphics and a detailed example; and of the comparison we conclude which is the best way to implement GAs for design and optimization optical system. The programs developed for this work were made using the C programming language and OSLO for the simulation of the optical systems.
Genetic Algorithm Based Neural Networks for Nonlinear Optimization
1994-09-28
This software develops a novel approach to nonlinear optimization using genetic algorithm based neural networks. To our best knowledge, this approach represents the first attempt at applying both neural network and genetic algorithm techniques to solve a nonlinear optimization problem. The approach constructs a neural network structure and an appropriately shaped energy surface whose minima correspond to optimal solutions of the problem. A genetic algorithm is employed to perform a parallel and powerful search ofmore » the energy surface.« less
Restart-Based Genetic Algorithm for the Quadratic Assignment Problem
NASA Astrophysics Data System (ADS)
Misevicius, Alfonsas
The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.
Autonomous photogrammetric network design based on changing environment genetic algorithms
NASA Astrophysics Data System (ADS)
Yang, Jian; Lu, Nai-Guang; Dong, Mingli
2008-10-01
In order to get good accuracy, designer used to consider how to place cameras. Usually, cameras placement design is a multidimensional optimal problem, so people used genetic algorithms to solve it. But genetic algorithms could result in premature or convergent problem. Sometime we get local minimum and observe vibrating phenomenon. Those will get inaccurate design. So we try to solve the problem using the changing environment genetic algorithms. The work proposes giving those species groups difference environment during difference stage to improve the property. Computer simulation result shows the acceleration in convergent speed and ability of selecting good individual. This work would be used in other application.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
NASA Astrophysics Data System (ADS)
Matsui, Shouichi; Watanabe, Isamu; Tokoro, Ken-Ichi
A new genetic algorithm is proposed for solving job-shop scheduling problems where the total number of search points is limited. The objective of the problem is to minimize the makespan. The solution is represented by an operation sequence, i.e., a permutation of operations. The proposed algorithm is based on the framework of the parameter-free genetic algorithm. It encodes a permutation using random keys into a chromosome. A schedule is derived from a permutation using a hybrid scheduling (HS), and the parameter of HS is also encoded in a chromosome. Experiments using benchmark problems show that the proposed algorithm outperforms the previously proposed algorithms, genetic algorithm by Shi et al. and the improved local search by Nakano et al., for large-scale problems under the constraint of limited number of search points.
Mobile transporter path planning using a genetic algorithm approach
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1988-01-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Optimization of computer-generated binary holograms using genetic algorithms
NASA Astrophysics Data System (ADS)
Cojoc, Dan; Alexandrescu, Adrian
1999-11-01
The aim of this paper is to compare genetic algorithms against direct point oriented coding in the design of binary phase Fourier holograms, computer generated. These are used as fan-out elements for free space optical interconnection. Genetic algorithms are optimization methods which model the natural process of genetic evolution. The configuration of the hologram is encoded to form a chromosome. To start the optimization, a population of different chromosomes randomly generated is considered. The chromosomes compete, mate and mutate until the best chromosome is obtained according to a cost function. After explaining the operators that are used by genetic algorithms, this paper presents two examples with 32 X 32 genes in a chromosome. The crossover type and the number of mutations are shown to be important factors which influence the convergence of the algorithm. GA is demonstrated to be a useful tool to design namely binary phase holograms of complicate structures.
Rocket stage optimization using a simple genetic algorithm
NASA Astrophysics Data System (ADS)
Trulove, Angella M.; Whitaker, Kevin W.
1993-06-01
Optimizing the number of rocket stages for a launch vehicle has typically focused on solving the governing equations with Lagrange multipliers. Recently, the development of artificial intelligence techniques has led to the use of simple genetic algorithms to solve many engineering optimization problems in a much more robust manner. The simple genetic algorithm is used in this investigation to determine the optimal number of rocket stages for a number of constraints: maximum payload mass, maximum payload velocity, and minimum cost. Excellent agreement is obtained when comparing genetic algorithm predictions with a traditional Lagrange multiplier optimization approach. The simple genetic algorithm is able to solve this multiparameter optimization problem without detailed knowledge of the search space and by always avoiding false optima.
Improved genetic algorithm for fast path planning of USV
NASA Astrophysics Data System (ADS)
Cao, Lu
2015-12-01
Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for USV(Unmanned Surface Vehicle), an approach of fast path planning based on voronoi diagram and improved Genetic Algorithm is proposed, which makes use of the principle of hierarchical path planning. First the voronoi diagram is utilized to generate the initial paths and then the optimal path is searched by using the improved Genetic Algorithm, which use multiprocessors parallel computing techniques to improve the traditional genetic algorithm. Simulation results verify that the optimal time is greatly reduced and path planning based on voronoi diagram and the improved Genetic Algorithm is more favorable in the real-time operation.
Mobile Transporter Path Planning Using A Genetic Algorithm Approach
NASA Astrophysics Data System (ADS)
Baffes, Paul; Wang, Lui
1988-10-01
The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
Dynamical genetic programming in XCSF.
Preen, Richard J; Bull, Larry
2013-01-01
A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to artificial neural networks. This paper presents results from an investigation into using a temporally dynamic symbolic representation within the XCSF learning classifier system. In particular, dynamical arithmetic networks are used to represent the traditional condition-action production system rules to solve continuous-valued reinforcement learning problems and to perform symbolic regression, finding competitive performance with traditional genetic programming on a number of composite polynomial tasks. In addition, the network outputs are later repeatedly sampled at varying temporal intervals to perform multistep-ahead predictions of a financial time series.
Superscattering of light optimized by a genetic algorithm
Mirzaei, Ali Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.
2014-07-07
We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.
Internal quantum efficiency analysis of solar cell by genetic algorithm
Xiong, Kanglin; Yang, Hui; Lu, Shulong; Zhou, Taofei; Wang, Rongxin; Qiu, Kai; Dong, Jianrong; Jiang, Desheng
2010-11-15
To investigate factors limiting the performance of a GaAs solar cell, genetic algorithm is employed to fit the experimentally measured internal quantum efficiency (IQE) in the full spectra range. The device parameters such as diffusion lengths and surface recombination velocities are extracted. Electron beam induced current (EBIC) is performed in the base region of the cell with obtained diffusion length agreeing with the fit result. The advantage of genetic algorithm is illustrated. (author)
Grooming of arbitrary traffic using improved genetic algorithms
NASA Astrophysics Data System (ADS)
Jiao, Yueguang; Xu, Zhengchun; Zhang, Hanyi
2004-04-01
A genetic algorithm is proposed with permutation based chromosome presentation and roulette wheel selection to solve traffic grooming problems in WDM ring network. The parameters of the algorithm are evaluated by calculating of large amount of traffic patterns at different conditions. Four methods were developed to improve the algorithm, which can be used combining with each other. Effects of them on the algorithm are studied via computer simulations. The results show that they can all make the algorithm more powerful to reduce the number of add-drop multiplexers or wavelengths required in a network.
Quantum Algorithm for Linear Programming Problems
NASA Astrophysics Data System (ADS)
Joag, Pramod; Mehendale, Dhananjay
The quantum algorithm (PRL 103, 150502, 2009) solves a system of linear equations with exponential speedup over existing classical algorithms. We show that the above algorithm can be readily adopted in the iterative algorithms for solving linear programming (LP) problems. The first iterative algorithm that we suggest for LP problem follows from duality theory. It consists of finding nonnegative solution of the equation forduality condition; forconstraints imposed by the given primal problem and for constraints imposed by its corresponding dual problem. This problem is called the problem of nonnegative least squares, or simply the NNLS problem. We use a well known method for solving the problem of NNLS due to Lawson and Hanson. This algorithm essentially consists of solving in each iterative step a new system of linear equations . The other iterative algorithms that can be used are those based on interior point methods. The same technique can be adopted for solving network flow problems as these problems can be readily formulated as LP problems. The suggested quantum algorithm cansolveLP problems and Network Flow problems of very large size involving millions of variables.
Genetic optimization of the HSTAMIDS landmine detection algorithm
NASA Astrophysics Data System (ADS)
Konduri, Ravi K.; Solomon, Geoff Z.; DeJong, Keith; Duvoisin, Herbert A.; Bartosz, Elizabeth E.
2004-09-01
CyTerra's dual sensor HSTAMIDS system has demonstrated exceptional landmine detection capabilities in extensive government-run field tests. Further optimization of the highly successful PentAD-class algorithms for Humanitarian Demining (HD) use (to enhance detection (Pd) and to lower the false alarm rate (FAR)) may be possible. PentAD contains several input parameters, making such optimization computationally intensive. Genetic algorithm techniques, which formerly provided substantial improvement in the detection performance of the metal detector sensor algorithm alone, have been applied to optimize the numerical values of the dual-sensor algorithm parameters. Genetic algorithm techniques have also been applied to choose among several sub-models and fusion techniques to potentially train the HSTAMIDS HD system in new ways. In this presentation we discuss the performance of the resulting algorithm as applied to field data.
Calculation of substructural analysis weights using a genetic algorithm.
Holliday, John D; Sani, Nor; Willett, Peter
2015-02-23
This work describes a genetic algorithm for the calculation of substructural analysis for use in ligand-based virtual screening. The algorithm is simple in concept and effective in operation, with simulated virtual screening experiments using the MDDR and WOMBAT data sets showing it to be superior to substructural analysis weights based on a naive Bayesian classifier.
Optimization of genomic selection training populations with a genetic algorithm
Technology Transfer Automated Retrieval System (TEKTRAN)
In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...
Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.
ERIC Educational Resources Information Center
Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand
2003-01-01
Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…
Immune allied genetic algorithm for Bayesian network structure learning
NASA Astrophysics Data System (ADS)
Song, Qin; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.
Genetic algorithm and the application for job shop group scheduling
NASA Astrophysics Data System (ADS)
Mao, Jianzhong; Wu, Zhiming
1995-08-01
Genetic algorithm (GA) is a heuristic and random search technique mimicking nature. This paper first presents the basic principle of GA, the definition and the function of the genetic operators, and the principal character of GA. On the basis of these, the paper proposes using GA as a new solution method of the job-shop group scheduling problem, discusses the coded representation method of the feasible solution, and the particular limitation to the genetic operators.
The Genetic Programming of Industrial Microorganisms.
ERIC Educational Resources Information Center
Hopwood, David A.
1981-01-01
Traces the development of the field of industrial microbial genetics, describing a range of techniques for genetic programing. Includes a discussion of site-directed mutagenesis, protoplast fusion, and recombinant DNA manipulations. (CS)
Optimization of a genetic algorithm for searching molecular conformer space
NASA Astrophysics Data System (ADS)
Brain, Zoe E.; Addicoat, Matthew A.
2011-11-01
We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA - a meta-genetic algorithm - was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.
On Using Surrogates with Genetic Programming.
Hildebrandt, Torsten; Branke, Jürgen
2015-01-01
One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP.
On Using Surrogates with Genetic Programming.
Hildebrandt, Torsten; Branke, Jürgen
2015-01-01
One way to accelerate evolutionary algorithms with expensive fitness evaluations is to combine them with surrogate models. Surrogate models are efficiently computable approximations of the fitness function, derived by means of statistical or machine learning techniques from samples of fully evaluated solutions. But these models usually require a numerical representation, and therefore cannot be used with the tree representation of genetic programming (GP). In this paper, we present a new way to use surrogate models with GP. Rather than using the genotype directly as input to the surrogate model, we propose using a phenotypic characterization. This phenotypic characterization can be computed efficiently and allows us to define approximate measures of equivalence and similarity. Using a stochastic, dynamic job shop scenario as an example of simulation-based GP with an expensive fitness evaluation, we show how these ideas can be used to construct surrogate models and improve the convergence speed and solution quality of GP. PMID:24967694
Atmospheric Downscaling using Genetic Programming
NASA Astrophysics Data System (ADS)
Zerenner, Tanja; Venema, Victor; Simmer, Clemens
2013-04-01
Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and downscaling procedures. Subject of our work is the downscaling scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current downscaling scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for downscaling surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require downscaling, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for downscaling surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and
An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Weir, John M.; Wells, B. Earl
2003-01-01
Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.
A genetic-based algorithm for personalized resistance training
Kiely, J; Suraci, B; Collins, DJ; de Lorenzo, D; Pickering, C; Grimaldi, KA
2016-01-01
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete's genetic profile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete's potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n = 28); study 2: soccer players (n = 39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic fitness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) significantly increased results in CMJ (P = 0.0005) and Aero3 (P = 0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or low-intensity trained with power genotype) demonstrated non-significant improvements in CMJ (P = 0.175) and less prominent results in Aero3 (P = 0.0134). In study 2, soccer players from the matched group also demonstrated significantly greater (P < 0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P < 0.0001). Our results indicate that matching the individual's genotype with the appropriate training modality leads to more effective
A Method of Solving Scheduling Problems Using Improved Guided Genetic Algorithm
NASA Astrophysics Data System (ADS)
Ou, Gyouhi; Tamura, Hiroki; Tanno, Koichi; Tang, Zheng
In this paper, an improved guided genetic algorithm is proposed forJob-shop schueduling problem. The proposed method is improved by genetic algorithm using multipliers which can be adjusted during the search process. The simulation results based on some benchmark problems that proves the proposed method can find better solutions than genetic algorithm and original guided genetic algorithm.
NASA Astrophysics Data System (ADS)
Vasant, P.; Ganesan, T.; Elamvazuthi, I.
2012-11-01
A fairly reasonable result was obtained for non-linear engineering problems using the optimization techniques such as neural network, genetic algorithms, and fuzzy logic independently in the past. Increasingly, hybrid techniques are being used to solve the non-linear problems to obtain better output. This paper discusses the use of neuro-genetic hybrid technique to optimize the geological structure mapping which is known as seismic survey. It involves the minimization of objective function subject to the requirement of geophysical and operational constraints. In this work, the optimization was initially performed using genetic programming, and followed by hybrid neuro-genetic programming approaches. Comparative studies and analysis were then carried out on the optimized results. The results indicate that the hybrid neuro-genetic hybrid technique produced better results compared to the stand-alone genetic programming method.
Cloud identification using genetic algorithms and massively parallel computation
NASA Technical Reports Server (NTRS)
Buckles, Bill P.; Petry, Frederick E.
1996-01-01
As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user
Programming Cells: Towardsan automated “Genetic Compiler”
Clancy, Kevin; Voigt, Christopher A.
2010-01-01
I. Summary The increasing scale and sophistication of genetic engineering will necessitate a new generation of computer-aided design (CAD). For large genetic programs, keeping track of the DNA on the level of nucleotides becomes tedious and error prone. To push the size of projects, it is important to abstract the designer from the process of part selection and optimization. The vision is to specify genetic programs in a higher-level language, which a genetic compiler could automatically convert into a DNA sequence. Steps towards this goal include: defining the semantics of the higher-level language, algorithms to select and assemble parts, and biophysical methods to link DNA sequence to function. These will be coupled to graphic design interfaces and simulation packages to aid in the prediction of program dynamics, optimize genes, and scan projects for errors. PMID:20702081
A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity.
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1994-05-01
In this dissertation the author reports on his efforts to develop a parallel genetic algorithm and apply it to the solution of set partitioning problem -- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. He developed a distributed steady-state genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. The authors found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulation found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high-quality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation they found was the difficulty solving problems with many constraints.
Polyglot Programming in Applications Used for Genetic Data Analysis
Nowak, Robert M.
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633
Polyglot programming in applications used for genetic data analysis.
Nowak, Robert M
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development. PMID:25197633
Polyglot programming in applications used for genetic data analysis.
Nowak, Robert M
2014-01-01
Applications used for the analysis of genetic data process large volumes of data with complex algorithms. High performance, flexibility, and a user interface with a web browser are required by these solutions, which can be achieved by using multiple programming languages. In this study, I developed a freely available framework for building software to analyze genetic data, which uses C++, Python, JavaScript, and several libraries. This system was used to build a number of genetic data processing applications and it reduced the time and costs of development.
NASA Technical Reports Server (NTRS)
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
Large-scale sequential quadratic programming algorithms
Eldersveld, S.K.
1992-09-01
The problem addressed is the general nonlinear programming problem: finding a local minimizer for a nonlinear function subject to a mixture of nonlinear equality and inequality constraints. The methods studied are in the class of sequential quadratic programming (SQP) algorithms, which have previously proved successful for problems of moderate size. Our goal is to devise an SQP algorithm that is applicable to large-scale optimization problems, using sparse data structures and storing less curvature information but maintaining the property of superlinear convergence. The main features are: 1. The use of a quasi-Newton approximation to the reduced Hessian of the Lagrangian function. Only an estimate of the reduced Hessian matrix is required by our algorithm. The impact of not having available the full Hessian approximation is studied and alternative estimates are constructed. 2. The use of a transformation matrix Q. This allows the QP gradient to be computed easily when only the reduced Hessian approximation is maintained. 3. The use of a reduced-gradient form of the basis for the null space of the working set. This choice of basis is more practical than an orthogonal null-space basis for large-scale problems. The continuity condition for this choice is proven. 4. The use of incomplete solutions of quadratic programming subproblems. Certain iterates generated by an active-set method for the QP subproblem are used in place of the QP minimizer to define the search direction for the nonlinear problem. An implementation of the new algorithm has been obtained by modifying the code MINOS. Results and comparisons with MINOS and NPSOL are given for the new algorithm on a set of 92 test problems.
A genetic algorithm approach in interface and surface structure optimization
Zhang, Jian
2010-01-01
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
Distributed genetic algorithms for the floorplan design problem
NASA Technical Reports Server (NTRS)
Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.
1991-01-01
Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.
Learning to control the program evolution process with cultural algorithms
Zannoni; Reynolds
1997-01-01
Traditional software engineering dictates the use of modular and structured programming and top-down stepwise refinement techniques that reduce the amount of variability arising in the development process by establishing standard procedures to be followed while writing software. This focusing leads to reduced variability in the resulting products, due to the use of standardized constructs. Genetic programming (GP) performs heuristic search in the space of programs. Programs produced through the GP paradigm emerge as the result of simulated evolution and are built through a bottom-up process, incrementally augmenting their functionality until a satisfactory level of performance is reached. Can we automatically extract knowledge from the GP programming process that can be useful to focus the search and reduce product variability, thus leading to a more effective use of the available resources? An answer to this question is investigated with the aid of cultural algorithms. A new system, cultural algorithms with genetic programming (CAGP), is presented. The system has two levels. The first is the pool of genetic programs (population level), and the second is a knowledge repository (belief set) that is built during the GP run and is used to guide the search process. The microevolution within the population brings about potentially meaningful characteristics of the programs for the achievement of the given task, such as properties exhibited by the best performers in the population. CAGP extracts these features and represents them as the set of the current beliefs. Beliefs correspond to constraints that all the genetic operators and programs must follow. Interaction between the two levels occurs in one direction through the extraction process and, in the other, through the modulation of an individual's program parameters according to which, and how many, of the constraints it follows. CAGP is applied to solve an instance of the symbolic regression problem, in which a
Filter Circuit Design by Parallel Genetic Programming
NASA Astrophysics Data System (ADS)
Yano, Yuichi; Kato, Toshiji; Inoue, Kaoru; Miki, Mitsunori
Genetic Programming (GP) is an extension of Genetic Algorithm(GA) to handle more structural problems. In this paper, an approach to filter circuit design by GP is proposed. By designing a gene which includes not only the parameters of consisting elements, but also the structural information of the circuit, it becomes possible to apply the proposed approach to various types of filter circuits. GP depends much on trial and error due to its probabilitic nature. To decrease this uncertainty and ensure the progress of the evolution, Parallel GP with multiple populations with the island model is also proposed. An MPI-based cluster system is used for realization of this parallel computing where each island correspondsd to each node. A lowpass and an asymmetric bandpass filters are designed. One hundred times of trials for multiple populations with and without migrations are tested in the design of lowpass filter to confirm the validity of the proposed method. In the asymmetric bandpass filter design, the results are compared with those of the circuit designed by hand to confirm the effectiveness of the proposed method. The proposed approach is applicable to various types of filter circuits. It can contribute to an automated design procedure, where it would require a expirenced designer if done by hand. It is also possible to obtain a new circuit design which would not be possible if done by hand.
Biased Random-Key Genetic Algorithms for the Winner Determination Problem in Combinatorial Auctions.
de Andrade, Carlos Eduardo; Toso, Rodrigo Franco; Resende, Mauricio G C; Miyazawa, Flávio Keidi
2015-01-01
In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.
Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
Acoustic design of rotor blades using a genetic algorithm
NASA Technical Reports Server (NTRS)
Wells, V. L.; Han, A. Y.; Crossley, W. A.
1995-01-01
A genetic algorithm coupled with a simplified acoustic analysis was used to generate low-noise rotor blade designs. The model includes thickness, steady loading and blade-vortex interaction noise estimates. The paper presents solutions for several variations in the fitness function, including thickness noise only, loading noise only, and combinations of the noise types. Preliminary results indicate that the analysis provides reasonable assessments of the noise produced, and that genetic algorithm successfully searches for 'good' designs. The results show that, for a given required thrust coefficient, proper blade design can noticeably reduce the noise produced at some expense to the power requirements.
Genetic algorithms and the search for viable string vacua
NASA Astrophysics Data System (ADS)
Abel, Steven; Rizos, John
2014-08-01
Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 1010 models, and yet a Genetic Algorithm can find them after constructing only 105 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.
Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
1992-01-01
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
Local path planning of a mobile robot using genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Rubo; Zhang, Guoyin; Gu, Guochang
1998-08-01
The local path planning of mobile robots can be regarded as finding a mapping from perception space to action space. Genetic algorithm is used to search optimal mapping in this paper so as to improve the obstacle avoidance ability of the robot. In this paper, the rotational angle and translation distance of the robot is divided into seven and four grades respectively. In addition, the length of the path that the robot covers before collision with obstacle is taken as fitness. The robot can learn to carry out local path planning through selection, crossover and mutation in genetic algorithm. The simulation results are given at the and of this paper.
Genetic algorithm approach to aircraft gate reassignment problem
Gu, Y.; Chung, C.A.
1999-10-01
The aircraft gate reassignment problem occurs when the departure of an incoming aircraft is delayed or a delay occurs in flight. If the delay is significant enough to delay the arrival of subsequent incoming aircraft at the assigned gate, the airline must revise the gate assignments to minimize extra delay times. This paper describes a genetic algorithm approach to solving the gate reassignment problem. By using a global search technique on quantified information, this genetic algorithm approach can efficiently find minimum extra delayed time solutions that are as effective or more effective than solutions generated by experienced gate managers.
A systematic study of genetic algorithms with genotype editing
Huang, C. F.; Rocha, L. M.
2004-01-01
This paper presents our systematic study on an RNA-editing computational model of Genetic Algorithms (GA). This model is constructed based on several genetic editing characteristics that are gleaned from the RNA editing system as observed in several organisms. We have expanded the traditional Genetic Algorithm with artificial editing mechanisms as proposed by [15]. The incorporation of editing mechanisms provides a means for artificial agents with genetic descriptions to gain greater phenotypic plasticity, which may be environmentally regulated. The systematic study of this RNA-editing model has shed some light into the evolutionary implications of RNA editing and how to select proper RNA editors for design of more robust GAS. The results will also show promising applications to complex real-world problems. We expect that the framework proposed will both facilitate determining the evolutionary role of RNA editing in biology, and advance the current state of research in Evolutionary Computation.
RNA-RNA interaction prediction using genetic algorithm
2014-01-01
Background RNA-RNA interaction plays an important role in the regulation of gene expression and cell development. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. In the RNA-RNA interaction prediction problem, two RNA sequences are given as inputs and the goal is to find the optimal secondary structure of two RNAs and between them. Some different algorithms have been proposed to predict RNA-RNA interaction structure. However, most of them suffer from high computational time. Results In this paper, we introduce a novel genetic algorithm called GRNAs to predict the RNA-RNA interaction. The proposed algorithm is performed on some standard datasets with appropriate accuracy and lower time complexity in comparison to the other state-of-the-art algorithms. In the proposed algorithm, each individual is a secondary structure of two interacting RNAs. The minimum free energy is considered as a fitness function for each individual. In each generation, the algorithm is converged to find the optimal secondary structure (minimum free energy structure) of two interacting RNAs by using crossover and mutation operations. Conclusions This algorithm is properly employed for joint secondary structure prediction. The results achieved on a set of known interacting RNA pairs are compared with the other related algorithms and the effectiveness and validity of the proposed algorithm have been demonstrated. It has been shown that time complexity of the algorithm in each iteration is as efficient as the other approaches. PMID:25114714
Genetic algorithm for extracting rules in discrete domain
Neruda, R.
1995-09-20
We propose a genetic algorithm that evolves families of rules from a set of examples. Inputs and outputs of the problem are discrete and nominal values which makes it difficult to use alternative learning methods that implicitly regard a metric space. A way how to encode sets of rules is presented together with special variants of genetic operators suitable for this encoding. The solution found by means of this process can be used as a core of a rule-based expert system.
Diversity Maintenance in Genetic Programming
NASA Astrophysics Data System (ADS)
Motoki, Tatsuya; Numaguchi, Yasushi
This paper is motivated by an experimental result that better performing genetic programming runs tend to have higher phenotypic diversity. To maintain phenotypic diversity, we apply implicit fitness sharing and its variant, called unfitness multiplying. To apply these methods to problems in which individuals have infinite kinds of possible behaviours, we classify posible behaviours into 50 achievement levels, and assign a reward or a penalty to each level. In implicit fitness sharing a reward is shared out among individuals with the same achievement level, and in unfitness multiplying a penalty is multiplied by the number of individuals with the same level and is distributed to related individuals. Five benchmark problems (11-multiplexer, sextic polynomial, four-sine, intertwined spiral, and artificial ant problems) are used to illustrate the effect of the methods. The results show that our methods clearly promote diversity and lead population to a smooth frequency distribution of achievement levels, and that our methods usually perform better than the original implicit fitness sharing on success rate and the best (raw) fitness. We also observe that the unfitness multiplying makes a quite different ranking over individuals than the one by the implicit fitness sharing.
Subsurface biological activity zone detection using genetic search algorithms
Mahinthakumar, G.; Gwo, J.P.; Moline, G.R.; Webb, O.F.
1999-12-01
Use of generic search algorithms for detection of subsurface biological activity zones (BAZ) is investigated through a series of hypothetical numerical biostimulation experiments. Continuous injection of dissolved oxygen and methane with periodically varying concentration stimulates the cometabolism of indigenous methanotropic bacteria. The observed breakthroughs of methane are used to deduce possible BAZ in the subsurface. The numerical experiments are implemented in a parallel computing environment to make possible the large number of simultaneous transport simulations required by the algorithm. The results show that genetic algorithms are very efficient in locating multiple activity zones, provided the observed signals adequately sample the BAZ.
Genetic algorithm for chromaticity correction in diffraction limited storage rings
NASA Astrophysics Data System (ADS)
Ehrlichman, M. P.
2016-04-01
A multiobjective genetic algorithm is developed for optimizing nonlinearities in diffraction limited storage rings. This algorithm determines sextupole and octupole strengths for chromaticity correction that deliver optimized dynamic aperture and beam lifetime. The algorithm makes use of dominance constraints to breed desirable properties into the early generations. The momentum aperture is optimized indirectly by constraining the chromatic tune footprint and optimizing the off-energy dynamic aperture. The result is an effective and computationally efficient technique for correcting chromaticity in a storage ring while maintaining optimal dynamic aperture and beam lifetime.
Automatic reactor model synthesis with genetic programming.
Dürrenmatt, David J; Gujer, Willi
2012-01-01
Successful modeling of wastewater treatment plant (WWTP) processes requires an accurate description of the plant hydraulics. Common methods such as tracer experiments are difficult and costly and thus have limited applicability in practice; engineers are often forced to rely on their experience only. An implementation of grammar-based genetic programming with an encoding to represent hydraulic reactor models as program trees should fill this gap: The encoding enables the algorithm to construct arbitrary reactor models compatible with common software used for WWTP modeling by linking building blocks, such as continuous stirred-tank reactors. Discharge measurements and influent and effluent concentrations are the only required inputs. As shown in a synthetic example, the technique can be used to identify a set of reactor models that perform equally well. Instead of being guided by experience, the most suitable model can now be chosen by the engineer from the set. In a second example, temperature measurements at the influent and effluent of a primary clarifier are used to generate a reactor model. A virtual tracer experiment performed on the reactor model has good agreement with a tracer experiment performed on-site.
Human-competitive evolution of quantum computing artefacts by Genetic Programming.
Massey, Paul; Clark, John A; Stepney, Susan
2006-01-01
We show how Genetic Programming (GP) can be used to evolve useful quantum computing artefacts of increasing sophistication and usefulness: firstly specific quantum circuits, then quantum programs, and finally system-independent quantum algorithms. We conclude the paper by presenting a human-competitive Quantum Fourier Transform (QFT) algorithm evolved by GP.
The multi-niche crowding genetic algorithm: Analysis and applications
Cedeno, W.
1995-09-01
The ability of organisms to evolve and adapt to the environment has provided mother nature with a rich and diverse set of species. Only organisms well adapted to their environment can survive from one generation to the next, transferring on the traits, that made them successful, to their offspring. Competition for resources and the ever changing environment drives some species to extinction and at the same time others evolve to maintain the delicate balance in nature. In this disertation we present the multi-niche crowding genetic algorithm, a computational metaphor to the survival of species in ecological niches in the face of competition. The multi-niche crowding genetic algorithm maintains stable subpopulations of solutions in multiple niches in multimodal landscapes. The algorithm introduces the concept of crowding selection to promote mating among members with qirnilar traits while allowing many members of the population to participate in mating. The algorithm uses worst among most similar replacement policy to promote competition among members with similar traits while allowing competition among members of different niches as well. We present empirical and theoretical results for the success of the multiniche crowding genetic algorithm for multimodal function optimization. The properties of the algorithm using different parameters are examined. We test the performance of the algorithm on problems of DNA Mapping, Aquifer Management, and the File Design Problem. Applications that combine the use of heuristics and special operators to solve problems in the areas of combinatorial optimization, grouping, and multi-objective optimization. We conclude by presenting the advantages and disadvantages of the algorithm and describing avenues for future investigation to answer other questions raised by this study.
Areibi, Shawki; Yang, Zhen
2004-01-01
Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem. PMID:15355604
Areibi, Shawki; Yang, Zhen
2004-01-01
Combining global and local search is a strategy used by many successful hybrid optimization approaches. Memetic Algorithms (MAs) are Evolutionary Algorithms (EAs) that apply some sort of local search to further improve the fitness of individuals in the population. Memetic Algorithms have been shown to be very effective in solving many hard combinatorial optimization problems. This paper provides a forum for identifying and exploring the key issues that affect the design and application of Memetic Algorithms. The approach combines a hierarchical design technique, Genetic Algorithms, constructive techniques and advanced local search to solve VLSI circuit layout in the form of circuit partitioning and placement. Results obtained indicate that Memetic Algorithms based on local search, clustering and good initial solutions improve solution quality on average by 35% for the VLSI circuit partitioning problem and 54% for the VLSI standard cell placement problem.
A parallel genetic algorithm for the set partitioning problem
Levine, D.
1996-12-31
This paper describes a parallel genetic algorithm developed for the solution of the set partitioning problem- a difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steady-state genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty real-world set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, high- quality integer feasible solutions were found for problems with 36, 699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
Experiences with the PGAPack Parallel Genetic Algorithm library
Levine, D.; Hallstrom, P.; Noelle, D.; Walenz, B.
1997-07-01
PGAPack is the first widely distributed parallel genetic algorithm library. Since its release, several thousand copies have been distributed worldwide to interested users. In this paper we discuss the key components of the PGAPack design philosophy and present a number of application examples that use PGAPack.
Crossover Improvement for the Genetic Algorithm in Information Retrieval.
ERIC Educational Resources Information Center
Vrajitoru, Dana
1998-01-01
In information retrieval (IR), the aim of genetic algorithms (GA) is to help a system to find, in a huge documents collection, a good reply to a query expressed by the user. Analysis of phenomena seen during the implementation of a GA for IR has led to a new crossover operation, which is introduced and compared to other learning methods.…
Optimization of reliability allocation strategies through use of genetic algorithms
Campbell, J.E.; Painton, L.A.
1996-08-01
This paper examines a novel optimization technique called genetic algorithms and its application to the optimization of reliability allocation strategies. Reliability allocation should occur in the initial stages of design, when the objective is to determine an optimal breakdown or allocation of reliability to certain components or subassemblies in order to meet system specifications. The reliability allocation optimization is applied to the design of a cluster tool, a highly complex piece of equipment used in semiconductor manufacturing. The problem formulation is presented, including decision variables, performance measures and constraints, and genetic algorithm parameters. Piecewise ``effort curves`` specifying the amount of effort required to achieve a certain level of reliability for each component of subassembly are defined. The genetic algorithm evolves or picks those combinations of ``effort`` or reliability levels for each component which optimize the objective of maximizing Mean Time Between Failures while staying within a budget. The results show that the genetic algorithm is very efficient at finding a set of robust solutions. A time history of the optimization is presented, along with histograms or the solution space fitness, MTBF, and cost for comparative purposes.
USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
Applying Genetic Algorithms To Query Optimization in Document Retrieval.
ERIC Educational Resources Information Center
Horng, Jorng-Tzong; Yeh, Ching-Chang
2000-01-01
Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)
Classifying epilepsy diseases using artificial neural networks and genetic algorithm.
Koçer, Sabri; Canal, M Rahmi
2011-08-01
In this study, FFT analysis is applied to the EEG signals of the normal and patient subjects and the obtained FFT coefficients are used as inputs in Artificial Neural Network (ANN). The differences shown by the non-stationary random signals such as EEG signals in cases of health and sickness (epilepsy) were evaluated and tried to be analyzed under computer-supported conditions by using artificial neural networks. Multi-Layer Perceptron (MLP) architecture is used Levenberg-Marquardt (LM), Quickprop (QP), Delta-bar delta (DBD), Momentum and Conjugate gradient (CG) learning algorithms, and the best performance was tried to be attained by ensuring the optimization with the use of genetic algorithms of the weights, learning rates, neuron numbers of hidden layer in the training process. This study shows that the artificial neural network increases the classification performance using genetic algorithm.
An Adaptive Immune Genetic Algorithm for Edge Detection
NASA Astrophysics Data System (ADS)
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
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.
Genetic algorithms for multicriteria shape optimization of induction furnace
NASA Astrophysics Data System (ADS)
Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo
2012-09-01
In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.
Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm
Tang, Yinyin; Wang, Yueke; Chen, Jianyun; Li, Xianbin
2016-01-01
The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large–scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi–hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems. PMID:27736986
Genetic Algorithm (GA)-Based Inclinometer Layout Optimization
Liang, Weijie; Zhang, Ping; Chen, Xianping; Cai, Miao; Yang, Daoguo
2015-01-01
This paper presents numerical simulation results of an airflow inclinometer with sensitivity studies and thermal optimization of the printed circuit board (PCB) layout for an airflow inclinometer based on a genetic algorithm (GA). Due to the working principle of the gas sensor, the changes of the ambient temperature may cause dramatic voltage drifts of sensors. Therefore, eliminating the influence of the external environment for the airflow is essential for the performance and reliability of an airflow inclinometer. In this paper, the mechanism of an airflow inclinometer and the influence of different ambient temperatures on the sensitivity of the inclinometer will be examined by the ANSYS-FLOTRAN CFD program. The results show that with changes of the ambient temperature on the sensing element, the sensitivity of the airflow inclinometer is inversely proportional to the ambient temperature and decreases when the ambient temperature increases. GA is used to optimize the PCB thermal layout of the inclinometer. The finite-element simulation method (ANSYS) is introduced to simulate and verify the results of our optimal thermal layout, and the results indicate that the optimal PCB layout greatly improves (by more than 50%) the sensitivity of the inclinometer. The study may be useful in the design of PCB layouts that are related to sensitivity improvement of gas sensors. PMID:25897500
An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm
Kumar, Manish
2015-01-01
One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality. PMID:27065770
JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve
2000-01-01
A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.
Evaluation of algorithms used to order markers on genetic maps.
Mollinari, M; Margarido, G R A; Vencovsky, R; Garcia, A A F
2009-12-01
When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with 100 and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results.
Efficient Improvement of Silage Additives by Using Genetic Algorithms
Davies, Zoe S.; Gilbert, Richard J.; Merry, Roger J.; Kell, Douglas B.; Theodorou, Michael K.; Griffith, Gareth W.
2000-01-01
The enormous variety of substances which may be added to forage in order to manipulate and improve the ensilage process presents an empirical, combinatorial optimization problem of great complexity. To investigate the utility of genetic algorithms for designing effective silage additive combinations, a series of small-scale proof of principle silage experiments were performed with fresh ryegrass. Having established that significant biochemical changes occur over an ensilage period as short as 2 days, we performed a series of experiments in which we used 50 silage additive combinations (prepared by using eight bacterial and other additives, each of which was added at six different levels, including zero [i.e., no additive]). The decrease in pH, the increase in lactate concentration, and the free amino acid concentration were measured after 2 days and used to calculate a “fitness” value that indicated the quality of the silage (compared to a control silage made without additives). This analysis also included a “cost” element to account for different total additive levels. In the initial experiment additive levels were selected randomly, but subsequently a genetic algorithm program was used to suggest new additive combinations based on the fitness values determined in the preceding experiments. The result was very efficient selection for silages in which large decreases in pH and high levels of lactate occurred along with low levels of free amino acids. During the series of five experiments, each of which comprised 50 treatments, there was a steady increase in the amount of lactate that accumulated; the best treatment combination was that used in the last experiment, which produced 4.6 times more lactate than the untreated silage. The additive combinations that were found to yield the highest fitness values in the final (fifth) experiment were assessed to determine a range of biochemical and microbiological quality parameters during full-term silage
NASA Astrophysics Data System (ADS)
Zu, Yun-Xiao; Zhou, Jie
2012-01-01
Multi-user cognitive radio network resource allocation based on the adaptive niche immune genetic algorithm is proposed, and a fitness function is provided. Simulations are conducted using the adaptive niche immune genetic algorithm, the simulated annealing algorithm, the quantum genetic algorithm and the simple genetic algorithm, respectively. The results show that the adaptive niche immune genetic algorithm performs better than the other three algorithms in terms of the multi-user cognitive radio network resource allocation, and has quick convergence speed and strong global searching capability, which effectively reduces the system power consumption and bit error rate.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
NASA Astrophysics Data System (ADS)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
Initialization Method for Grammar-Guided Genetic Programming
NASA Astrophysics Data System (ADS)
García-Arnau, M.; Manrique, D.; Ríos, J.; Rodríguez-Patón, A.
This paper proposes a new tree-generation algorithm for grammarguided genetic programming that includes a parameter to control the maximum size of the trees to be generated. An important feature of this algorithm is that the initial populations generated are adequately distributed in terms of tree size and distribution within the search space. Consequently, genetic programming systems starting from the initial populations generated by the proposed method have a higher convergence speed. Two different problems have been chosen to carry out the experiments: a laboratory test involving searching for arithmetical equalities and the real-world task of breast cancer prognosis. In both problems, comparisons have been made to another five important initialization methods.
A genetic algorithm for fitting Lorentzian line shapes in Mössbauer spectra
NASA Astrophysics Data System (ADS)
Ahonen, Hannu; de Souza Júnior, Paulo A.; Garg, Vijayendra K.
1997-05-01
A genetic algorithm was implemented for finding an approximative solution to the problem of fitting a combination of Lorentzian lines to a measured Mössbauer spectrum. This iterative algorithm exploits the idea of letting several solutions (individuals) compete with each other for the opportunity of being selected to create new solutions (reproduction). Each solution was represented as a string of binary digits (chromosome). New individuals were created by pairwise exchanging bits in the binary representations of two selected solutions (crossover). In addition, the bits in the new solutions may be switched randomly from zero to one or conversely (mutation). The input of the program that implements the genetic algorithm consists of the measured spectrum, the maximum velocity, the peak positions and the expected number of Lorentzian lines in the spectrum. Each line is represented with the help of three variables, which correspond to its intensity, full line width at half maxima and peak position. An additional parameter was associated to the background level in the spectrum. A χ2 test was used for determining the quality of each parameter combination (fitness). The results of the genetic algorithm have been compared with those obtained by a widely used commercial program. The preliminary results obtained seem to be very promising and encourage to further development of the algorithm and its implementation.
Selection Intensity in Genetic Algorithms with Generation Gaps
Cantu-Paz, E.
2000-01-19
This paper presents calculations of the selection intensity of common selection and replacement methods used in genetic algorithms (GAs) with generation gaps. The selection intensity measures the increase of the average fitness of the population after selection, and it can be used to predict the average fitness of the population at each iteration as well as the number of steps until the population converges to a unique solution. In addition, the theory explains the fast convergence of some algorithms with small generation gaps. The accuracy of the calculations was verified experimentally with a simple test function. The results of this study facilitate comparisons between different algorithms, and provide a tool to adjust the selection pressure, which is indispensable to obtain robust algorithms.
Packing Boxes into Multiple Containers Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Menghani, Deepak; Guha, Anirban
2016-07-01
Container loading problems have been studied extensively in the literature and various analytical, heuristic and metaheuristic methods have been proposed. This paper presents two different variants of a genetic algorithm framework for the three-dimensional container loading problem for optimally loading boxes into multiple containers with constraints. The algorithms are designed so that it is easy to incorporate various constraints found in real life problems. The algorithms are tested on data of standard test cases from literature and are found to compare well with the benchmark algorithms in terms of utilization of containers. This, along with the ability to easily incorporate a wide range of practical constraints, makes them attractive for implementation in real life scenarios.
Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm
NASA Astrophysics Data System (ADS)
Şencan Şahin, Arzu
2012-11-01
Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector.
Adaptable Constrained Genetic Programming: Extensions and Applications
NASA Technical Reports Server (NTRS)
Janikow, Cezary Z.
2005-01-01
An evolutionary algorithm applies evolution-based principles to problem solving. To solve a problem, the user defines the space of potential solutions, the representation space. Sample solutions are encoded in a chromosome-like structure. The algorithm maintains a population of such samples, which undergo simulated evolution by means of mutation, crossover, and survival of the fittest principles. Genetic Programming (GP) uses tree-like chromosomes, providing very rich representation suitable for many problems of interest. GP has been successfully applied to a number of practical problems such as learning Boolean functions and designing hardware circuits. To apply GP to a problem, the user needs to define the actual representation space, by defining the atomic functions and terminals labeling the actual trees. The sufficiency principle requires that the label set be sufficient to build the desired solution trees. The closure principle allows the labels to mix in any arity-consistent manner. To satisfy both principles, the user is often forced to provide a large label set, with ad hoc interpretations or penalties to deal with undesired local contexts. This unfortunately enlarges the actual representation space, and thus usually slows down the search. In the past few years, three different methodologies have been proposed to allow the user to alleviate the closure principle by providing means to define, and to process, constraints on mixing the labels in the trees. Last summer we proposed a new methodology to further alleviate the problem by discovering local heuristics for building quality solution trees. A pilot system was implemented last summer and tested throughout the year. This summer we have implemented a new revision, and produced a User's Manual so that the pilot system can be made available to other practitioners and researchers. We have also designed, and partly implemented, a larger system capable of dealing with much more powerful heuristics.
A novel pipeline based FPGA implementation of a genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2014-05-01
To solve problems when an analytical solution is not available, more and more bio-inspired computation techniques have been applied in the last years. Thus, an efficient algorithm is the Genetic Algorithm (GA), which imitates the biological evolution process, finding the solution by the mechanism of "natural selection", where the strong has higher chances to survive. A genetic algorithm is an iterative procedure which operates on a population of individuals called "chromosomes" or "possible solutions" (usually represented by a binary code). GA performs several processes with the population individuals to produce a new population, like in the biological evolution. To provide a high speed solution, pipelined based FPGA hardware implementations are used, with a nstages pipeline for a n-phases genetic algorithm. The FPGA pipeline implementations are constraints by the different execution time of each stage and by the FPGA chip resources. To minimize these difficulties, we propose a bio-inspired technique to modify the crossover step by using non identical twins. Thus two of the chosen chromosomes (parents) will build up two new chromosomes (children) not only one as in classical GA. We analyze the contribution of this method to reduce the execution time in the asynchronous and synchronous pipelines and also the possibility to a cheaper FPGA implementation, by using smaller populations. The full hardware architecture for a FPGA implementation to our target ALTERA development card is presented and analyzed.
Strain gage selection in loads equations using a genetic algorithm
NASA Technical Reports Server (NTRS)
1994-01-01
Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.
Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
A genetic algorithm for layered multisource video distribution
NASA Astrophysics Data System (ADS)
Cheok, Lai-Tee; Eleftheriadis, Alexandros
2005-03-01
We propose a genetic algorithm -- MckpGen -- for rate scaling and adaptive streaming of layered video streams from multiple sources in a bandwidth-constrained environment. A genetic algorithm (GA) consists of several components: a representation scheme; a generator for creating an initial population; a crossover operator for producing offspring solutions from parents; a mutation operator to promote genetic diversity and a repair operator to ensure feasibility of solutions produced. We formulated the problem as a Multiple-Choice Knapsack Problem (MCKP), a variant of Knapsack Problem (KP) and a decision problem in combinatorial optimization. MCKP has many successful applications in fault tolerance, capital budgeting, resource allocation for conserving energy on mobile devices, etc. Genetic algorithms have been used to solve NP-complete problems effectively, such as the KP, however, to the best of our knowledge, there is no GA for MCKP. We utilize a binary chromosome representation scheme for MCKP and design and implement the components, utilizing problem-specific knowledge for solving MCKP. In addition, for the repair operator, we propose two schemes (RepairSimple and RepairBRP). Results show that RepairBRP yields significantly better performance. We further show that the average fitness of the entire population converges towards the best fitness (optimal) value and compare the performance at various bit-rates.
Threshold matrix for digital halftoning by genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Alander, Jarmo T.; Mantere, Timo J.; Pyylampi, Tero
1998-10-01
Digital halftoning is used both in low and high resolution high quality printing technologies. Our method is designed to be mainly used for low resolution ink jet marking machines to produce both gray tone and color images. The main problem with digital halftoning is pink noise caused by the human eye's visual transfer function. To compensate for this the random dot patterns used are optimized to contain more blue than pink noise. Several such dot pattern generator threshold matrices have been created automatically by using genetic algorithm optimization, a non-deterministic global optimization method imitating natural evolution and genetics. A hybrid of genetic algorithm with a search method based on local backtracking was developed together with several fitness functions evaluating dot patterns for rectangular grids. By modifying the fitness function, a family of dot generators results, each with its particular statistical features. Several versions of genetic algorithms, backtracking and fitness functions were tested to find a reasonable combination. The generated threshold matrices have been tested by simulating a set of test images using the Khoros image processing system. Even though the work was focused on developing low resolution marking technology, the resulting family of dot generators can be applied also in other halftoning application areas including high resolution printing technology.
Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization
NASA Technical Reports Server (NTRS)
Shaykhian, Gholam Ali; Sen, S. K.
2007-01-01
Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *
An improved localization algorithm based on genetic algorithm in wireless sensor networks.
Peng, Bo; Li, Lei
2015-04-01
Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.
Genetic algorithms and their use in geophysical problems
NASA Astrophysics Data System (ADS)
Parker, Paul Bradley
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free
Genetic algorithms and their use in Geophysical Problems
Parker, Paul B.
1999-04-01
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free
Genetic Parallel Programming: design and implementation.
Cheang, Sin Man; Leung, Kwong Sak; Lee, Kin Hong
2006-01-01
This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs. However, experimental results show that GPP evolves parallel programs with less computational effort than that of their sequential counterparts. It creates a new approach to evolving a feasible problem solution in parallel program form and then serializes it into a sequential program if required. The effectiveness and efficiency of GPP are investigated using a suite of 14 well-studied benchmark problems. Experimental results show that GPP speeds up evolution substantially.
Feature extraction from multiple data sources using genetic programming.
Szymanski, J. J.; Brumby, Steven P.; Pope, P. A.; Eads, D. R.; Galassi, M. C.; Harvey, N. R.; Perkins, S. J.; Porter, R. B.; Theiler, J. P.; Young, A. C.; Bloch, J. J.; David, N. A.; Esch-Mosher, D. M.
2002-01-01
Feature extration from imagery is an important and long-standing problem in remote sensing. In this paper, we report on work using genetic programming to perform feature extraction simultaneously from multispectral and digital elevation model (DEM) data. The tool used is the GENetic Imagery Exploitation (GENIE) software, which produces image-processing software that inherently combines spatial and spectral processing. GENIE is particularly useful in exploratory studies of imagery, such as one often does in combining data from multiple sources. The user trains the software by painting the feature of interest with a simple graphical user interface. GENIE then uses genetic programming techniques to produce an image-processing pipeline. Here, we demonstrate evolution of image processing algorithms that extract a range of land-cover features including towns, grasslands, wild fire burn scars, and several types of forest. We use imagery from the DOE/NNSA Multispectral Thermal Imager (MTI) spacecraft, fused with USGS 1:24000 scale DEM data.
Thermoluminescence curves simulation using genetic algorithm with factorial design
NASA Astrophysics Data System (ADS)
Popko, E. A.; Weinstein, I. A.
2016-05-01
The evolutionary approach is an effective optimization tool for numeric analysis of thermoluminescence (TL) processes to assess the microparameters of kinetic models and to determine its effects on the shape of TL peaks. In this paper, the procedure for tuning of genetic algorithm (GA) is presented. This approach is based on multifactorial experiment and allows choosing intrinsic mechanisms of evolutionary operators which provide the most efficient algorithm performance. The proposed method is tested by considering the “one trap-one recombination center” (OTOR) model as an example and advantages for approximation of experimental TL curves are shown.
A Dedicated Genetic Algorithm for Localization of Moving Magnetic Objects
Alimi, Roger; Weiss, Eyal; Ram-Cohen, Tsuriel; Geron, Nir; Yogev, Idan
2015-01-01
A dedicated Genetic Algorithm (GA) has been developed to localize the trajectory of ferromagnetic moving objects within a bounded perimeter. Localization of moving ferromagnetic objects is an important tool because it can be employed in situations when the object is obscured. This work is innovative for two main reasons: first, the GA has been tuned to provide an accurate and fast solution to the inverse magnetic field equations problem. Second, the algorithm has been successfully tested using real-life experimental data. Very accurate trajectory localization estimations were obtained over a wide range of scenarios. PMID:26393598
Optimization of multilayer cylindrical cloaks using genetic algorithms and NEWUOA
NASA Astrophysics Data System (ADS)
Sakr, Ahmed A.; Abdelmageed, Alaa K.
2016-06-01
The problem of minimizing the scattering from a multilayer cylindrical cloak is studied. Both TM and TE polarizations are considered. A two-stage optimization procedure using genetic algorithms and NEWUOA (new unconstrained optimization algorithm) is adopted for realizing the cloak using homogeneous isotropic layers. The layers are arranged such that they follow a repeated pattern of alternating DPS and DNG materials. The results show that a good level of invisibility can be realized using a reasonable number of layers. Maintaining the cloak performance over a finite range of frequencies without sacrificing the level of invisibility is achieved.
NASA Astrophysics Data System (ADS)
Windarto, Indratno, S. W.; Nuraini, N.; Soewono, E.
2014-02-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The algorithm begins by defining the optimization variables, defining the cost function (in a minimization problem) or the fitness function (in a maximization problem) and selecting genetic algorithm parameters. The main procedures in genetic algorithm are generating initial population, selecting some chromosomes (individual) as parent's individual, mating, and mutation. In this paper, binary and continuous genetic algorithms were implemented to estimate growth rate and carrying capacity parameter from poultry data cited from literature. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, both algorithms can estimate these parameters well. Suitable range for mutation rate in continuous genetic algorithm is wider than the binary one.
Genetic Algorithm for Initial Orbit Determination with Too Short Arc
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-01-01
The sky surveys of space objects have obtained a huge quantity of too-short-arc (TSA) observation data. However, the classical method of initial orbit determination (IOD) can hardly get reasonable results for the TSAs. The IOD is reduced to a two-stage hierarchical optimization problem containing three variables for each stage. Using the genetic algorithm, a new method of the IOD for TSAs is established, through the selection of optimizing variables as well as the corresponding genetic operator for specific problems. Numerical experiments based on the real measurements show that the method can provide valid initial values for the follow-up work.
Optimal Design of Geodetic Network Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Vajedian, Sanaz; Bagheri, Hosein
2010-05-01
A geodetic network is a network which is measured exactly by techniques of terrestrial surveying based on measurement of angles and distances and can control stability of dams, towers and their around lands and can monitor deformation of surfaces. The main goals of an optimal geodetic network design process include finding proper location of control station (First order Design) as well as proper weight of observations (second order observation) in a way that satisfy all the criteria considered for quality of the network with itself is evaluated by the network's accuracy, reliability (internal and external), sensitivity and cost. The first-order design problem, can be dealt with as a numeric optimization problem. In this designing finding unknown coordinates of network stations is an important issue. For finding these unknown values, network geodetic observations that are angle and distance measurements must be entered in an adjustment method. In this regard, using inverse problem algorithms is needed. Inverse problem algorithms are methods to find optimal solutions for given problems and include classical and evolutionary computations. The classical approaches are analytical methods and are useful in finding the optimum solution of a continuous and differentiable function. Least squares (LS) method is one of the classical techniques that derive estimates for stochastic variables and their distribution parameters from observed samples. The evolutionary algorithms are adaptive procedures of optimization and search that find solutions to problems inspired by the mechanisms of natural evolution. These methods generate new points in the search space by applying operators to current points and statistically moving toward more optimal places in the search space. Genetic algorithm (GA) is an evolutionary algorithm considered in this paper. This algorithm starts with definition of initial population, and then the operators of selection, replication and variation are applied
A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2013-05-01
With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.
A Study of Penalty Function Methods for Constraint Handling with Genetic Algorithm
NASA Technical Reports Server (NTRS)
Ortiz, Francisco
2004-01-01
COMETBOARDS (Comparative Evaluation Testbed of Optimization and Analysis Routines for Design of Structures) is a design optimization test bed that can evaluate the performance of several different optimization algorithms. A few of these optimization algorithms are the sequence of unconstrained minimization techniques (SUMT), sequential linear programming (SLP) and the sequential quadratic programming techniques (SQP). A genetic algorithm (GA) is a search technique that is based on the principles of natural selection or "survival of the fittest". Instead of using gradient information, the GA uses the objective function directly in the search. The GA searches the solution space by maintaining a population of potential solutions. Then, using evolving operations such as recombination, mutation and selection, the GA creates successive generations of solutions that will evolve and take on the positive characteristics of their parents and thus gradually approach optimal or near-optimal solutions. By using the objective function directly in the search, genetic algorithms can be effectively applied in non-convex, highly nonlinear, complex problems. The genetic algorithm is not guaranteed to find the global optimum, but it is less likely to get trapped at a local optimum than traditional gradient-based search methods when the objective function is not smooth and generally well behaved. The purpose of this research is to assist in the integration of genetic algorithm (GA) into COMETBOARDS. COMETBOARDS cast the design of structures as a constrained nonlinear optimization problem. One method used to solve constrained optimization problem with a GA to convert the constrained optimization problem into an unconstrained optimization problem by developing a penalty function that penalizes infeasible solutions. There have been several suggested penalty function in the literature each with there own strengths and weaknesses. A statistical analysis of some suggested penalty functions
Genetic Algorithm Application in Optimization of Wireless Sensor Networks
Norouzi, Ali; Zaim, A. Halim
2014-01-01
There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. PMID:24693235
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Adaptive process control using fuzzy logic and genetic algorithms
NASA Technical Reports Server (NTRS)
Karr, C. L.
1993-01-01
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Design of wavelength-selective waveplates using genetic algorithm
NASA Astrophysics Data System (ADS)
Katayama, Ryuichi
2013-03-01
Wavelength-selective waveplates, which act either identically or differently for plural wavelengths, are useful for optical systems that handle plural wavelengths. However, they cannot be analytically designed because of the complexity of their structure. Genetic algorithm is one of the methods for solving optimization problems and is used for several kinds of optical design (e.g., design of thin films, diffractive optical elements, and lenses). I considered that it is effective for designing wavelength-selective waveplates also and tried to design them using the genetic algorithm for the first time to the best of my knowledge. As a result, four types of wavelength-selective waveplate for three wavelengths (405, 650, and 780 nm) were successfully designed. These waveplates are useful for Blu-ray Disc/Digital Versatile Disc/Compact Disc compatible optical pickups.
Calibration of FRESIM for Singapore expressway using genetic algorithm
Cheu, R.L.; Jin, X.; Srinivasa, D.; Ng, K.C.; Ng, Y.L.
1998-11-01
FRESIM is a microscopic time-stepping simulation model for freeway corridor traffic operations. To enable FRESIM to realistically simulate expressway traffic flow in Singapore, parameters that govern the movement of vehicles needed to be recalibrated for local traffic conditions. This paper presents the application of a genetic algorithm as an optimization method for finding a suitable combination of FRESIM parameter values. The calibration is based on field data collected on weekdays over a 5.8 km segment of the Ayer Rajar Expressway. Independent calibrations have been made for evening peak and midday off-peak traffic. The results show that the genetic algorithm is able to search for two sets of parameter values that enable FRESIM to produce 30-s loop-detector volume and speed (averaged across all lanes) closely matching the field data under two different traffic conditions. The two sets of parameter values are found to produce a consistently good match for data collected in different days.
Forecasting Smoothed Non-Stationary Time Series Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Norouzzadeh, P.; Rahmani, B.; Norouzzadeh, M. S.
We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
NASA Technical Reports Server (NTRS)
Rogers, James L.
2004-01-01
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
A sustainable genetic algorithm for satellite resource allocation
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Campbell, M. L.; Krenz, W. C.
1995-01-01
A hybrid genetic algorithm is used to schedule tasks for 8 satellites, which can be modelled as a robot whose task is to retrieve objects from a two dimensional field. The objective is to find a schedule that maximizes the value of objects retrieved. Typical of the real-world tasks to which this corresponds is the scheduling of ground contacts for a communications satellite. An important feature of our application is that the amount of time available for running the scheduler is not necessarily known in advance. This requires that the scheduler produce reasonably good results after a short period but that it also continue to improve its results if allowed to run for a longer period. We satisfy this requirement by developing what we call a sustainable genetic algorithm.
Users guide to the PGAPack parallel genetic algorithm library
Levine, D.
1996-01-01
PGAPack is a parallel genetic algorithm library that is intended to provide most capabilities desired in a genetic algorithm package, in an integrated, seamless, and portable manner. Key features of PGAPack are as follows: Ability to be called from Fortran or C. Executable on uniprocessors, multiprocessors, multicomputers, and workstation networks. Binary-, integer-, real-, and character-valued native data types. Object-oriented data structure neutral design. Parameterized population replacement. Multiple choices for selection, crossover, and mutation operators. Easy integration of hill-climbing heuristics. Easy-to-use interface for novice and application users. Multiple levels of access for expert users. Full extensibility to support custom operators and new data types. Extensive debugging facilities. Large set of example problems.
Modeling the Volcanic Source at Long Valley, CA, Using a Genetic Algorithm Technique
NASA Technical Reports Server (NTRS)
Tiampo, Kristy F.
1999-01-01
In this project, we attempted to model the deformation pattern due to the magmatic source at Long Valley caldera using a real-value coded genetic algorithm (GA) inversion similar to that found in Michalewicz, 1992. The project has been both successful and rewarding. The genetic algorithm, coded in the C programming language, performs stable inversions over repeated trials, with varying initial and boundary conditions. The original model used a GA in which the geophysical information was coded into the fitness function through the computation of surface displacements for a Mogi point source in an elastic half-space. The program was designed to invert for a spherical magmatic source - its depth, horizontal location and volume - using the known surface deformations. It also included the capability of inverting for multiple sources.
First flights of genetic-algorithm Kitty Hawk
Goldberg, D.E.
1994-12-31
The design of complex systems requires an effective methodology of invention. This paper considers the methodology of the Wright brothers in inventing the powered airplane and suggests how successes in the design of genetic algorithms have come at the hands of a Wright-brothers-like approach. Recent reliable subquadratic results in solving hard problems with nontraditional GAs and predictions of the limits of simple GAs are presented as two accomplishments achieved in this manner.
A quantum genetic algorithm with quantum crossover and mutation operations
NASA Astrophysics Data System (ADS)
SaiToh, Akira; Rahimi, Robabeh; Nakahara, Mikio
2013-11-01
In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm that has a quantum crossover procedure performing crossovers among all chromosomes in parallel for each generation. A complexity analysis shows that a quadratic speedup is achieved over its classical counterpart in the dominant factor of the run time to handle each generation.
Investigation of range extension with a genetic algorithm
Austin, A. S., LLNL
1998-03-04
Range optimization is one of the tasks associated with the development of cost- effective, stand-off, air-to-surface munitions systems. The search for the optimal input parameters that will result in the maximum achievable range often employ conventional Monte Carlo techniques. Monte Carlo approaches can be time-consuming, costly, and insensitive to mutually dependent parameters and epistatic parameter effects. An alternative search and optimization technique is available in genetic algorithms. In the experiments discussed in this report, a simplified platform motion simulator was the fitness function for a genetic algorithm. The parameters to be optimized were the inputs to this motion generator and the simulator`s output (terminal range) was the fitness measure. The parameters of interest were initial launch altitude, initial launch speed, wing angle-of-attack, and engine ignition time. The parameter values the GA produced were validated by Monte Carlo investigations employing a full-scale six-degree-of-freedom (6 DOF) simulation. The best results produced by Monte Carlo processes using values based on the GA derived parameters were within - 1% of the ranges generated by the simplified model using the evolved parameter values. This report has five sections. Section 2 discusses the motivation for the range extension investigation and reviews the surrogate flight model developed as a fitness function for the genetic algorithm tool. Section 3 details the representation and implementation of the task within the genetic algorithm framework. Section 4 discusses the results. Section 5 concludes the report with a summary and suggestions for further research.
OPTIMIZATION OF LONG RURAL FEEDERS USING A GENETIC ALGORITHM
Wishart, Michael; Ledwich, Gerard; Ghosh, Arindam; Ivanovich, Grujica
2010-06-15
This paper describes the optimization of conductor size and the voltage regulator location and magnitude of long rural distribution lines. The optimization minimizes the lifetime cost of the lines, including capital costs and losses while observing voltage drop and operational constraints using a Genetic Algorithm (GA). The GA optimization is applied to a real Single Wire Earth Return (SWER) network in regional Queensland and results are presented.
MAC protocol for ad hoc networks using a genetic algorithm.
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L; Reyna, Alberto
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput.
MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm
Elizarraras, Omar; Panduro, Marco; Méndez, Aldo L.
2014-01-01
The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. PMID:25140339
A meta-learning system based on genetic algorithms
NASA Astrophysics Data System (ADS)
Pellerin, Eric; Pigeon, Luc; Delisle, Sylvain
2004-04-01
The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system"s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.
Designing neuroclassifier fusion system by immune genetic algorithm
NASA Astrophysics Data System (ADS)
Liang, Jimin; Zhao, Heng; Yang, Wanhai
2001-09-01
A multiple neural network classifier fusion system design method using immune genetic algorithm (IGA) is proposed. The IGA is modeled after the mechanics of human immunity. By using vaccination and immune selection in the evolution procedures, the IGA outperforms the traditional genetic algorithms in restraining the degenerate phenomenon and increasing the converging speed. The fusion system consists of N neural network classifiers that work independently and in parallel to classify a given input pattern. The classifiers' outputs are aggregated by a fusion scheme to decide the collective classification results. The goal of the system design is to obtain a fusion system with both good generalization and efficiency in space and time. Two kinds of measures, the accuracy of classification and the size of the neural networks, are used by IGA to evaluate the fusion system. The vaccines are abstracted by a self-adaptive scheme during the evolutionary process. A numerical experiment on the 'alternate labels' problem is implemented and the comparisons of IGA with traditional genetic algorithm are presented.
Genetic Algorithm Optimizes Q-LAW Control Parameters
NASA Technical Reports Server (NTRS)
Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard
2008-01-01
A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.
Neural-network-biased genetic algorithms for materials design
NASA Astrophysics Data System (ADS)
Patra, Tarak; Meenakshisundaram, Venkatesh; Simmons, David
Machine learning tools have been progressively adopted by the materials science community to accelerate design of materials with targeted properties. However, in the search for new materials exhibiting properties and performance beyond that previously achieved, machine learning approaches are frequently limited by two major shortcomings. First, they are intrinsically interpolative. They are therefore better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require the availability of large datasets, which in some fields are not available and would be prohibitively expensive to produce. Here we describe a new strategy for combining genetic algorithms, neural networks and other machine learning tools, and molecular simulation to discover materials with extremal properties in the absence of pre-existing data. Predictions from progressively constructed machine learning tools are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct molecular dynamics simulation. We survey several initial materials design problems we have addressed with this framework and compare its performance to that of standard genetic algorithm approaches. We acknowledge the W. M. Keck Foundation for support of this work.
RCQ-GA: RDF Chain Query Optimization Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hogenboom, Alexander; Milea, Viorel; Frasincar, Flavius; Kaymak, Uzay
The application of Semantic Web technologies in an Electronic Commerce environment implies a need for good support tools. Fast query engines are needed for efficient querying of large amounts of data, usually represented using RDF. We focus on optimizing a special class of SPARQL queries, the so-called RDF chain queries. For this purpose, we devise a genetic algorithm called RCQ-GA that determines the order in which joins need to be performed for an efficient evaluation of RDF chain queries. The approach is benchmarked against a two-phase optimization algorithm, previously proposed in literature. The more complex a query is, the more RCQ-GA outperforms the benchmark in solution quality, execution time needed, and consistency of solution quality. When the algorithms are constrained by a time limit, the overall performance of RCQ-GA compared to the benchmark further improves.
An Airborne Conflict Resolution Approach Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mondoloni, Stephane; Conway, Sheila
2001-01-01
An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.
Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization
NASA Technical Reports Server (NTRS)
Lohn, Jason D.; Kraus, William F.; Haith, Gary L.; Clancy, Daniel (Technical Monitor)
2002-01-01
We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and fitness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more difficult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at finding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA finds a solution that dominates solutions produced by eight other algorithms, yet the CGA has poor coverage across the Pareto front.
Models, algorithms and programs for phylogeny reconciliation.
Doyon, Jean-Philippe; Ranwez, Vincent; Daubin, Vincent; Berry, Vincent
2011-09-01
Gene sequences contain a gold mine of phylogenetic information. But unfortunately for taxonomists this information does not only tell the story of the species from which it was collected. Genes have their own complex histories which record speciation events, of course, but also many other events. Among them, gene duplications, transfers and losses are especially important to identify. These events are crucial to account for when reconstructing the history of species, and they play a fundamental role in the evolution of genomes, the diversification of organisms and the emergence of new cellular functions. We review reconciliations between gene and species trees, which are rigorous approaches for identifying duplications, transfers and losses that mark the evolution of a gene family. Existing reconciliation models and algorithms are reviewed and difficulties in modeling gene transfers are discussed. We also compare different reconciliation programs along with their advantages and disadvantages.
Models, algorithms and programs for phylogeny reconciliation.
Doyon, Jean-Philippe; Ranwez, Vincent; Daubin, Vincent; Berry, Vincent
2011-09-01
Gene sequences contain a gold mine of phylogenetic information. But unfortunately for taxonomists this information does not only tell the story of the species from which it was collected. Genes have their own complex histories which record speciation events, of course, but also many other events. Among them, gene duplications, transfers and losses are especially important to identify. These events are crucial to account for when reconstructing the history of species, and they play a fundamental role in the evolution of genomes, the diversification of organisms and the emergence of new cellular functions. We review reconciliations between gene and species trees, which are rigorous approaches for identifying duplications, transfers and losses that mark the evolution of a gene family. Existing reconciliation models and algorithms are reviewed and difficulties in modeling gene transfers are discussed. We also compare different reconciliation programs along with their advantages and disadvantages. PMID:21949266
Locomotive assignment problem with train precedence using genetic algorithm
NASA Astrophysics Data System (ADS)
Noori, Siamak; Ghannadpour, Seyed Farid
2012-07-01
This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.
A Multi-Objective Genetic Algorithm for Outlier Removal.
Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch
2015-12-28
Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.
A Multi-Objective Genetic Algorithm for Outlier Removal.
Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch
2015-12-28
Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications. PMID:26553402
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
A Comparison of Genetic Programming Variants for Hyper-Heuristics
Harris, Sean
2015-03-01
Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.
An implementation of continuous genetic algorithm in parameter estimation of predator-prey model
NASA Astrophysics Data System (ADS)
Windarto
2016-03-01
Genetic algorithm is an optimization method based on the principles of genetics and natural selection in life organisms. The main components of this algorithm are chromosomes population (individuals population), parent selection, crossover to produce new offspring, and random mutation. In this paper, continuous genetic algorithm was implemented to estimate parameters in a predator-prey model of Lotka-Volterra type. For simplicity, all genetic algorithm parameters (selection rate and mutation rate) are set to be constant along implementation of the algorithm. It was found that by selecting suitable mutation rate, the algorithms can estimate these parameters well.
Automatic Data Filter Customization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Mandrake, Lukas
2013-01-01
This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.
Structural health monitoring feature design by genetic programming
NASA Astrophysics Data System (ADS)
Harvey, Dustin Y.; Todd, Michael D.
2014-09-01
Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.
Optimal design of link systems using successive zooming genetic algorithm
NASA Astrophysics Data System (ADS)
Kwon, Young-Doo; Sohn, Chang-hyun; Kwon, Soon-Bum; Lim, Jae-gyoo
2009-07-01
Link-systems have been around for a long time and are still used to control motion in diverse applications such as automobiles, robots and industrial machinery. This study presents a procedure involving the use of a genetic algorithm for the optimal design of single four-bar link systems and a double four-bar link system used in diesel engine. We adopted the Successive Zooming Genetic Algorithm (SZGA), which has one of the most rapid convergence rates among global search algorithms. The results are verified by experiment and the Recurdyn dynamic motion analysis package. During the optimal design of single four-bar link systems, we found in the case of identical input/output (IO) angles that the initial and final configurations show certain symmetry. For the double link system, we introduced weighting factors for the multi-objective functions, which minimize the difference between output angles, providing balanced engine performance, as well as the difference between final output angle and the desired magnitudes of final output angle. We adopted a graphical method to select a proper ratio between the weighting factors.
Turbine blade fixture design using kinematic methods and genetic algorithms
NASA Astrophysics Data System (ADS)
Bausch, John J., III
2000-10-01
The design of fixtures for turbine blades is a difficult problem even for experience toolmakers. Turbine blades are characterized by complex 3D surfaces, high performance materials that are difficult to manufacture, close tolerance finish requirements, and high precision machining accuracy. Tool designers typically rely on modified designs based on experience, but have no analytical tools to guide or even evaluate their designs. This paper examines the application of kinematic algorithms to the design of six-point-nest, seventh-point-clamp datum transfer fixtures for turbine blade production. The kinematic algorithms, based on screw coordinate theory, are computationally intensive. When used in a blind search mode the time required to generate an actual design is unreasonable. In order to reduce the computation time, the kinematic methods are combined with genetic algorithms and a set of heuristic design rules to guide the search. The kinematic, genetic, and heuristic methods were integrated within a fixture design module as part of the Unigraphics CAD system used by Pratt and Whitney. The kinematic design module was used to generate a datum transfer fixture design for a standard production turbine blade. This design was then used to construct an actual fixture, and compared to the existing production fixture for the same part. The positional accuracy of both designs was compared using a coordinate measurement machine (CMM). Based on the CMM data, the observed variation of kinematic design was over two orders-of-magnitude less than for the production design resulting in greatly improved accuracy.
High-Speed General Purpose Genetic Algorithm Processor.
Hoseini Alinodehi, Seyed Pourya; Moshfe, Sajjad; Saber Zaeimian, Masoumeh; Khoei, Abdollah; Hadidi, Khairollah
2016-07-01
In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.
The mGA1.0: A common LISP implementation of a messy genetic algorithm
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Kerzic, Travis
1990-01-01
Genetic algorithms (GAs) are finding increased application in difficult search, optimization, and machine learning problems in science and engineering. Increasing demands are being placed on algorithm performance, and the remaining challenges of genetic algorithm theory and practice are becoming increasingly unavoidable. Perhaps the most difficult of these challenges is the so-called linkage problem. Messy GAs were created to overcome the linkage problem of simple genetic algorithms by combining variable-length strings, gene expression, messy operators, and a nonhomogeneous phasing of evolutionary processing. Results on a number of difficult deceptive test functions are encouraging with the mGA always finding global optima in a polynomial number of function evaluations. Theoretical and empirical studies are continuing, and a first version of a messy GA is ready for testing by others. A Common LISP implementation called mGA1.0 is documented and related to the basic principles and operators developed by Goldberg et. al. (1989, 1990). Although the code was prepared with care, it is not a general-purpose code, only a research version. Important data structures and global variations are described. Thereafter brief function descriptions are given, and sample input data are presented together with sample program output. A source listing with comments is also included.
Rabow, A. A.; Scheraga, H. A.
1996-01-01
We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904
Genetic algorithm for multiple bus line coordination on urban arterial.
Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei
2015-01-01
Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority.
Application of genetic algorithms to tuning fuzzy control systems
NASA Technical Reports Server (NTRS)
Espy, Todd; Vombrack, Endre; Aldridge, Jack
1993-01-01
Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.
Genetic algorithm for multiple bus line coordination on urban arterial.
Yang, Zhen; Wang, Wei; Chen, Shuyan; Ding, Haoyang; Li, Xiaowei
2015-01-01
Bus travel time on road section is defined and analyzed with the effect of multiple bus lines. An analytical model is formulated to calculate the total red time a bus encounters when travelling along the arterial. Genetic algorithm is used to optimize the offset scheme of traffic signals to minimize the total red time that all bus lines encounter in two directions of the arterial. The model and algorithm are applied to the major part of Zhongshan North Street in the city of Nanjing. The results show that the methods in this paper can reduce total red time of all the bus lines by 31.9% on the object arterial and thus improve the traffic efficiency of the whole arterial and promote public transport priority. PMID:25663837
A genetic algorithm based method for docking flexible molecules
Judson, R.S.; Jaeger, E.P.; Treasurywala, A.M.
1993-11-01
The authors describe a computational method for docking flexible molecules into protein binding sites. The method uses a genetic algorithm (GA) to search the combined conformation/orientation space of the molecule to find low energy conformation. Several techniques are described that increase the efficiency of the basic search method. These include the use of several interacting GA subpopulations or niches; the use of a growing algorithm that initially docks only a small part of the molecule; and the use of gradient minimization during the search. To illustrate the method, they dock Cbz-GlyP-Leu-Leu (ZGLL) into thermolysin. This system was chosen because a well refined crystal structure is available and because another docking method had previously been tested on this system. Their method is able to find conformations that lie physically close to and in some cases lower in energy than the crystal conformation in reasonable periods of time on readily available hardware.
Optimization in optical systems revisited: Beyond genetic algorithms
NASA Astrophysics Data System (ADS)
Gagnon, Denis; Dumont, Joey; Dubé, Louis
2013-05-01
Designing integrated photonic devices such as waveguides, beam-splitters and beam-shapers often requires optimization of a cost function over a large solution space. Metaheuristics - algorithms based on empirical rules for exploring the solution space - are specifically tailored to those problems. One of the most widely used metaheuristics is the standard genetic algorithm (SGA), based on the evolution of a population of candidate solutions. However, the stochastic nature of the SGA sometimes prevents access to the optimal solution. Our goal is to show that a parallel tabu search (PTS) algorithm is more suited to optimization problems in general, and to photonics in particular. PTS is based on several search processes using a pool of diversified initial solutions. To assess the performance of both algorithms (SGA and PTS), we consider an integrated photonics design problem, the generation of arbitrary beam profiles using a two-dimensional waveguide-based dielectric structure. The authors acknowledge financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC).
Soewono, C. N.; Takaki, N.
2012-07-01
In this work genetic algorithm was proposed to solve fuel loading pattern optimization problem in thorium fueled heavy water reactor. The objective function of optimization was to maximize the conversion ratio and minimize power peaking factor. Those objectives were simultaneously optimized using non-dominated Pareto-based population ranking optimal method. Members of non-dominated population were assigned selection probabilities based on their rankings in a manner similar to Baker's single criterion ranking selection procedure. A selected non-dominated member was bred through simple mutation or one-point crossover process to produce a new member. The genetic algorithm program was developed in FORTRAN 90 while neutronic calculation and analysis was done by COREBN code, a module of core burn-up calculation for SRAC. (authors)
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2002-01-01
As part of the NASA Aviation Safety Program, a unique model-based diagnostics method that employs neural networks and genetic algorithms for aircraft engine performance diagnostics has been developed and demonstrated at the NASA Glenn Research Center against a nonlinear gas turbine engine model. Neural networks are applied to estimate the internal health condition of the engine, and genetic algorithms are used for sensor fault detection, isolation, and quantification. This hybrid architecture combines the excellent nonlinear estimation capabilities of neural networks with the capability to rank the likelihood of various faults given a specific sensor suite signature. The method requires a significantly smaller data training set than a neural network approach alone does, and it performs the combined engine health monitoring objectives of performance diagnostics and sensor fault detection and isolation in the presence of nominal and degraded engine health conditions.
Peng, Bin; Liu, Ke-ling; Li, Zhi-min; Wang, Yue-song; Huang, Tu-jiang
2002-06-01
Genetic algorithm (GA) is used in automatic qualitative analysis by a sequential inductively coupled plasma spectrometer (ICP-AES) and a computer program is developed in this paper. No any standard samples are needed, and spectroscopic interferences can be eliminated. All elements and their concentration ranges of an unknown sample can be reported. The replication rate Pr, crossover rate Pc, and mutation rate of the genetic algorithm were adjusted to be 0.6, 0.4 and 0 respectively. The analytical results of GA are in good agreement with the reference values. It indicates that, combined with the intensity information, the GA can be applied to spectroscopic qualitative analysis and expected to become an effective method in qualitative analysis in ICP-AES after further work. PMID:12938334
Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.
2016-11-01
Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.
Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.
2016-08-01
Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.
Application of Genetic Algorithms in Nonlinear Heat Conduction Problems
Khan, Waqar A.
2014-01-01
Genetic algorithms are employed to optimize dimensionless temperature in nonlinear heat conduction problems. Three common geometries are selected for the analysis and the concept of minimum entropy generation is used to determine the optimum temperatures under the same constraints. The thermal conductivity is assumed to vary linearly with temperature while internal heat generation is assumed to be uniform. The dimensionless governing equations are obtained for each selected geometry and the dimensionless temperature distributions are obtained using MATLAB. It is observed that GA gives the minimum dimensionless temperature in each selected geometry. PMID:24695517
Optimal brushless DC motor design using genetic algorithms
NASA Astrophysics Data System (ADS)
Rahideh, A.; Korakianitis, T.; Ruiz, P.; Keeble, T.; Rothman, M. T.
2010-11-01
This paper presents a method for the optimal design of a slotless permanent magnet brushless DC (BLDC) motor with surface mounted magnets using a genetic algorithm. Characteristics of the motor are expressed as functions of motor geometries. The objective function is a combination of losses, volume and cost to be minimized simultaneously. Electrical and mechanical requirements (i.e. voltage, torque and speed) and other limitations (e.g. upper and lower limits of the motor geometries) are cast into constraints of the optimization problem. One sample case is used to illustrate the design and optimization technique.
Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms
NASA Technical Reports Server (NTRS)
Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.
1997-01-01
The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.
Using genetic algorithms for solving heavy-atom sites.
Chang, G; Lewis, M
1994-09-01
A novel procedure has been developed for locating heavy-atom positions in crystals of macromolecules. This method used genetic algorithms (GA's) to search for heavy-atom sites that are consistent with an observed difference Patterson function. The procedure is straightforward to apply, space-group independent, and particularly powerful for cases involving non-crystallographic symmetry of multiple heavy atoms in the asymmetric unit. In this paper, we introduce how GA's are used for determining the heavy-atom positions and show how this method is more efficient than a sequential search. PMID:15299364
Flexible Job-Shop Scheduling Problem by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Ida, Kenichi; Oka, Kensaku
Flexible Job-shop Scheduling Problem is expansion of the traditional Job-shop Scheduling Problem that an operation can be processed one or more machines. The purpose of this problem is to look for the smallest makespan. For that purpose, it is necessary to decide optimal assignment of machines to operations and order of operations on machines. In this paper, we focus on maximum of workloads for all machines and propose new suvival selection, creation method of initial solution, mutation, and escape method to Genetic Algorithm for this problem. The efficacy of our method is demonstrated by comparing its numerical experiment results with another methods.
Parameterization of interatomic potential by genetic algorithms: A case study
Ghosh, Partha S. Arya, A.; Dey, G. K.; Ranawat, Y. S.
2015-06-24
A framework for Genetic Algorithm based methodology is developed to systematically obtain and optimize parameters for interatomic force field functions for MD simulations by fitting to a reference data base. This methodology is applied to the fitting of ThO{sub 2} (CaF{sub 2} prototype) – a representative of ceramic based potential fuel for nuclear applications. The resulting GA optimized parameterization of ThO{sub 2} is able to capture basic structural, mechanical, thermo-physical properties and also describes defect structures within the permissible range.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms (GA) in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Full design of fuzzy controllers using genetic algorithms
NASA Technical Reports Server (NTRS)
Homaifar, Abdollah; Mccormick, ED
1992-01-01
This paper examines the applicability of genetic algorithms in the complete design of fuzzy logic controllers. While GA has been used before in the development of rule sets or high performance membership functions, the interdependence between these two components dictates that they should be designed together simultaneously. GA is fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. We show the application of this new method to the development of a cart controller.
Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Tang, Xinggang; Zhang, Weihong; Zhu, Jihong
A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.
Genetic Algorithm based Decentralized PI Type Controller: Load Frequency Control
NASA Astrophysics Data System (ADS)
Dwivedi, Atul; Ray, Goshaidas; Sharma, Arun Kumar
2016-12-01
This work presents a design of decentralized PI type Linear Quadratic (LQ) controller based on genetic algorithm (GA). The proposed design technique allows considerable flexibility in defining the control objectives and it does not consider any knowledge of the system matrices and moreover it avoids the solution of algebraic Riccati equation. To illustrate the results of this work, a load-frequency control problem is considered. Simulation results reveal that the proposed scheme based on GA is an alternative and attractive approach to solve load-frequency control problem from both performance and design point of views.
Overdetermined broadband spectroscopic Mueller matrix polarimeter designed by genetic algorithms.
Aas, Lars Martin Sandvik; Ellingsen, Pål Gunnar; Fladmark, Bent Even; Letnes, Paul Anton; Kildemo, Morten
2013-04-01
This paper reports on the design and implementation of a liquid crystal variable retarder based overdetermined spectroscopic Mueller matrix polarimeter, with parallel processing of all wavelengths. The system was designed using a modified version of a recently developed genetic algorithm [Letnes et al. Opt. Express 18, 22, 23095 (2010)]. A generalization of the eigenvalue calibration method is reported that allows the calibration of such overdetermined polarimetric systems. Out of several possible designs, one of the designs was experimentally implemented and calibrated. It is reported that the instrument demonstrated good performance, with a measurement accuracy in the range of 0.1% for the measurement of air. PMID:23571964
Algorithm and program for information processing with the filin apparatus
NASA Technical Reports Server (NTRS)
Gurin, L. S.; Morkrov, V. S.; Moskalenko, Y. I.; Tsoy, K. A.
1979-01-01
The reduction of spectral radiation data from space sources is described. The algorithm and program for identifying segments of information obtained from the Film telescope-spectrometer on the Salyut-4 are presented. The information segments represent suspected X-ray sources. The proposed algorithm is an algorithm of the lowest level. Following evaluation, information free of uninformative segments is subject to further processing with algorithms of a higher level. The language used is FORTRAN 4.
Optimal Refueling Pattern Search for a CANDU Reactor Using a Genetic Algorithm
Quang Binh, DO; Gyuhong, ROH; Hangbok, CHOI
2006-07-01
This paper presents the results from the application of genetic algorithms to a refueling optimization of a Canada deuterium uranium (CANDU) reactor. This work aims at making a mathematical model of the refueling optimization problem including the objective function and constraints and developing a method based on genetic algorithms to solve the problem. The model of the optimization problem and the proposed method comply with the key features of the refueling strategy of the CANDU reactor which adopts an on-power refueling operation. In this study, a genetic algorithm combined with an elitism strategy was used to automatically search for the refueling patterns. The objective of the optimization was to maximize the discharge burn-up of the refueling bundles, minimize the maximum channel power, or minimize the maximum change in the zone controller unit (ZCU) water levels. A combination of these objectives was also investigated. The constraints include the discharge burn-up, maximum channel power, maximum bundle power, channel power peaking factor and the ZCU water level. A refueling pattern that represents the refueling rate and channels was coded by a one-dimensional binary chromosome, which is a string of binary numbers 0 and 1. A computer program was developed in FORTRAN 90 running on an HP 9000 workstation to conduct the search for the optimal refueling patterns for a CANDU reactor at the equilibrium state. The results showed that it was possible to apply genetic algorithms to automatically search for the refueling channels of the CANDU reactor. The optimal refueling patterns were compared with the solutions obtained from the AUTOREFUEL program and the results were consistent with each other. (authors)
Analysis of the numerical effects of parallelism on a parallel genetic algorithm
Hart, W.E.; Belew, R.K.; Kohn, S.; Baden, S.
1995-09-18
This paper examines the effects of relaxed synchronization on both the numerical and parallel efficiency of parallel genetic algorithms (GAs). We describe a coarse-grain geographically structured parallel genetic algorithm. Our experiments show that asynchronous versions of these algorithms have a lower run time than-synchronous GAs. Furthermore, we demonstrate that this improvement in performance is partly due to the fact that the numerical efficiency of the asynchronous genetic algorithm is better than the synchronous genetic algorithm. Our analysis includes a critique of the utility of traditional parallel performance measures for parallel GAs, and we evaluate the claims made by several researchers that parallel GAs can have superlinear speedup.
An Introduction to Genetic Algorithms and to Their Use in Information Retrieval.
ERIC Educational Resources Information Center
Jones, Gareth; And Others
1994-01-01
Genetic algorithms, a class of nondeterministic algorithms in which the role of chance makes the precise nature of a solution impossible to guarantee, seem to be well suited to combinatorial-optimization problems in information retrieval. Provides an introduction to techniques and characteristics of genetic algorithms and illustrates their…
Segmentation of thermographic images of hands using a genetic algorithm
NASA Astrophysics Data System (ADS)
Ghosh, Payel; Mitchell, Melanie; Gold, Judith
2010-01-01
This paper presents a new technique for segmenting thermographic images using a genetic algorithm (GA). The individuals of the GA also known as chromosomes consist of a sequence of parameters of a level set function. Each chromosome represents a unique segmenting contour. An initial population of segmenting contours is generated based on the learned variation of the level set parameters from training images. Each segmenting contour (an individual) is evaluated for its fitness based on the texture of the region it encloses. The fittest individuals are allowed to propagate to future generations of the GA run using selection, crossover and mutation. The dataset consists of thermographic images of hands of patients suffering from upper extremity musculo-skeletal disorders (UEMSD). Thermographic images are acquired to study the skin temperature as a surrogate for the amount of blood flow in the hands of these patients. Since entire hands are not visible on these images, segmentation of the outline of the hands on these images is typically performed by a human. In this paper several different methods have been tried for segmenting thermographic images: Gabor-wavelet-based texture segmentation method, the level set method of segmentation and our GA which we termed LSGA because it combines level sets with genetic algorithms. The results show a comparative evaluation of the segmentation performed by all the methods. We conclude that LSGA successfully segments entire hands on images in which hands are only partially visible.
Actuator Placement Via Genetic Algorithm for Aircraft Morphing
NASA Technical Reports Server (NTRS)
Crossley, William A.; Cook, Andrea M.
2001-01-01
This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.
Feature Subset Selection, Class Separability, and Genetic Algorithms
Cantu-Paz, E
2004-01-21
The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore, the problem of feature subset selection has received considerable attention. Genetic approaches to this problem usually follow the wrapper approach: treat the inducer as a black box that is used to evaluate candidate feature subsets. The evaluations might take a considerable time and the traditional approach might be unpractical for large data sets. This paper describes a hybrid of a simple genetic algorithm and a method based on class separability applied to the selection of feature subsets for classification problems. The proposed hybrid was compared against each of its components and two other feature selection wrappers that are used widely. The objective of this paper is to determine if the proposed hybrid presents advantages over the other methods in terms of accuracy or speed in this problem. The experiments used a Naive Bayes classifier and public-domain and artificial data sets. The experiments suggest that the hybrid usually finds compact feature subsets that give the most accurate results, while beating the execution time of the other wrappers.
Genetic algorithms for geophysical parameter inversion from altimeter data
NASA Astrophysics Data System (ADS)
Ramillien, Guillaume
2001-11-01
A new approach for inverting several geophysical parameters at the same time from altimeter and marine data by implementing genetic algorithms (GAs) is presented. These original techniques of optimization based on non-deterministic rules simulate the evolution of a population of candidate solutions for a given objective function to minimize. They offer a robust and efficient alternative to gradient techniques for non-linear parameter inversion. Here genetic algorithms are used for solving a discrete gravity problem of data associated with an undersea relief, to retrieve seven parameters at the same time: the elastic thickness, the mean ocean depth, the seamount location (longitude/latitude), its amplitude, radius and density from its observed gravity/geoid signature. This approach was also successfully used to adjust lithosphere parameters in the real case of the Rarotonga seamount [21.2°S 159.8°W] in the Southern Cook Islands region, where GA simulations provided robust estimates of these seven parameters. The GA found very realistic values for the mean ocean depth and the seamount amplitude and the precise geographical location of Rarotonga Island. Moreover, the values of elastic thickness (~14-15km) and seamount density (~2850-2870kgm-3) estimated by the GA are consistent with the ones proposed in earlier studies.
Generation of Compliant Mechanisms using Hybrid Genetic Algorithm
NASA Astrophysics Data System (ADS)
Sharma, D.; Deb, K.
2014-10-01
Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( η). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for η value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.
Optimal robust motion controller design using multiobjective genetic algorithm.
Sarjaš, Andrej; Svečko, Rajko; Chowdhury, Amor
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm-differential evolution. PMID:24987749
Experience with a Genetic Algorithm Implemented on a Multiprocessor Computer
NASA Technical Reports Server (NTRS)
Plassman, Gerald E.; Sobieszczanski-Sobieski, Jaroslaw
2000-01-01
Numerical experiments were conducted to find out the extent to which a Genetic Algorithm (GA) may benefit from a multiprocessor implementation, considering, on one hand, that analyses of individual designs in a population are independent of each other so that they may be executed concurrently on separate processors, and, on the other hand, that there are some operations in a GA that cannot be so distributed. The algorithm experimented with was based on a gaussian distribution rather than bit exchange in the GA reproductive mechanism, and the test case was a hub frame structure of up to 1080 design variables. The experimentation engaging up to 128 processors confirmed expectations of radical elapsed time reductions comparing to a conventional single processor implementation. It also demonstrated that the time spent in the non-distributable parts of the algorithm and the attendant cross-processor communication may have a very detrimental effect on the efficient utilization of the multiprocessor machine and on the number of processors that can be used effectively in a concurrent manner. Three techniques were devised and tested to mitigate that effect, resulting in efficiency increasing to exceed 99 percent.
Optimization of an antenna array using genetic algorithms
Kiehbadroudinezhad, Shahideh; Noordin, Nor Kamariah; Sali, A.; Abidin, Zamri Zainal
2014-06-01
An array of antennas is usually used in long distance communication. The observation of celestial objects necessitates a large array of antennas, such as the Giant Metrewave Radio Telescope (GMRT). Optimizing this kind of array is very important when observing a high performance system. The genetic algorithm (GA) is an optimization solution for these kinds of problems that reconfigures the position of antennas to increase the u-v coverage plane or decrease the sidelobe levels (SLLs). This paper presents how to optimize a correlator antenna array using the GA. A brief explanation about the GA and operators used in this paper (mutation and crossover) is provided. Then, the results of optimization are discussed. The results show that the GA provides efficient and optimum solutions among a pool of candidate solutions in order to achieve the desired array performance for the purposes of radio astronomy. The proposed algorithm is able to distribute the u-v plane more efficiently than GMRT with a more than 95% distribution ratio at snapshot, and to fill the u-v plane from a 20% to more than 68% filling ratio as the number of generations increases in the hour tracking observations. Finally, the algorithm is able to reduce the SLL to –21.75 dB.
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR. PMID:25405225
Synthesizing Dynamic Programming Algorithms from Linear Temporal Logic Formulae
NASA Technical Reports Server (NTRS)
Rosu, Grigore; Havelund, Klaus
2001-01-01
The problem of testing a linear temporal logic (LTL) formula on a finite execution trace of events, generated by an executing program, occurs naturally in runtime analysis of software. We present an algorithm which takes an LTL formula and generates an efficient dynamic programming algorithm. The generated algorithm tests whether the LTL formula is satisfied by a finite trace of events given as input. The generated algorithm runs in linear time, its constant depending on the size of the LTL formula. The memory needed is constant, also depending on the size of the formula.
Computational fluid dynamics based bulbous bow optimization using a genetic algorithm
NASA Astrophysics Data System (ADS)
Mahmood, Shahid; Huang, Debo
2012-09-01
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of "total resistance". Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Ancestral genome inference using a genetic algorithm approach.
Gao, Nan; Yang, Ning; Tang, Jijun
2013-01-01
Recent advancement of technologies has now made it routine to obtain and compare gene orders within genomes. Rearrangements of gene orders by operations such as reversal and transposition are rare events that enable researchers to reconstruct deep evolutionary histories. An important application of genome rearrangement analysis is to infer gene orders of ancestral genomes, which is valuable for identifying patterns of evolution and for modeling the evolutionary processes. Among various available methods, parsimony-based methods (including GRAPPA and MGR) are the most widely used. Since the core algorithms of these methods are solvers for the so called median problem, providing efficient and accurate median solver has attracted lots of attention in this field. The "double-cut-and-join" (DCJ) model uses the single DCJ operation to account for all genome rearrangement events. Because mathematically it is much simpler than handling events directly, parsimony methods using DCJ median solvers has better speed and accuracy. However, the DCJ median problem is NP-hard and although several exact algorithms are available, they all have great difficulties when given genomes are distant. In this paper, we present a new algorithm that combines genetic algorithm (GA) with genomic sorting to produce a new method which can solve the DCJ median problem in limited time and space, especially in large and distant datasets. Our experimental results show that this new GA-based method can find optimal or near optimal results for problems ranging from easy to very difficult. Compared to existing parsimony methods which may severely underestimate the true number of evolutionary events, the sorting-based approach can infer ancestral genomes which are much closer to their true ancestors. The code is available at http://phylo.cse.sc.edu. PMID:23658708
Genetic programming approach to extracting features from remotely sensed imagery
Theiler, J. P.; Perkins, S. J.; Harvey, N. R.; Szymanski, J. J.; Brumby, Steven P.
2001-01-01
Multi-instrument data sets present an interesting challenge to feature extraction algorithm developers. Beyond the immediate problems of spatial co-registration, the remote sensing scientist must explore a complex algorithm space in which both spatial and spectral signatures may be required to identify a feature of interest. We describe a genetic programming/supervised classifier software system, called Genie, which evolves and combines spatio-spectral image processing tools for remotely sensed imagery. We describe our representation of candidate image processing pipelines, and discuss our set of primitive image operators. Our primary application has been in the field of geospatial feature extraction, including wildfire scars and general land-cover classes, using publicly available multi-spectral imagery (MSI) and hyper-spectral imagery (HSI). Here, we demonstrate our system on Landsat 7 Enhanced Thematic Mapper (ETM+) MSI. We exhibit an evolved pipeline, and discuss its operation and performance.
Bornholdt, S.; Graudenz, D.
1993-07-01
A learning algorithm based on genetic algorithms for asymmetric neural networks with an arbitrary structure is presented. It is suited for the learning of temporal patterns and leads to stable neural networks with feedback.
Genetic Algorithms, Pulsar Planets, and Ionized Interstellar Microturbulence
NASA Astrophysics Data System (ADS)
Lazio, T. J.
1997-09-01
We probe the intense microturbulence in the Galactic center and the radio-wave scattering it generates by analyzing observations of extragalactic sources, OH and H$_2$O masers, and free-free emission. The region responsible for the enhanced, anisotropic angular broadening of Sgr~A$^*$ and nearby OH masers is within 150~pc of the Galactic center and has an angular radius $\\approx 1\\arcdeg$. The enhanced scattering probably occurs in the interface regions between $10^7$~K gas and molecular clouds and is a manifestation of the energetic processes occurring in the Galactic center. Radio scattering measurements are also used to probe turbulent gas toward the Galactic anticenter. Ionized gas at Galactocentric distances $\\sim 50$~kpc is suggested by absorption lines in quasar spectra, the appearance of the H 1 disks of nearby galaxies, and models for low-redshift quasar absorption systems and Galactic ``fountains.'' We conducted multifrequency, Very Long Baseline Array (VLBA) observations on twelve extragalactic sources in order to measure their scattering diameters. Seven sources are at $|b| < 1\\arcdeg$ and their lines of sight potentially probe path lengths $\\gtrsim 50$~kpc through the disk. We find that the ionized disk is unwarped, has an extent of $\\approx 20$~kpc, and traces the extent of massive star formation in the outer Galaxy. Planetary companions to neutron stars are challenging to recognize amid the several processes that contribute to pulsar arrival time data. We use a genetic algorithm to search for planetary companions to pulsars. Genetic algorithms are an optimization method that uses biological-like concepts such as survival of the fittest, mutation, and chromosome exchange. The algorithm searches parameter space in the same way that life finds optimal niches in the biological environment---incremental rewarding of successful variations. Fitting for Keplerian orbits requires a search through four non-linear parameters per planet and is especially
[Non-linear rectification of sensor based on immune genetic Algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
[Non-linear rectification of sensor based on immune genetic algorithm].
Lu, Lirong; Zhou, Jinyang; Niu, Xiaodong
2014-08-01
A non-linear rectification based on immune genetic algorithm (IGA) is proposed in this paper, for the shortcoming of the non-linearity rectification. This algorithm introducing the biologic immune mechanism into the genetic algorithm can restrain the disadvantages that the poor precision, slow convergence speed and early maturity of the genetic algorithm. Computer simulations indicated that the algorithm not only keeps population diversity, but also increases the convergent speed, precision and the stability greatly. The results have shown the correctness and effectiveness of the method.
Improving ecological forecasts of copepod community dynamics using genetic algorithms
NASA Astrophysics Data System (ADS)
Record, N. R.; Pershing, A. J.; Runge, J. A.; Mayo, C. A.; Monger, B. C.; Chen, C.
2010-08-01
The validity of computational models is always in doubt. Skill assessment and validation are typically done by demonstrating that output is in agreement with empirical data. We test this approach by using a genetic algorithm to parameterize a biological-physical coupled copepod population dynamics computation. The model is applied to Cape Cod Bay, Massachusetts, and is designed for operational forecasting. By running twin experiments on terms in this dynamical system, we demonstrate that a good fit to data does not necessarily imply a valid parameterization. An ensemble of good fits, however, provides information on the accuracy of parameter values, on the functional importance of parameters, and on the ability to forecast accurately with an incorrect set of parameters. Additionally, we demonstrate that the technique is a useful tool for operational forecasting.
Alien Genetic Algorithm for Exploration of Search Space
NASA Astrophysics Data System (ADS)
Patel, Narendra; Padhiyar, Nitin
2010-10-01
Genetic Algorithm (GA) is a widely accepted population based stochastic optimization technique used for single and multi objective optimization problems. Various versions of modifications in GA have been proposed in last three decades mainly addressing two issues, namely increasing convergence rate and increasing probability of global minima. While both these. While addressing the first issue, GA tends to converge to a local optima and addressing the second issue corresponds the large computational efforts. Thus, to reduce the contradictory effects of these two aspects, we propose a modification in GA by adding an alien member in the population at every generation. Addition of an Alien member in the current population at every generation increases the probability of obtaining global minima at the same time maintaining higher convergence rate. With two test cases, we have demonstrated the efficacy of the proposed GA by comparing with the conventional GA.
Optimizing the controllability of arbitrary networks with genetic algorithm
NASA Astrophysics Data System (ADS)
Li, Xin-Feng; Lu, Zhe-Ming
2016-04-01
Recently, as the controllability of complex networks attracts much attention, how to optimize networks' controllability has become a common and urgent problem. In this paper, we develop an efficient genetic algorithm oriented optimization tool to optimize the controllability of arbitrary networks consisting of both state nodes and control nodes under Popov-Belevitch-Hautus rank condition. The experimental results on a number of benchmark networks show the effectiveness of this method and the evolution of network topology is captured. Furthermore, we explore how network structure affects its controllability and find that the sparser a network is, the more control nodes are needed to control it and the larger the differences between node degrees, the more control nodes are needed to achieve the full control. Our framework provides an alternative to controllability optimization and can be applied to arbitrary networks without any limitations.
Population Induced Instabilities in Genetic Algorithms for Constrained Optimization
NASA Astrophysics Data System (ADS)
Vlachos, D. S.; Parousis-Orthodoxou, K. J.
2013-02-01
Evolutionary computation techniques, like genetic algorithms, have received a lot of attention as optimization techniques but, although they exhibit a very promising potential in curing the problem, they have not produced a significant breakthrough in the area of systematic treatment of constraints. There are two mainly ways of handling the constraints: the first is to produce an infeasibility measure and add it to the general cost function (the well known penalty methods) and the other is to modify the mutation and crossover operation in a way that they only produce feasible members. Both methods have their drawbacks and are strongly correlated to the problem that they are applied. In this work, we propose a different treatment of the constraints: we induce instabilities in the evolving population, in a way that infeasible solution cannot survive as they are. Preliminary results are presented in a set of well known from the literature constrained optimization problems.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2015-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster
NASA Technical Reports Server (NTRS)
Story, George
2014-01-01
Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
Strawberry Maturity Neural Network Detectng System Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Xu, Liming
The quick and non-detective detection of agriculture product is one of the measures to increase the precision and productivity of harvesting and grading. Having analyzed H frequency of different maturities in different light intensities, the results show that H frequency for the same maturity has little influence in different light intensities; Under the same light intensity, three strawberry maturities are changing in order. After having confirmed the H frequency section to distinguish the different strawberry maturity, the triplelayer feed-forward neural network system to detect strawberry maturity was designed by using genetic algorithm. The test results show that the detecting precision ratio is 91.7%, it takes 160ms to distinguish one strawberry. Therefore, the online non-detective detecting the strawberry maturity could be realized.
Genetic Algorithms and Nucleation in VIH-AIDS transition.
NASA Astrophysics Data System (ADS)
Barranon, Armando
2003-03-01
VIH to AIDS transition has been modeled via a genetic algorithm that uses boom-boom principle and where population evolution is simulated with a cellular automaton based on SIR model. VIH to AIDS transition is signed by nucleation of infected cells and low probability of infection are obtained for different mutation rates in agreement with clinical results. A power law is obtained with a critical exponent close to the critical exponent of cubic, spherical percolation, colossal magnetic resonance, Ising Model and liquid-gas phase transition in heavy ion collisions. Computations were carried out at UAM-A Supercomputing Lab and author acknowledges financial support from Division of CBI at UAM-A.
Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits.
Watanabe, Leandro H; Myers, Chris J
2014-01-01
This paper describes a hierarchical stochastic simulation algorithm, which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.
Optimization of Power Coefficient of Wind Turbine Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Rajakumar, Sappani; Ravindran, Durairaj; Sivakumar, Mahalingam; Venkatachalam, Gopalan; Muthukumar, Shunmugavelu
2016-06-01
In the design of a wind turbine, the goal is to attain the highest possible power output under specified atmospheric conditions. The optimization of power coefficient of horizontal axis wind turbine has been carried out by integration of blade element momentum method and genetic algorithm (GA). The design variables considered are wind velocity, angle of attack and tip speed ratio. The objective function is power coefficient of wind turbine. The different combination of design variables are optimized using GA and then the Power coefficient is optimized. The optimized design variables are validated with the experimental results available in the literature. By this optimization work the optimum design variables of wind turbine can be found economically than experimental work. NACA44XX series airfoils are considered for this optimization work.
Automatic Arrangement of Utility Poles by Genetic Algorithms
NASA Astrophysics Data System (ADS)
Inoue, Yutaka; Iba, Hitoshi
In this paper, Genetic Algorithms (GAs) have been applied to designing the arrangement of utility poles. There are two purposes of this study. One is to lower the construction cost, i.e., to reduce the number of necessary utility poles. Another goal is to eliminate designer’s load to some extent. In our method, the arrangement is determined with the following three steps: (1) Determine the arrangement of utility poles necessary for supplying the electric power to each customer. (2) Obtain the positions of other utility poles required to support electric wires by searching for the route of electric wires. (3) Adaptively tune the arrangement and the number of utility poles. Experimental results show that the method is more effective in designing for utility poles than the usual approach. The simulation domains include realistic areas with various characteristics. We can confirm that the above two goals are achieved satisfactorily.
Merging of synchrotron serial crystallographic data by a genetic algorithm.
Zander, Ulrich; Cianci, Michele; Foos, Nicolas; Silva, Catarina S; Mazzei, Luca; Zubieta, Chloe; de Maria, Alejandro; Nanao, Max H
2016-09-01
Recent advances in macromolecular crystallography have made it practical to rapidly collect hundreds of sub-data sets consisting of small oscillations of incomplete data. This approach, generally referred to as serial crystallography, has many uses, including an increased effective dose per data set, the collection of data from crystals without harvesting (in situ data collection) and studies of dynamic events such as catalytic reactions. However, selecting which data sets from this type of experiment should be merged can be challenging and new methods are required. Here, it is shown that a genetic algorithm can be used for this purpose, and five case studies are presented in which the merging statistics are significantly improved compared with conventional merging of all data. PMID:27599735
An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
Merging of synchrotron serial crystallographic data by a genetic algorithm
Zander, Ulrich; Cianci, Michele; Foos, Nicolas; Silva, Catarina S.; Mazzei, Luca; Zubieta, Chloe; de Maria, Alejandro; Nanao, Max H.
2016-01-01
Recent advances in macromolecular crystallography have made it practical to rapidly collect hundreds of sub-data sets consisting of small oscillations of incomplete data. This approach, generally referred to as serial crystallography, has many uses, including an increased effective dose per data set, the collection of data from crystals without harvesting (in situ data collection) and studies of dynamic events such as catalytic reactions. However, selecting which data sets from this type of experiment should be merged can be challenging and new methods are required. Here, it is shown that a genetic algorithm can be used for this purpose, and five case studies are presented in which the merging statistics are significantly improved compared with conventional merging of all data. PMID:27599735
Fuzzy Genetic Algorithm Based on Principal Operation and Inequity Degree
NASA Astrophysics Data System (ADS)
Li, Fachao; Jin, Chenxia
In this paper, starting from the structure of fuzzy information, by distinguishing principal indexes and assistant indexes, give comparison of fuzzy information on synthesizing effect and operation of fuzzy optimization on principal indexes transformation, further, propose axiom system of fuzzy inequity degree from essence of constraint, and give an instructive metric method; Then, combining genetic algorithm, give fuzzy optimization methods based on principal operation and inequity degree (denoted by BPO&ID-FGA, for short); Finally, consider its convergence using Markov chain theory and analyze its performance through an example. All these indicate, BPO&ID-FGA can not only effectively merge decision consciousness into the optimization process, but possess better global convergence, so it can be applied to many fuzzy optimization problems.
On a programming language for graph algorithms
NASA Technical Reports Server (NTRS)
Rheinboldt, W. C.; Basili, V. R.; Mesztenyi, C. K.
1971-01-01
An algorithmic language, GRAAL, is presented for describing and implementing graph algorithms of the type primarily arising in applications. The language is based on a set algebraic model of graph theory which defines the graph structure in terms of morphisms between certain set algebraic structures over the node set and arc set. GRAAL is modular in the sense that the user specifies which of these mappings are available with any graph. This allows flexibility in the selection of the storage representation for different graph structures. In line with its set theoretic foundation, the language introduces sets as a basic data type and provides for the efficient execution of all set and graph operators. At present, GRAAL is defined as an extension of ALGOL 60 (revised) and its formal description is given as a supplement to the syntactic and semantic definition of ALGOL. Several typical graph algorithms are written in GRAAL to illustrate various features of the language and to show its applicability.
A new perspective on dark energy modeling via genetic algorithms
NASA Astrophysics Data System (ADS)
Nesseris, Savvas; García-Bellido, Juan
2012-11-01
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity dL(z) and the angular diameter distance dA(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ωm(a) in the growth rate data, fσ8(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function ΩM(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡dL(z)/(1+z)2dA(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ~ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.
A new perspective on dark energy modeling via genetic algorithms
Nesseris, Savvas; García-Bellido, Juan E-mail: juan.garciabellido@uam.es
2012-11-01
We use Genetic Algorithms to extract information from several cosmological probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations (BAO) and the growth rate of matter perturbations. This is done by implementing a model independent and bias-free reconstruction of the various scales and distances that characterize the data, like the luminosity d{sub L}(z) and the angular diameter distance d{sub A}(z) in the SnIa and BAO data, respectively, or the dependence with redshift of the matter density Ω{sub m}(a) in the growth rate data, fσ{sub 8}(z). These quantities can then be used to reconstruct the expansion history of the Universe, and the resulting Dark Energy (DE) equation of state w(z) in the context of FRW models, or the mass radial function Ω{sub M}(r) in LTB models. In this way, the reconstruction is completely independent of our prior bias. Furthermore, we use this method to test the Etherington relation, ie the well-known relation between the luminosity and the angular diameter distance, η≡d{sub L}(z)/(1+z){sup 2}d{sub A}(z), which is equal to 1 in metric theories of gravity. We find that the present data seem to suggest a 3-σ deviation from one at redshifts z ∼ 0.5. Finally, we present a novel way, within the Genetic Algorithm paradigm, to analytically estimate the errors on the reconstructed quantities by calculating a Path Integral over all possible functions that may contribute to the likelihood. We show that this can be done regardless of the data being correlated or uncorrelated with each other and we also explicitly demonstrate that our approach is in good agreement with other error estimation techniques like the Fisher Matrix approach and the Bootstrap Monte Carlo.
Modelling and genetic algorithm based optimisation of inverse supply chain
NASA Astrophysics Data System (ADS)
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
A dynamic programming algorithm for RNA structure prediction including pseudoknots.
Rivas, E; Eddy, S R
1999-02-01
We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of O(N6) in time and O(N4) in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model.
A genetic algorithmic approach to antenna null-steering using a cluster computer.
NASA Astrophysics Data System (ADS)
Recine, Greg; Cui, Hong-Liang
2001-06-01
We apply a genetic algorithm (GA) to the problem of electronically steering the maximums and nulls of an antenna array to desired positions (null toward enemy listener/jammer, max toward friendly listener/transmitter). The antenna pattern itself is computed using NEC2 which is called by the main GA program. Since a GA naturally lends itself to parallelization, this simulation was applied to our new twin 64-node cluster computers (Gemini). Design issues and uses of the Gemini cluster in our group are also discussed.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2013-03-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
NASA Astrophysics Data System (ADS)
Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei
2012-04-01
In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.
Human emotion detector based on genetic algorithm using lip features
NASA Astrophysics Data System (ADS)
Brown, Terrence; Fetanat, Gholamreza; Homaifar, Abdollah; Tsou, Brian; Mendoza-Schrock, Olga
2010-04-01
We predicted human emotion using a Genetic Algorithm (GA) based lip feature extractor from facial images to classify all seven universal emotions of fear, happiness, dislike, surprise, anger, sadness and neutrality. First, we isolated the mouth from the input images using special methods, such as Region of Interest (ROI) acquisition, grayscaling, histogram equalization, filtering, and edge detection. Next, the GA determined the optimal or near optimal ellipse parameters that circumvent and separate the mouth into upper and lower lips. The two ellipses then went through fitness calculation and were followed by training using a database of Japanese women's faces expressing all seven emotions. Finally, our proposed algorithm was tested using a published database consisting of emotions from several persons. The final results were then presented in confusion matrices. Our results showed an accuracy that varies from 20% to 60% for each of the seven emotions. The errors were mainly due to inaccuracies in the classification, and also due to the different expressions in the given emotion database. Detailed analysis of these errors pointed to the limitation of detecting emotion based on the lip features alone. Similar work [1] has been done in the literature for emotion detection in only one person, we have successfully extended our GA based solution to include several subjects.
Use of genetic algorithm for the selection of EEG features
NASA Astrophysics Data System (ADS)
Asvestas, P.; Korda, A.; Kostopoulos, S.; Karanasiou, I.; Ouzounoglou, A.; Sidiropoulos, K.; Ventouras, E.; Matsopoulos, G.
2015-09-01
Genetic Algorithm (GA) is a popular optimization technique that can detect the global optimum of a multivariable function containing several local optima. GA has been widely used in the field of biomedical informatics, especially in the context of designing decision support systems that classify biomedical signals or images into classes of interest. The aim of this paper is to present a methodology, based on GA, for the selection of the optimal subset of features that can be used for the efficient classification of Event Related Potentials (ERPs), which are recorded during the observation of correct or incorrect actions. In our experiment, ERP recordings were acquired from sixteen (16) healthy volunteers who observed correct or incorrect actions of other subjects. The brain electrical activity was recorded at 47 locations on the scalp. The GA was formulated as a combinatorial optimizer for the selection of the combination of electrodes that maximizes the performance of the Fuzzy C Means (FCM) classification algorithm. In particular, during the evolution of the GA, for each candidate combination of electrodes, the well-known (Σ, Φ, Ω) features were calculated and were evaluated by means of the FCM method. The proposed methodology provided a combination of 8 electrodes, with classification accuracy 93.8%. Thus, GA can be the basis for the selection of features that discriminate ERP recordings of observations of correct or incorrect actions.
South American foF2 database using genetic algorithms
NASA Astrophysics Data System (ADS)
Gularte, Erika; Bilitza, Dieter; Carpintero, Daniel; Jaen, Juliana
2016-07-01
We present the first step towards a new database of the ionospheric parameter foF2 for the South American region. The foF2 parameter, being the maximum of the ionospheric electronic density profile and its main sculptor, is of great interest not only in atmospheric studies but also in the realm of radio propagation. Due to its importance, its large variability and the difficulty to model it in time and space, it was the subject of an intense study since decades ago. The current databases, used by the IRI (International Reference Ionosphere) model, and based on Fourier expansions, has been built in the 60s from the available ionosondes at that time; therefore, it is still short of South American data. The main goal of this work is to upgrade the database, incorporating the now available data compiled by the RAPEAS (Red Argentina para el Estudio de la Atmósfera Superior, Argentine Network for the Study of the Upper Atmosphere) network. Also, we developed an algorithm to study the foF2 variability, based on the modern technique of genetic algorithms, which has been successfully applied on other disciplines. One of the main advantages of this technique is its ability in working with many variables and with unfavorable samples. The results are compared with the IRI databases, and improvements to the latter are suggested. Finally, it is important to notice that the new database is designed so that new available data can be easily incorporated.
Development of a genetic algorithm for molecular scale catalyst design
McLeod, A.S.; Gladden, L.F.; Johnston, M.E.
1997-04-01
A genetic algorithm has been developed to determine the optimal design of a two-component catalyst for the diffusion-limited A + B AB{up_arrow} reaction in which each species is adsorbed specifically on one of two types of sites. Optimization of the distribution of catalytic sites on the surface is achieved by means of an evolutionary algorithm which repeatedly selects the more active surfaces from a population of possible solutions leading to a gradual improvement in the activity of the catalyst surface. A Monte Carlo simulation is used to determine the activity of each of the catalyst surfaces. It is found that for a reacting mixture composed of equal amounts of each component the optimal active site distribution is that of a checkerboard, this solution being approximately 25% more active than a random site distribution. Study of a range of reactant compositions has shown the optimal distribution of catalytically active sites to be dependent on the composition of the ratio of A to B in the reacting mixture. The potential for application of the optimization method introduced here to other catalysts systems is discussed. 27 refs., 7 figs.
Genetic algorithm parameter optimization: applied to sensor coverage
NASA Astrophysics Data System (ADS)
Sahin, Ferat; Abbate, Giuseppe
2004-08-01
Genetic Algorithms are powerful tools, which when set upon a solution space will search for the optimal answer. These algorithms though have some associated problems, which are inherent to the method such as pre-mature convergence and lack of population diversity. These problems can be controlled with changes to certain parameters such as crossover, selection, and mutation. This paper attempts to tackle these problems in GA by having another GA controlling these parameters. The values for crossover parameter are: one point, two point, and uniform. The values for selection parameters are: best, worst, roulette wheel, inside 50%, outside 50%. The values for the mutation parameter are: random and swap. The system will include a control GA whose population will consist of different parameters settings. While this GA is attempting to find the best parameters it will be advancing into the search space of the problem and refining the population. As the population changes due to the search so will the optimal parameters. For every control GA generation each of the individuals in the population will be tested for fitness by being run through the problem GA with the assigned parameters. During these runs the population used in the next control generation is compiled. Thus, both the issue of finding the best parameters and the solution to the problem are attacked at the same time. The goal is to optimize the sensor coverage in a square field. The test case used was a 30 by 30 unit field with 100 sensor nodes. Each sensor node had a coverage area of 3 by 3 units. The algorithm attempts to optimize the sensor coverage in the field by moving the nodes. The results show that the control GA will provide better results when compared to a system with no parameter changes.
NASA Astrophysics Data System (ADS)
Xu, Dexiang
This dissertation presents a novel method of designing finite word length Finite Impulse Response (FIR) digital filters using a Real Parameter Parallel Genetic Algorithm (RPPGA). This algorithm is derived from basic Genetic Algorithms which are inspired by natural genetics principles. Both experimental results and theoretical studies in this work reveal that the RPPGA is a suitable method for determining the optimal or near optimal discrete coefficients of finite word length FIR digital filters. Performance of RPPGA is evaluated by comparing specifications of filters designed by other methods with filters designed by RPPGA. The parallel and spatial structures of the algorithm result in faster and more robust optimization than basic genetic algorithms. A filter designed by RPPGA is implemented in hardware to attenuate high frequency noise in a data acquisition system for collecting seismic signals. These studies may lead to more applications of the Real Parameter Parallel Genetic Algorithms in Electrical Engineering.
Optimal Robust Motion Controller Design Using Multiobjective Genetic Algorithm
Svečko, Rajko
2014-01-01
This paper describes the use of a multiobjective genetic algorithm for robust motion controller design. Motion controller structure is based on a disturbance observer in an RIC framework. The RIC approach is presented in the form with internal and external feedback loops, in which an internal disturbance rejection controller and an external performance controller must be synthesised. This paper involves novel objectives for robustness and performance assessments for such an approach. Objective functions for the robustness property of RIC are based on simple even polynomials with nonnegativity conditions. Regional pole placement method is presented with the aims of controllers' structures simplification and their additional arbitrary selection. Regional pole placement involves arbitrary selection of central polynomials for both loops, with additional admissible region of the optimized pole location. Polynomial deviation between selected and optimized polynomials is measured with derived performance objective functions. A multiobjective function is composed of different unrelated criteria such as robust stability, controllers' stability, and time-performance indexes of closed loops. The design of controllers and multiobjective optimization procedure involve a set of the objectives, which are optimized simultaneously with a genetic algorithm—differential evolution. PMID:24987749
Genetic Algorithms, Pulsar Planets, and Ionized Interstellar Microturbulence
NASA Astrophysics Data System (ADS)
Lazio, T. Joseph W.
1997-10-01
We probe the intense microturbulence in the Galactic center and the radio-wave scattering it generates by analyzing observations of extragalactic sources, OH and H2O masers, and free-free emission. The region responsible for the enhanced, anisotropic angular broadening of Sgr A* and nearby OH masers is within 150 pc of the Galactic center and has an angular radius ≈ 1o. The enhanced scattering probably occurs in the interface regions between 107 K gas and molecular clouds and is a manifestation of the energetic processes occurring in the Galactic center. Radio scattering measurements are also used to probe turbulent gas toward the Galactic anticenter. Ionized gas at Galactocentric distances ~50 kpc is suggested by absorption lines in quasar spectra, the appearance of the H I disks of nearby galaxies, and models for low-redshift quasar absorption systems and Galactic 'fountains.' We conducted multifrequency, Very Long Baseline Array (VLBA) observations on twelve extragalactic sources in order to measure their scattering sizes. Seven sources are at | b| < 1o and their lines of sight potentially probe path lengths ~>50 kpc through the disk. We find that the ionized disk is unwarped, has an extent of ≈20 kpc, and traces the extent of massive star formation in the outer Galaxy. Planetary companions to neutron stars are challenging to recognize amid the several processes that contribute to pulsar arrival time data. We use a genetic algorithm to search for planetary companions to pulsars. Genetic algorithms are an optimization method that uses biological-like concepts such as survival of the fittest, mutation, and chromosome exchange. The algorithm searches parameter space in the same way that life finds optimal niches in the biological environment-incremental rewarding of successful variations. Fitting for Keplerian orbits requires a search through four non-linear parameters per planet and is especially difficult if there is a large range of planetary masses and
Orbit design and estimation for surveillance missions using genetic algorithms
NASA Astrophysics Data System (ADS)
Abdelkhalik, Osama Mohamed Omar
2005-11-01
The problem of observing a given set of Earth target sites within an assigned time frame is examined. Attention is given mainly to visiting these sites as sub-satellite nadir points. Solutions to this problem in the literature require thrusters to continuously maneuver the satellite from one site to another. A natural solution is proposed. A natural solution is a gravitational orbit that enables the spacecraft to satisfy the mission requirements without maneuvering. Optimization of a penalty function is performed to find natural solutions for satellite orbit configurations. This penalty function depends on the mission objectives. Two mission objectives are considered: maximum observation time and maximum resolution. The penalty function poses multi minima and a genetic algorithm technique is used to solve this problem. In the case that there is no one orbit satisfying the mission requirements, a multi-orbit solution is proposed. In a multi-orbit solution, the set of target sites is split into two groups. Then the developed algorithm is used to search for a natural solution for each group. The satellite has to be maneuvered between the two solution orbits. Genetic algorithms are used to find the optimal orbit transfer between the two orbits using impulsive thrusters. A new formulation for solving the orbit maneuver problem using genetic algorithms is developed. The developed formulation searches for a minimum fuel consumption maneuver and guarantees that the satellite will be transferred exactly to the final orbit even if the solution is non-optimal. The results obtained demonstrate the feasibility of finding natural solutions for many case studies. The problem of the design of suitable satellite constellation for Earth observing applications is addressed. Two cases are considered. The first is the remote sensing missions for a particular region with high frequency and small swath width. The second is the interferometry radar Earth observation missions. In satellite
A scalable parallel algorithm for multiple objective linear programs
NASA Technical Reports Server (NTRS)
Wiecek, Malgorzata M.; Zhang, Hong
1994-01-01
This paper presents an ADBASE-based parallel algorithm for solving multiple objective linear programs (MOLP's). Job balance, speedup and scalability are of primary interest in evaluating efficiency of the new algorithm. Implementation results on Intel iPSC/2 and Paragon multiprocessors show that the algorithm significantly speeds up the process of solving MOLP's, which is understood as generating all or some efficient extreme points and unbounded efficient edges. The algorithm gives specially good results for large and very large problems. Motivation and justification for solving such large MOLP's are also included.
NASA Astrophysics Data System (ADS)
Biswas, S.; Goswami, S. K.
2010-10-01
In the present paper an attempt has been made to place the distributed generation at an optimal location so as to improve the technical as well as economical performance. Among technical issues the sag performance and the loss have been considered. Genetic algorithm method has been used as the optimization technique in this problem. For sag analysis the impact of 3-phase symmetrical short circuit faults is considered. Total load disturbed during the faults is considered as an indicator of sag performance. The solution algorithm is demonstrated on a 34 bus radial distribution system with some lateral branches. For simplicity only one DG of predefined capacity is considered. MATLAB has been used as the programming environment.
Duplication of coding segments in genetic programming
Haynes, T.
1996-12-31
Research into the utility of non-coding segments, or introns, in genetic-based encodings has shown that they expedite the evolution of solutions in domains by protecting building blocks against destructive crossover. We consider a genetic programming system where non-coding segments can be removed, and the resultant chromosomes returned into the population. This parsimonious repair leads to premature convergence, since as we remove the naturally occurring non-coding segments, we strip away their protective backup feature. We then duplicate the coding segments in the repaired chromosomes, and place the modified chromosomes into the population. The duplication method significantly improves the learning rate in the domain we have considered. We also show that this method can be applied to other domains.
Dynamic programming algorithm for detecting dim infrared moving targets
NASA Astrophysics Data System (ADS)
He, Lisha; Mao, Liangjing; Xie, Lijun
2009-10-01
Infrared (IR) target detection is a key part of airborne infrared weapon system, especially the detection of poor dim moving IR target embedded in complex context. This paper presents an improved Dynamic Programming (DP) algorithm in allusion to low Signal to Noise Ratio (SNR) infrared dim moving targets under cluttered context. The algorithm brings the dim target to prominence by accumulating the energy of pixels in the image sequence, after suppressing the background noise with a mathematical morphology preprocessor. As considering the continuity and stabilization of target's energy and forward direction, this algorithm has well solved the energy scattering problem that exists in the original DP algorithm. An effective energy segmentation threshold is given by a Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter with a regional peak extraction algorithm. Simulation results show that the improved DP tracking algorithm performs well in detecting poor dim targets.
New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
User-Based Document Clustering by Redescribing Subject Descriptions with a Genetic Algorithm.
ERIC Educational Resources Information Center
Gordon, Michael D.
1991-01-01
Discussion of clustering of documents and queries in information retrieval systems focuses on the use of a genetic algorithm to adapt subject descriptions so that documents become more effective in matching relevant queries. Various types of clustering are explained, and simulation experiments used to test the genetic algorithm are described. (27…
Order-Based Fitness Functions for Genetic Algorithms Applied to Relevance Feedback.
ERIC Educational Resources Information Center
Lopez-Pujalte, Cristina; Guerrero-Bote, Vicente P.; de Moya-Anegon, Felix
2003-01-01
Discusses genetic algorithms in information retrieval, especially for relevance feedback, and evaluates the efficacy of a genetic algorithm with various order-based fitness functions for relevance feedback in a test database. Compares results with the Ide dec-hi method, one of the best traditional methods. (Contains 56 references.) (Author/LRW)
On the use of genetic programming for mining comprehensible rules in subgroup discovery.
Luna, José María; Romero, José Raúl; Romero, Cristóbal; Ventura, Sebastián
2014-12-01
This paper proposes a novel grammar-guided genetic programming algorithm for subgroup discovery. This algorithm, called comprehensible grammar-based algorithm for subgroup discovery (CGBA-SD), combines the requirements of discovering comprehensible rules with the ability to mine expressive and flexible solutions owing to the use of a context-free grammar. Each rule is represented as a derivation tree that shows a solution described using the language denoted by the grammar. The algorithm includes mechanisms to adapt the diversity of the population by self-adapting the probabilities of recombination and mutation. We compare the approach with existing evolutionary and classic subgroup discovery algorithms. CGBA-SD appears to be a very promising algorithm that discovers comprehensible subgroups and behaves better than other algorithms as measures by complexity, interest, and precision indicate. The results obtained were validated by means of a series of nonparametric tests.
SPLICER - A GENETIC ALGORITHM TOOL FOR SEARCH AND OPTIMIZATION, VERSION 1.0 (MACINTOSH VERSION)
NASA Technical Reports Server (NTRS)
Wang, L.
1994-01-01
SPLICER is a genetic algorithm tool which can be used to solve search and optimization problems. Genetic algorithms are adaptive search procedures (i.e. problem solving methods) based loosely on the processes of natural selection and Darwinian "survival of the fittest." SPLICER provides the underlying framework and structure for building a genetic algorithm application. These algorithms apply genetically-inspired operators to populations of potential solutions in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. SPLICER 1.0 was created using a modular architecture that includes a Genetic Algorithm Kernel, interchangeable Representation Libraries, Fitness Modules and User Interface Libraries, and well-defined interfaces between these components. The architecture supports portability, flexibility, and extensibility. SPLICER comes with all source code and several examples. For instance, a "traveling salesperson" example searches for the minimum distance through a number of cities visiting each city only once. Stand-alone SPLICER applications can be used without any programming knowledge. However, to fully utilize SPLICER within new problem domains, familiarity with C language programming is essential. SPLICER's genetic algorithm (GA) kernel was developed independent of representation (i.e. problem encoding), fitness function or user interface type. The GA kernel comprises all functions necessary for the manipulation of populations. These functions include the creation of populations and population members, the iterative population model, fitness scaling, parent selection and sampling, and the generation of population statistics. In addition, miscellaneous functions are included in the kernel (e.g., random number generators). Different problem-encoding schemes and functions are defined and stored in interchangeable representation libraries. This allows the GA kernel to be used with any
A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
NASA Astrophysics Data System (ADS)
Thammano, Arit; Teekeng, Wannaporn
2015-05-01
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
Functional Localization of Genetic Network Programming
NASA Astrophysics Data System (ADS)
Eto, Shinji; Hirasawa, Kotaro; Hu, Jinglu
According to the knowledge of brain science, it is suggested that there exists cerebral functional localization, which means that a specific part of the cerebrum is activated depending on various kinds of information human receives. The aim of this paper is to build an artificial model to realize functional localization based on Genetic Network Programming (GNP), a new evolutionary computation method recently developed. GNP has a directed graph structure suitable for realizing functional localization. We studied the basic characteristics of the proposed system by making GNP work in a functionally localized way.
Modelling and genetic algorithm based optimisation of inverse supply chain
NASA Astrophysics Data System (ADS)
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
Genetic algorithms applied to nonlinear and complex domains
Barash, D; Woodin, A E
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a final result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Genetic algorithms applied to nonlinear and complex domains
Barash, D; Woodin, A E
1999-06-01
The dissertation, titled ''Genetic Algorithms Applied to Nonlinear and Complex Domains'', describes and then applies a new class of powerful search algorithms (GAS) to certain domains. GAS are capable of solving complex and nonlinear problems where many parameters interact to produce a ''final'' result such as the optimization of the laser pulse in the interaction of an atom with an intense laser field. GAS can very efficiently locate the global maximum by searching parameter space in problems which are unsuitable for a search using traditional methods. In particular, the dissertation contains new scientific findings in two areas. First, the dissertation examines the interaction of an ultra-intense short laser pulse with atoms. GAS are used to find the optimal frequency for stabilizing atoms in the ionization process. This leads to a new theoretical formulation, to explain what is happening during the ionization process and how the electron is responding to finite (real-life) laser pulse shapes. It is shown that the dynamics of the process can be very sensitive to the ramp of the pulse at high frequencies. The new theory which is formulated, also uses a novel concept (known as the (t,t') method) to numerically solve the time-dependent Schrodinger equation Second, the dissertation also examines the use of GAS in modeling decision making problems. It compares GAS with traditional techniques to solve a class of problems known as Markov Decision Processes. The conclusion of the dissertation should give a clear idea of where GAS are applicable, especially in the physical sciences, in problems which are nonlinear and complex, i.e. difficult to analyze by other means.
Melanoma Prognostic Model Using Tissue Microarrays and Genetic Algorithms
Gould Rothberg, Bonnie E.; Berger, Aaron J.; Molinaro, Annette M.; Subtil, Antonio; Krauthammer, Michael O.; Camp, Robert L.; Bradley, William R.; Ariyan, Stephan; Kluger, Harriet M.; Rimm, David L.
2009-01-01
Purpose As a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence. Methods Protein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004. Results Multiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than –0.052, p21WAF1 nuclear compartment AQUA score of more than 12.98, p16INK4A ln(non-nuclear/nuclear AQUA score ratio) of ≤ −0.083, β-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of ≤ 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts. Conclusion This multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy. PMID:19884546
Accurate construction of consensus genetic maps via integer linear programming.
Wu, Yonghui; Close, Timothy J; Lonardi, Stefano
2011-01-01
We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or, the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts, the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at http://www.cs.ucr.edu/~yonghui/mgmap.html. PMID:20479505
Accurate construction of consensus genetic maps via integer linear programming.
Wu, Yonghui; Close, Timothy J; Lonardi, Stefano
2011-01-01
We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or, the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts, the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at http://www.cs.ucr.edu/~yonghui/mgmap.html.
Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2001-01-01
A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.
NASA Astrophysics Data System (ADS)
Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie
2015-12-01
The serious information redundancy in hyperspectral images (HIs) cannot contribute to the data analysis accuracy, instead it require expensive computational resources. Consequently, to identify the most useful and valuable information from the HIs, thereby improve the accuracy of data analysis, this paper proposed a novel hyperspectral band selection method using the hybrid genetic algorithm and gravitational search algorithm (GA-GSA). In the proposed method, the GA-GSA is mapped to the binary space at first. Then, the accuracy of the support vector machine (SVM) classifier and the number of selected spectral bands are utilized to measure the discriminative capability of the band subset. Finally, the band subset with the smallest number of spectral bands as well as covers the most useful and valuable information is obtained. To verify the effectiveness of the proposed method, studies conducted on an AVIRIS image against two recently proposed state-of-the-art GSA variants are presented. The experimental results revealed the superiority of the proposed method and indicated that the method can indeed considerably reduce data storage costs and efficiently identify the band subset with stable and high classification precision.
Grant, C W; Lenderman, J S; Gansemer, J D
2011-02-24
This document is an update to the 'ADIS Algorithm Evaluation Project Plan' specified in the Statement of Work for the US-VISIT Identity Matching Algorithm Evaluation Program, as deliverable II.D.1. The original plan was delivered in August 2010. This document modifies the plan to reflect modified deliverables reflecting delays in obtaining a database refresh. This document describes the revised schedule of the program deliverables. The detailed description of the processes used, the statistical analysis processes and the results of the statistical analysis will be described fully in the program deliverables. The US-VISIT Identity Matching Algorithm Evaluation Program is work performed by Lawrence Livermore National Laboratory (LLNL) under IAA HSHQVT-07-X-00002 P00004 from the Department of Homeland Security (DHS).
Binocular self-calibration performed via adaptive genetic algorithm based on laser line imaging
NASA Astrophysics Data System (ADS)
Apolinar Muñoz Rodríguez, J.; Mejía Alanís, Francisco Carlos
2016-07-01
An accurate technique to perform binocular self-calibration by means of an adaptive genetic algorithm based on a laser line is presented. In this calibration, the genetic algorithm computes the vision parameters through simulated binary crossover (SBX). To carry it out, the genetic algorithm constructs an objective function from the binocular geometry of the laser line projection. Then, the SBX minimizes the objective function via chromosomes recombination. In this algorithm, the adaptive procedure determines the search space via line position to obtain the minimum convergence. Thus, the chromosomes of vision parameters provide the minimization. The approach of the proposed adaptive genetic algorithm is to calibrate and recalibrate the binocular setup without references and physical measurements. This procedure leads to improve the traditional genetic algorithms, which calibrate the vision parameters by means of references and an unknown search space. It is because the proposed adaptive algorithm avoids errors produced by the missing of references. Additionally, the three-dimensional vision is carried out based on the laser line position and vision parameters. The contribution of the proposed algorithm is corroborated by an evaluation of accuracy of binocular calibration, which is performed via traditional genetic algorithms.
Genetic algorithm optimized triply compensated pulses in NMR spectroscopy.
Manu, V S; Veglia, Gianluigi
2015-11-01
Sensitivity and resolution in NMR experiments are affected by magnetic field inhomogeneities (of both external and RF), errors in pulse calibration, and offset effects due to finite length of RF pulses. To remedy these problems, built-in compensation mechanisms for these experimental imperfections are often necessary. Here, we propose a new family of phase-modulated constant-amplitude broadband pulses with high compensation for RF inhomogeneity and heteronuclear coupling evolution. These pulses were optimized using a genetic algorithm (GA), which consists in a global optimization method inspired by Nature's evolutionary processes. The newly designed π and π/2 pulses belong to the 'type A' (or general rotors) symmetric composite pulses. These GA-optimized pulses are relatively short compared to other general rotors and can be used for excitation and inversion, as well as refocusing pulses in spin-echo experiments. The performance of the GA-optimized pulses was assessed in Magic Angle Spinning (MAS) solid-state NMR experiments using a crystalline U-(13)C, (15)N NAVL peptide as well as U-(13)C, (15)N microcrystalline ubiquitin. GA optimization of NMR pulse sequences opens a window for improving current experiments and designing new robust pulse sequences.
Optimization of a Genetic Algorithm for the Functionalization of Fullerenes.
Addicoat, Matthew A; Page, Alister J; Brain, Zoe E; Flack, Lloyd; Morokuma, Keiji; Irle, Stephan
2012-05-01
We present the optimization of a genetic algorithm (GA) that is designed to predict the most stable structural isomers of hydrogenated and hydroxylated fullerene cages. Density functional theory (DFT) and density functional tight binding (DFTB) methods are both employed to compute isomer energies. We show that DFTB and DFT levels of theory are in good agreement with each other and that therefore both sets of optimized GA parameters are very similar. As a prototypical fullerene cage, we consider the functionalization of the C20 species, since for this smallest possible fullerene cage it is possible to compute all possible isomer energies for evaluation of the GA performance. An energy decomposition analysis for both C20Hn and C20(OH)n systems reveals that, for only few functional groups, the relative stabilities of different structural isomers may be rationalized simply with recourse to π-Hückel theory. However, upon a greater degree of functionalization, π-electronic effects alone are incapable of describing the interaction between the functional groups and the distorted cage, and both σ- and π-electronic structure must be taken into account in order to understand the relative isomer stabilities.
Prediction of plasma processes using neural network and genetic algorithm
NASA Astrophysics Data System (ADS)
Kim, Byungwhan; Bae, Jungki
2005-10-01
Using genetic algorithm (GA) and backpropagation neural network (BPNN), computer models of plasma processes were constructed. The GA was applied to optimize five training factors simultaneously. The presented technique was evaluated with plasma etch data, characterized by a statistical experimental design. The etching was conducted in an inductively coupled plasma etch system. The etch outputs to model include aluminum (Al) etch rate, Al selectivity, silica profile angle, and DC bias. GA-BPNN models demonstrated improved predictions of more than 20% for all etch outputs but the DC bias. This indicates that a simultaneous optimization of training factors is more effective in improving the prediction performance of BPNN model than a sequential optimization of individual training factor. Compared to GA-BPNN models constructed in a previous training set, the presented models also yielded a much improved prediction of more than 35% for all etch outputs. The proven improvement indicates that the presented training set is more effective to improve GA-BPNN models.
Improvement of unsupervised texture classification based on genetic algorithms
NASA Astrophysics Data System (ADS)
Okumura, Hiroshi; Togami, Yuuki; Arai, Kohei
2004-11-01
At the previous conference, the authors are proposed a new unsupervised texture classification method based on the genetic algorithms (GA). In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: 1) the determination of the number of classification category; 2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; 3) 50 chromosomes are generated using random number; 4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); 5) in the selection operation in the GA, the elite preservation strategy is employed; 6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; 7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; 8) go to the procedure 4. However, this method has not been automated because it requires not only target image but also the number of categories for classification. In this paper, we describe some improvement for implementation of automated texture classification. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.
A Moving Target Environment for Computer Configurations Using Genetic Algorithms
Crouse, Michael; Fulp, Errin W.
2011-10-31
Moving Target (MT) environments for computer systems provide security through diversity by changing various system properties that are explicitly defined in the computer configuration. Temporal diversity can be achieved by making periodic configuration changes; however in an infrastructure of multiple similarly purposed computers diversity must also be spatial, ensuring multiple computers do not simultaneously share the same configuration and potential vulnerabilities. Given the number of possible changes and their potential interdependencies discovering computer configurations that are secure, functional, and diverse is challenging. This paper describes how a Genetic Algorithm (GA) can be employed to find temporally and spatially diverse secure computer configurations. In the proposed approach a computer configuration is modeled as a chromosome, where an individual configuration setting is a trait or allele. The GA operates by combining multiple chromosomes (configurations) which are tested for feasibility and ranked based on performance which will be measured as resistance to attack. The result of successive iterations of the GA are secure configurations that are diverse due to the crossover and mutation processes. Simulations results will demonstrate this approach can provide at MT environment for a large infrastructure of similarly purposed computers by discovering temporally and spatially diverse secure configurations.
Shape: automatic conformation prediction of carbohydrates using a genetic algorithm
2009-01-01
Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses. PMID:20298520
Optimizing SRF Gun Cavity Profiles in a Genetic Algorithm Framework
Alicia Hofler, Pavel Evtushenko, Frank Marhauser
2009-09-01
Automation of DC photoinjector designs using a genetic algorithm (GA) based optimization is an accepted practice in accelerator physics. Allowing the gun cavity field profile shape to be varied can extend the utility of this optimization methodology to superconducting and normal conducting radio frequency (SRF/RF) gun based injectors. Finding optimal field and cavity geometry configurations can provide guidance for cavity design choices and verify existing designs. We have considered two approaches for varying the electric field profile. The first is to determine the optimal field profile shape that should be used independent of the cavity geometry, and the other is to vary the geometry of the gun cavity structure to produce an optimal field profile. The first method can provide a theoretical optimal and can illuminate where possible gains can be made in field shaping. The second method can produce more realistically achievable designs that can be compared to existing designs. In this paper, we discuss the design and implementation for these two methods for generating field profiles for SRF/RF guns in a GA based injector optimization scheme and provide preliminary results.
Improved interpretation of satellite altimeter data using genetic algorithms
NASA Technical Reports Server (NTRS)
Messa, Kenneth; Lybanon, Matthew
1992-01-01
Genetic algorithms (GA) are optimization techniques that are based on the mechanics of evolution and natural selection. They take advantage of the power of cumulative selection, in which successive incremental improvements in a solution structure become the basis for continued development. A GA is an iterative procedure that maintains a 'population' of 'organisms' (candidate solutions). Through successive 'generations' (iterations) the population as a whole improves in simulation of Darwin's 'survival of the fittest'. GA's have been shown to be successful where noise significantly reduces the ability of other search techniques to work effectively. Satellite altimetry provides useful information about oceanographic phenomena. It provides rapid global coverage of the oceans and is not as severely hampered by cloud cover as infrared imagery. Despite these and other benefits, several factors lead to significant difficulty in interpretation. The GA approach to the improved interpretation of satellite data involves the representation of the ocean surface model as a string of parameters or coefficients from the model. The GA searches in parallel, a population of such representations (organisms) to obtain the individual that is best suited to 'survive', that is, the fittest as measured with respect to some 'fitness' function. The fittest organism is the one that best represents the ocean surface model with respect to the altimeter data.
Toward Developing Genetic Algorithms to Aid in Critical Infrastructure Modeling
Not Available
2007-05-01
Today’s society relies upon an array of complex national and international infrastructure networks such as transportation, telecommunication, financial and energy. Understanding these interdependencies is necessary in order to protect our critical infrastructure. The Critical Infrastructure Modeling System, CIMS©, examines the interrelationships between infrastructure networks. CIMS© development is sponsored by the National Security Division at the Idaho National Laboratory (INL) in its ongoing mission for providing critical infrastructure protection and preparedness. A genetic algorithm (GA) is an optimization technique based on Darwin’s theory of evolution. A GA can be coupled with CIMS© to search for optimum ways to protect infrastructure assets. This includes identifying optimum assets to enforce or protect, testing the addition of or change to infrastructure before implementation, or finding the optimum response to an emergency for response planning. This paper describes the addition of a GA to infrastructure modeling for infrastructure planning. It first introduces the CIMS© infrastructure modeling software used as the modeling engine to support the GA. Next, the GA techniques and parameters are defined. Then a test scenario illustrates the integration with CIMS© and the preliminary results.
Optimal vaccination schedule search using genetic algorithm over MPI technology
2012-01-01
Background Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented. PMID:23148787
Optimization on robot arm machining by using genetic algorithms
NASA Astrophysics Data System (ADS)
Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En
2007-12-01
In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.
The Adaptive Analysis of Visual Cognition using Genetic Algorithms
Cook, Robert G.; Qadri, Muhammad A. J.
2014-01-01
Two experiments used a novel, open-ended, and adaptive test procedure to examine visual cognition in animals. Using a genetic algorithm, a pigeon was tested repeatedly from a variety of different initial conditions for its solution to an intermediate brightness search task. On each trial, the animal had to accurately locate and peck a target element of intermediate brightness from among a variable number of surrounding darker and lighter distractor elements. Displays were generated from six parametric variables, or genes (distractor number, element size, shape, spacing, target brightness, distractor brightness). Display composition changed over time, or evolved, as a function of the bird’s differential accuracy within the population of values for each gene. Testing three randomized initial conditions and one set of controlled initial conditions, element size and number of distractors were identified as the most important factors controlling search accuracy, with distractor brightness, element shape, and spacing making secondary contributions. The resulting changes in this multidimensional stimulus space suggested the existence of a set of conditions that the bird repeatedly converged upon regardless of initial conditions. This psychological “attractor” represents the cumulative action of the cognitive operations used by the pigeon in solving and performing this search task. The results are discussed regarding their implications for visual cognition in pigeons and the usefulness of adaptive, subject-driven experimentation for investigating human and animal cognition more generally. PMID:24000905
Reducing aerodynamic vibration with piezoelectric actuators: a genetic algorithm optimization
NASA Astrophysics Data System (ADS)
Hu, Zhenning; Jakiela, Mark; Pitt, Dale M.; Burnham, Jay K.
2004-07-01
Modern high performance aircraft fly at high speeds and high angles of attack. This can result in "buffet" aerodynamics, an unsteady turbulent flow that causes vibrations of the wings, tails, and body of the aircraft. This can result in decreased performance and ride quality, and fatigue failures. We are experimenting with controlling these vibrations by using piezoceramic actuators attached to the inner and outer skin of the aircraft. In this project, a tail or wing is investigated. A "generic" tail finite element model is studied in which individual actuators are assumed to exactly cover individual finite elements. Various optimizations of the orientations and power consumed by these actuators are then performed. Real coded genetic algorithms are used to perform the optimizations and a design space approximation technique is used to minimize costly finite element runs. An important result is the identification of a power consumption threshold for the entire system. Below the threshold, vibration control performance of optimized systems decreases with decreasing values of power supplied to the entire system.
Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics
Hofler, Alicia; Terzic, Balsa; Kramer, Matthew; Zvezdin, Anton; Morozov, Vasiliy; Roblin, Yves; Lin, Fanglei; Jarvis, Colin
2013-01-01
The genetic algorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.
Constrained genetic algorithms for optimizing multi-use reservoir operation
NASA Astrophysics Data System (ADS)
Chang, Li-Chiu; Chang, Fi-John; Wang, Kuo-Wei; Dai, Shin-Yi
2010-08-01
To derive an optimal strategy for reservoir operations to assist the decision-making process, we propose a methodology that incorporates the constrained genetic algorithm (CGA) where the ecological base flow requirements are considered as constraints to water release of reservoir operation when optimizing the 10-day reservoir storage. Furthermore, a number of penalty functions designed for different types of constraints are integrated into reservoir operational objectives to form the fitness function. To validate the applicability of this proposed methodology for reservoir operations, the Shih-Men Reservoir and its downstream water demands are used as a case study. By implementing the proposed CGA in optimizing the operational performance of the Shih-Men Reservoir for the last 20 years, we find this method provides much better performance in terms of a small generalized shortage index (GSI) for human water demands and greater ecological base flows for most of the years than historical operations do. We demonstrate the CGA approach can significantly improve the efficiency and effectiveness of water supply capability to both human and ecological base flow requirements and thus optimize reservoir operations for multiple water users. The CGA can be a powerful tool in searching for the optimal strategy for multi-use reservoir operations in water resources management.
Development of hybrid genetic algorithms for product line designs.
Balakrishnan, P V Sundar; Gupta, Rakesh; Jacob, Varghese S
2004-02-01
In this paper, we investigate the efficacy of artificial intelligence (AI) based meta-heuristic techniques namely genetic algorithms (GAs), for the product line design problem. This work extends previously developed methods for the single product design problem. We conduct a large scale simulation study to determine the effectiveness of such an AI based technique for providing good solutions and bench mark the performance of this against the current dominant approach of beam search (BS). We investigate the potential advantages of pursuing the avenue of developing hybrid models and then implement and study such hybrid models using two very distinct approaches: namely, seeding the initial GA population with the BS solution, and employing the BS solution as part of the GA operator's process. We go on to examine the impact of two alternate string representation formats on the quality of the solutions obtained by the above proposed techniques. We also explicitly investigate a critical managerial factor of attribute importance in terms of its impact on the solutions obtained by the alternate modeling procedures. The alternate techniques are then evaluated, using statistical analysis of variance, on a fairy large number of data sets, as to the quality of the solutions obtained with respect to the state-of-the-art benchmark and in terms of their ability to provide multiple, unique product line options.
Image segmentation with genetic algorithms: a formulation and implementation
NASA Astrophysics Data System (ADS)
Seetharaman, Gunasekaran; Narasimhan, Amruthur; Sathe, Anand; Storc, Lisa
1991-10-01
Image segmentation is an important step in any computer vision system. Segmentation refers to the partitioning of the image plane into several regions, such that each region corresponds to a logical entity present in the scene. The problem is inherently NP, and the theory on the existence and uniqueness of the ideal segmentation is not yet established. Several methods have been proposed in literature for image segmentation. With the exception of the state-space approach to segmentation, other methods lack generality. The state-space approach, however, amounts to searching for the solution in a large search space of 22n(2) possibilities for a n X n image. In this paper, a classic approach based on state-space techniques for segmentation due to Brice and Fennema is reformulated using genetic algorithms. The state space representation of a partially segmented image lends itself to binary strings, in which the dominant substrings are easily explained in terms of chromosomes. Also the operations such as crossover and mutations are easily abstracted. In particular, when multiple images are segmented from an image sequence, fusion of constraints from one to the other becomes clear under this formulation.
Improved satellite image compression and reconstruction via genetic algorithms
NASA Astrophysics Data System (ADS)
Babb, Brendan; Moore, Frank; Peterson, Michael; Lamont, Gary
2008-10-01
A wide variety of signal and image processing applications, including the US Federal Bureau of Investigation's fingerprint compression standard [3] and the JPEG-2000 image compression standard [26], utilize wavelets. This paper describes new research that demonstrates how a genetic algorithm (GA) may be used to evolve transforms that outperform wavelets for satellite image compression and reconstruction under conditions subject to quantization error. The new approach builds upon prior work by simultaneously evolving real-valued coefficients representing matched forward and inverse transform pairs at each of three levels of a multi-resolution analysis (MRA) transform. The training data for this investigation consists of actual satellite photographs of strategic urban areas. Test results show that a dramatic reduction in the error present in reconstructed satellite images may be achieved without sacrificing the compression capabilities of the forward transform. The transforms evolved during this research outperform previous start-of-the-art solutions, which optimized coefficients for the reconstruction transform only. These transforms also outperform wavelets, reducing error by more than 0.76 dB at a quantization level of 64. In addition, transforms trained using representative satellite images do not perform quite as well when subsequently tested against images from other classes (such as fingerprints or portraits). This result suggests that the GA developed for this research is automatically learning to exploit specific attributes common to the class of images represented in the training population.
Inner Random Restart Genetic Algorithm for Practical Delivery Schedule Optimization
NASA Astrophysics Data System (ADS)
Sakurai, Yoshitaka; Takada, Kouhei; Onoyama, Takashi; Tsukamoto, Natsuki; Tsuruta, Setsuo
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires solving several tens to hundreds but less than 2 thousands cities Traveling Salesman Problems (TSP) within interactive response time (less than about 3 second), with expert-level accuracy (less than about 3% of error rate). Further, to make things more difficult, the optimization is subjects to special requirements or preferences of each various delivery sites, persons, or societies. To meet these requirements, an Inner Random Restart Genetic Algorithm (Irr-GA) is proposed and developed. This method combines meta-heuristics such as random restart and GA having different types of simple heuristics. Such simple heuristics are 2-opt and NI (Nearest Insertion) methods, each applied for gene operations. The proposed method is hierarchical structured, integrating meta-heuristics and heuristics both of which are multiple but simple. This method is elaborated so that field experts as well as field engineers can easily understand to make the solution or method easily customized and extended according to customers' needs or taste. Comparison based on the experimental results and consideration proved that the method meets the above requirements more than other methods judging from not only optimality but also simplicity, flexibility, and expandability in order for this method to be practically used.
Research on trust-region algorithms for nonlinear programming
Dennis, J.E.; Tapia, R.A.
1991-11-01
This report discusses research on the following topics: interior- point methods for linear programming; trust-region SQP newton's method for general nonlinear programming problems; trust-region SQP newton's method for large sparse nonlinear programming problems with applications to oil reservoir management; a unified approach to global convergence of trust-region methods for nonsmooth optimization; and SQP augmented lagrangian BRGS algorithm for constrained optimization. (LSP).
Predicting Student Grades in Learning Management Systems with Multiple Instance Genetic Programming
ERIC Educational Resources Information Center
Zafra, Amelia; Ventura, Sebastian
2009-01-01
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Genetic programs constructed from layered logic gates in single cells
Moon, Tae Seok; Lou, Chunbo; Tamsir, Alvin; Stanton, Brynne C.; Voigt, Christopher A.
2014-01-01
Genetic programs function to integrate environmental sensors, implement signal processing algorithms and control expression dynamics1. These programs consist of integrated genetic circuits that individually implement operations ranging from digital logic to dynamic circuits2–6, and they have been used in various cellular engineering applications, including the implementation of process control in metabolic networks and the coordination of spatial differentiation in artificial tissues. A key limitation is that the circuits are based on biochemical interactions occurring in the confined volume of the cell, so the size of programs has been limited to a few circuits1,7. Here we apply part mining and directed evolution to build a set of transcriptional AND gates in Escherichia coli. Each AND gate integrates two promoter inputs and controls one promoter output. This allows the gates to be layered by having the output promoter of an upstream circuit serve as the input promoter for a downstream circuit. Each gate consists of a transcription factor that requires a second chaperone protein to activate the output promoter. Multiple activator–chaperone pairs are identified from type III secretion pathways in different strains of bacteria. Directed evolution is applied to increase the dynamic range and orthogonality of the circuits. These gates are connected in different permutations to form programs, the largest of which is a 4-input AND gate that consists of 3 circuits that integrate 4 inducible systems, thus requiring 11 regulatory proteins. Measuring the performance of individual gates is sufficient to capture the behaviour of the complete program. Errors in the output due to delays (faults), a common problem for layered circuits, are not observed. This work demonstrates the successful layering of orthogonal logic gates, a design strategy that could enable the construction of large, integrated circuits in single cells. PMID:23041931
Suspended sediment modeling using genetic programming and soft computing techniques
NASA Astrophysics Data System (ADS)
Kisi, Ozgur; Dailr, Ali Hosseinzadeh; Cimen, Mesut; Shiri, Jalal
2012-07-01
SummaryModeling suspended sediment load is an important factor in water resources engineering as it crucially affects the design and management of water resources structures. In this study the genetic programming (GP) technique was applied for estimating the daily suspended sediment load in two stations in Cumberland River in U.S. Daily flow and sediment data from 1972 to 1989 were used to train and test the applied genetic programming models. The effect of various GP operators on sediment load estimation was investigated. The optimal fitness function, operator functions, linking function and learning algorithm were obtained for modeling daily suspended sediment. The GP estimates were compared with those of the Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) results, in term of coefficient of determination, mean absolute error, coefficient of residual mass and variance accounted for. The comparison results indicated that the GP is superior to the ANFIS, ANN and SVM models in estimating daily suspended sediment load.
A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms
Kanwal, Maxinder S; Ramesh, Avinash S; Huang, Lauren A
2013-01-01
Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates. PMID:24627784
Guo, Wensheng; Yang, Guowu; Wu, Wei; He, Lei; Sun, Mingyu
2014-01-01
In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.
NASA Astrophysics Data System (ADS)
Smith, Matthew R.; Kuo, Fang-An; Hsieh, Chih-Wei; Yu, Jen-Perng; Wu, Jong-Shinn; Ferguson, Alex
2010-06-01
Presented is a rapid calculation tool for the optimization of blast wave related mitigation strategies. The motion of gas resulting from a blast wave (specified by the user) is solved by the Quiet Direct Simulation (QDS) method - a rapid kinetic theory-based finite volume method. The optimization routine employed is a newly developed Genetic Algorithm (GA) which is demonstrated to be similar to a Differential Evolution (DE) scheme with several modifications. In any Genetic Algorithm, individuals contain genetic information which is passed on to newly created individuals in successive generations. The results from unsteady QDS simulations are used to determine the individual's "genetic fitness" which is employed by the proposed Genetic Algorithm during the reproduction process. The combined QDS/GA algorithm is applied to various test cases and finally the optimization of a non-trivial blast wave mitigation strategy. Both QDS and the proposed GA are demonstrated to perform with minimal computational expense while accurately solving the optimization problems presented.
In-Space Radiator Shape Optimization using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Hull, Patrick V.; Kittredge, Ken; Tinker, Michael; SanSoucie, Michael
2006-01-01
Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in
Discovering Knowledge from Noisy Databases Using Genetic Programming.
ERIC Educational Resources Information Center
Wong, Man Leung; Leung, Kwong Sak; Cheng, Jack C. Y.
2000-01-01
Presents a framework that combines Genetic Programming and Inductive Logic Programming, two approaches in data mining, to induce knowledge from noisy databases. The framework is based on a formalism of logic grammars and is implemented as a data mining system called LOGENPRO (Logic Grammar-based Genetic Programming System). (Contains 34…
Evaluation of a statewide program in genetic diseases.
Mitchell, J A; Petroski, G
1998-07-01
We used the Genetics Office Automation System (GOAS), a database management system designed to facilitate collection and analysis of medical genetic data, to evaluate the Missouri Genetics Disease Program (MGDP). From 1985 through 1995, patient data were collected at four tertiary care genetic centers. The number of genetic visits per 100,000 people more than doubled from 1985 through 1995. The results of subpopulation analyses indicate that the MGDP has facilitated improvements in: (1) services for newborns and infants, (2) rural outreach programs, and (3) evaluation of the incidence and impact of genetic disorders. PMID:9677054
Design optimization of space launch vehicles using a genetic algorithm
NASA Astrophysics Data System (ADS)
Bayley, Douglas James
The United States Air Force (USAF) continues to have a need for assured access to space. In addition to flexible and responsive spacelift, a reduction in the cost per launch of space launch vehicles is also desirable. For this purpose, an investigation of the design optimization of space launch vehicles has been conducted. Using a suite of custom codes, the performance aspects of an entire space launch vehicle were analyzed. A genetic algorithm (GA) was employed to optimize the design of the space launch vehicle. A cost model was incorporated into the optimization process with the goal of minimizing the overall vehicle cost. The other goals of the design optimization included obtaining the proper altitude and velocity to achieve a low-Earth orbit. Specific mission parameters that are particular to USAF space endeavors were specified at the start of the design optimization process. Solid propellant motors, liquid fueled rockets, and air-launched systems in various configurations provided the propulsion systems for two, three and four-stage launch vehicles. Mass properties models, an aerodynamics model, and a six-degree-of-freedom (6DOF) flight dynamics simulator were all used to model the system. The results show the feasibility of this method in designing launch vehicles that meet mission requirements. Comparisons to existing real world systems provide the validation for the physical system models. However, the ability to obtain a truly minimized cost was elusive. The cost model uses an industry standard approach, however, validation of this portion of the model was challenging due to the proprietary nature of cost figures and due to the dependence of many existing systems on surplus hardware.
A weight based genetic algorithm for selecting views
NASA Astrophysics Data System (ADS)
Talebian, Seyed H.; Kareem, Sameem A.
2013-03-01
Data warehouse is a technology designed for supporting decision making. Data warehouse is made by extracting large amount of data from different operational systems; transforming it to a consistent form and loading it to the central repository. The type of queries in data warehouse environment differs from those in operational systems. In contrast to operational systems, the analytical queries that are issued in data warehouses involve summarization of large volume of data and therefore in normal circumstance take a long time to be answered. On the other hand, the result of these queries must be answered in a short time to enable managers to make decisions as short time as possible. As a result, an essential need in this environment is in improving the performances of queries. One of the most popular methods to do this task is utilizing pre-computed result of queries. In this method, whenever a new query is submitted by the user instead of calculating the query on the fly through a large underlying database, the pre-computed result or views are used to answer the queries. Although, the ideal option would be pre-computing and saving all possible views, but, in practice due to disk space constraint and overhead due to view updates it is not considered as a feasible choice. Therefore, we need to select a subset of possible views to save on disk. The problem of selecting the right subset of views is considered as an important challenge in data warehousing. In this paper we suggest a Weighted Based Genetic Algorithm (WBGA) for solving the view selection problem with two objectives.
GRAVITATIONAL LENS MODELING WITH GENETIC ALGORITHMS AND PARTICLE SWARM OPTIMIZERS
Rogers, Adam; Fiege, Jason D.
2011-02-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point-spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least-squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automatically, which represents the trade-off between the image {chi}{sup 2} and regularization effects, and allows an estimate of the optimally regularized solution for each lens parameter set. In the final step of the optimization procedure, the lens model with the lowest {chi}{sup 2} is used while the global optimizer solves for the source intensity distribution directly. This allows us to accurately determine the number of degrees of freedom in the problem to facilitate comparison between lens models and enforce positivity on the source profile. In practice, we find that the GA conducts a more thorough search of the parameter space than the PSO.
Yoshimaru, Eriko S; Randtke, Edward A; Pagel, Mark D; Cárdenas-Rodríguez, Julio
2016-02-01
Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners. PMID:26778301
NASA Astrophysics Data System (ADS)
Yoshimaru, Eriko S.; Randtke, Edward A.; Pagel, Mark D.; Cárdenas-Rodríguez, Julio
2016-02-01
Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners.
Qin, Xiaosheng; Huang, Guohe; Liu, Lei
2010-01-01
A genetic-algorithm-aided stochastic optimization (GASO) model was developed in this study for supporting regional air quality management under uncertainty. The model incorporated genetic algorithm (GA) and Monte Carlo simulation techniques into a general stochastic chance-constrained programming (CCP) framework and allowed uncertainties in simulation and optimization model parameters to be considered explicitly in the design of least-cost strategies. GA was used to seek the optimal solution of the management model by progressively evaluating the performances of individual solutions. Monte Carlo simulation was used to check the feasibility of each solution. A management problem in terms of regional air pollution control was studied to demonstrate the applicability of the proposed method. Results of the case study indicated the proposed model could effectively communicate uncertainties into the optimization process and generate solutions that contained a spectrum of potential air pollutant treatment options with risk and cost information. Decision alternatives could be obtained by analyzing tradeoffs between the overall pollutant treatment cost and the system-failure risk due to inherent uncertainties.
Sensitivity of blackbody effective emissivity to wavelength and temperature: By genetic algorithm
Ejigu, E. K.; Liedberg, H. G.
2013-09-11
A variable-temperature blackbody (VTBB) is used to calibrate an infrared radiation thermometer (pyrometer). The effective emissivity (ε{sub eff}) of a VTBB is dependent on temperature and wavelength other than the geometry of the VTBB. In the calibration process the effective emissivity is often assumed to be constant within the wavelength and temperature range. There are practical situations where the sensitivity of the effective emissivity needs to be known and correction has to be applied. We present a method using a genetic algorithm to investigate the sensitivity of the effective emissivity to wavelength and temperature variation. Two matlab® programs are generated: the first to model the radiance temperature calculation and the second to connect the model to the genetic algorithm optimization toolbox. The effective emissivity parameter is taken as a chromosome and optimized at each wavelength and temperature point. The difference between the contact temperature (reading from a platinum resistance thermometer or liquid in glass thermometer) and radiance temperature (calculated from the ε{sub eff} values) is used as an objective function where merit values are calculated and best fit ε{sub eff} values selected. The best fit ε{sub eff} values obtained as a solution show how sensitive they are to temperature and wavelength parameter variation. Uncertainty components that arise from wavelength and temperature variation are determined based on the sensitivity analysis. Numerical examples are considered for illustration.
A practical algorithm for static analysis of parallel programs
McDowell, C.E. )
1989-06-01
One approach to analyzing the behavior of a concurrent program requires determining the reachable program states. A program state consists of a set of task states, the values of shared variables used for synchronization, and local variables that derive the values directly from synchronization operations. However, the number of reachable states rises exponentially with the number of tasks and becomes intractable for many concurrent programs. A variation of this approach merges a set of related states into a single virtual state. Using this approach, the analysis of concurrent programs becomes feasible as the number of virtual states is often orders of magnitude less than the number of reachable states. This paper presents a method for determining the virtual states that describe the reachable program states, and the reduction in the number of states is analyzed. The algorithms given have been implemented in a state program analyzer for multitasking Fortran, and the results obtained are discussed.
Constraint identification and algorithm stabilization for degenerate nonlinear programs.
Wright, S. J.; Mathematics and Computer Science
2003-01-01
In the vicinity of a solution of a nonlinear programming problem at which both strict complementarity and linear independence of the active constraints may fail to hold, we describe a technique for distinguishing weakly active from strongly active constraints. We show that this information can be used to modify the sequential quadratic programming algorithm so that it exhibits superlinear convergence to the solution under assumptions weaker than those made in previous analyses.
Use of a genetic algorithm to solve fluid flow problems on an NCUBE/2 multiprocessor computer
Pryor, R.J.; Cline, D.D.
1992-04-01
This paper presents a method to solve partial differential equations governing two-phase fluid flow by using a genetic algorithm on the NCUBE/2 multiprocessor computer. Genetic algorithms represent a significant departure from traditional approaches of solving fluid flow problems. The inherent parallelism of genetic algorithms offers the prospect of obtaining solutions faster than ever possible. The paper discusses the two-phase flow equations, the genetic representation of the unknowns, the fitness function, the genetic operators, and the implementation of the genetic algorithm on the NCUBE/2 computer. The paper investigates the implementation efficiency using a pipe blowdown test and presents the effects of varying both the genetic parameters and the number of processors. The results show that genetic algorithms provide a major advancement in methods for solving two-phase flow problems. A desired goal of solving these equations for a specific simulation problem in real time or faster requires computers with an order of magnitude more processors or faster than the NCUBE/2's 1024.
Use of a genetic algorithm to solve fluid flow problems on an NCUBE/2 multiprocessor computer
Pryor, R.J.; Cline, D.D.
1992-04-01
This paper presents a method to solve partial differential equations governing two-phase fluid flow by using a genetic algorithm on the NCUBE/2 multiprocessor computer. Genetic algorithms represent a significant departure from traditional approaches of solving fluid flow problems. The inherent parallelism of genetic algorithms offers the prospect of obtaining solutions faster than ever possible. The paper discusses the two-phase flow equations, the genetic representation of the unknowns, the fitness function, the genetic operators, and the implementation of the genetic algorithm on the NCUBE/2 computer. The paper investigates the implementation efficiency using a pipe blowdown test and presents the effects of varying both the genetic parameters and the number of processors. The results show that genetic algorithms provide a major advancement in methods for solving two-phase flow problems. A desired goal of solving these equations for a specific simulation problem in real time or faster requires computers with an order of magnitude more processors or faster than the NCUBE/2`s 1024.
NASA Astrophysics Data System (ADS)
Que, Dashun; Li, Gang; Yue, Peng
2007-12-01
An adaptive optimization watermarking algorithm based on Genetic Algorithm (GA) and discrete wavelet transform (DWT) is proposed in this paper. The core of this algorithm is the fitness function optimization model for digital watermarking based on GA. The embedding intensity for digital watermarking can be modified adaptively, and the algorithm can effectively ensure the imperceptibility of watermarking while the robustness is ensured. The optimization model research may provide a new idea for anti-coalition attacks of digital watermarking algorithm. The paper has fulfilled many experiments, including the embedding and extracting experiments of watermarking, the influence experiments by the weighting factor, the experiments of embedding same watermarking to the different cover image, the experiments of embedding different watermarking to the same cover image, the comparative analysis experiments between this optimization algorithm and human visual system (HVS) algorithm and etc. The simulation results and the further analysis show the effectiveness and advantage of the new algorithm, which also has versatility and expandability. And meanwhile it has better ability of anti-coalition attacks. Moreover, the robustness and security of watermarking algorithm are improved by scrambling transformation and chaotic encryption while preprocessing the watermarking.
Genetic algorithms and their application to in silico evolution of genetic regulatory networks.
Knabe, Johannes F; Wegner, Katja; Nehaniv, Chrystopher L; Schilstra, Maria J
2010-01-01
A genetic algorithm (GA) is a procedure that mimics processes occurring in Darwinian evolution to solve computational problems. A GA introduces variation through "mutation" and "recombination" in a "population" of possible solutions to a problem, encoded as strings of characters in "genomes," and allows this population to evolve, using selection procedures that favor the gradual enrichment of the gene pool with the genomes of the "fitter" individuals. GAs are particularly suitable for optimization problems in which an effective system design or set of parameter values is sought.In nature, genetic regulatory networks (GRNs) form the basic control layer in the regulation of gene expression levels. GRNs are composed of regulatory interactions between genes and their gene products, and are, inter alia, at the basis of the development of single fertilized cells into fully grown organisms. This paper describes how GAs may be applied to find functional regulatory schemes and parameter values for models that capture the fundamental GRN characteristics. The central ideas behind evolutionary computation and GRN modeling, and the considerations in GA design and use are discussed, and illustrated with an extended example. In this example, a GRN-like controller is sought for a developmental system based on Lewis Wolpert's French flag model for positional specification, in which cells in a growing embryo secrete and detect morphogens to attain a specific spatial pattern of cellular differentiation. PMID:20835807
Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-03-01
When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.
Genetic algorithm approach for adaptive power and subcarrier allocation in multi-user OFDM systems
NASA Astrophysics Data System (ADS)
Reddy, Y. B.; Naraghi-Pour, Mort
2007-04-01
In this paper, a novel genetic algorithm application is proposed for adaptive power and subcarrier allocation in multi-user Orthogonal Frequency Division Multiplexing (OFDM) systems. To test the application, a simple genetic algorithm was implemented in MATLAB language. With the goal of minimizing the overall transmit power while ensuring the fulfillment of each user's rate and bit error rate (BER) requirements, the proposed algorithm acquires the needed allocation through genetic search. The simulations were tested for BER 0.1 to 0.00001, data rate of 256 bit per OFDM block and chromosome length of 128. The results show that genetic algorithm outperforms the results in [3] in subcarrier allocation. The convergence of GA model with 8 users and 128 subcarriers performs better in power requirement compared to that in [4] but converges more slowly.
NASA Technical Reports Server (NTRS)
Wang, Lui; Valenzuela-Rendon, Manuel
1993-01-01
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.
Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
Louis, S.J.; Raines, G.L.
2003-01-01
We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.
Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Benford, Andrew; Tinker, Michael L.
2004-01-01
The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.
Genetic programming based ensemble system for microarray data classification.
Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To
2015-01-01
Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Rogers, L.L.
1992-08-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.
NASA Technical Reports Server (NTRS)
Tinker, Michael L.; Steincamp, James W.; Stewart, Eric T.; Patton, Bruce W.; Pannell, William P.; Newby, Ronald L.; Coffman, Mark E.; Qualls, A. L.; Bancroft, S.; Molvik, Greg
2003-01-01
The Nuclear Electric Vehicle Optimization Toolset (NEVOT) optimizes the design of all major Nuclear Electric Propulsion (NEP) vehicle subsystems for a defined mission within constraints and optimization parameters chosen by a user. The tool uses a Genetic Algorithm (GA) search technique to combine subsystem designs and evaluate the fitness of the integrated design to fulfill a mission. The fitness of an individual is used within the GA to determine its probability of survival through successive generations in which the designs with low fitness are eliminated and replaced with combinations or mutations of designs with higher fitness. The program can find optimal solutions for different sets of fitness metrics without modification and can create and evaluate vehicle designs that might never be conceived of through traditional design techniques. It is anticipated that the flexible optimization methodology will expand present knowledge of the design trade-offs inherent in designing nuclear powered space vehicles and lead to improved NEP designs.
Development of Web-Based Menu Planning Support System and its Solution Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Kashima, Tomoko; Matsumoto, Shimpei; Ishii, Hiroaki
2009-10-01
Recently lifestyle-related diseases have become an object of public concern, while at the same time people are being more health conscious. As an essential factor for causing the lifestyle-related diseases, we assume that the knowledge circulation on dietary habits is still insufficient. This paper focuses on everyday meals close to our life and proposes a well-balanced menu planning system as a preventive measure of lifestyle-related diseases. The system is developed by using a Web-based frontend and it provides multi-user services and menu information sharing capabilities like social networking services (SNS). The system is implemented on a Web server running Apache (HTTP server software), MySQL (database management system), and PHP (scripting language for dynamic Web pages). For the menu planning, a genetic algorithm is applied by understanding this problem as multidimensional 0-1 integer programming.
Comprehensive bidding strategies with genetic programming/finite state automata
Richter, C.W. Jr.; Sheble, G.B.; Ashlock, D.
1999-11-01
This research is an extension of the authors' previous work in double auctions aimed at developing bidding strategies for electric utilities which trade electricity competitively. The improvements detailed in this paper come from using data structures which combine genetic programming and finite state automata termed GP-Automata. The strategies developed by the method described here are adaptive--reacting to inputs--whereas the previously developed strategies were only suitable in the particular scenario for which they had been designed. The strategies encoded in the GP-Automata are tested in an auction simulator. The simulator pits them against other distribution companies (distcos) and generation companies (gencos), buying and selling power via double auctions implemented in regional commodity exchanges. The GP-Automata are evolved with a genetic algorithm so that they possess certain characteristics. In addition to designing successful bidding strategies (whose usage would result in higher profits) the resulting strategies can also be designed to imitate certain types of trading behaviors. The resulting strategies can be implemented directly in on-line trading, or can be used as realistic competitors in an off-line trading simulator.
Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
NASA Astrophysics Data System (ADS)
Balakrishnan, D.; Quan, C.; Tay, C. J.
2013-06-01
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.
ERIC Educational Resources Information Center
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
ERIC Educational Resources Information Center
Chen, Hsinchun
1995-01-01
Presents an overview of artificial-intelligence-based inductive learning techniques and their use in information science research. Three methods are discussed: the connectionist Hopfield network; the symbolic ID3/ID5R; evolution-based genetic algorithms. The knowledge representations and algorithms of these methods are examined in the context of…
Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm
ERIC Educational Resources Information Center
Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.
2009-01-01
Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…
Decomposition algorithms for stochastic programming on a computational grid.
Linderoth, J.; Wright, S.; Mathematics and Computer Science; Axioma Inc.
2003-01-01
We describe algorithms for two-stage stochastic linear programming with recourse and their implementation on a grid computing platform. In particular, we examine serial and asynchronous versions of the L-shaped method and a trust-region method. The parallel platform of choice is the dynamic, heterogeneous, opportunistic platform provided by the Condor system. The algorithms are of master-worker type (with the workers being used to solve second-stage problems), and the MW runtime support library (which supports master-worker computations) is key to the implementation. Computational results are presented on large sample-average approximations of problems from the literature.
NASA Astrophysics Data System (ADS)
Kanagaraj, G.; Ponnambalam, S. G.; Jawahar, N.; Mukund Nilakantan, J.
2014-10-01
This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.
A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.
ERIC Educational Resources Information Center
Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind
1999-01-01
Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)
Genetic Programming Transforms in Linear Regression Situations
NASA Astrophysics Data System (ADS)
Castillo, Flor; Kordon, Arthur; Villa, Carlos
The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.
NASA Astrophysics Data System (ADS)
Ushijima, Timothy T.; Yeh, William W.-G.
2013-10-01
An optimal experimental design algorithm is developed to select locations for a network of observation wells that provide maximum information about unknown groundwater pumping in a confined, anisotropic aquifer. The design uses a maximal information criterion that chooses, among competing designs, the design that maximizes the sum of squared sensitivities while conforming to specified design constraints. The formulated optimization problem is non-convex and contains integer variables necessitating a combinatorial search. Given a realistic large-scale model, the size of the combinatorial search required can make the problem difficult, if not impossible, to solve using traditional mathematical programming techniques. Genetic algorithms (GAs) can be used to perform the global search; however, because a GA requires a large number of calls to a groundwater model, the formulated optimization problem still may be infeasible to solve. As a result, proper orthogonal decomposition (POD) is applied to the groundwater model to reduce its dimensionality. Then, the information matrix in the full model space can be searched without solving the full model. Results from a small-scale test case show identical optimal solutions among the GA, integer programming, and exhaustive search methods. This demonstrates the GA's ability to determine the optimal solution. In addition, the results show that a GA with POD model reduction is several orders of magnitude faster in finding the optimal solution than a GA using the full model. The proposed experimental design algorithm is applied to a realistic, two-dimensional, large-scale groundwater problem. The GA converged to a solution for this large-scale problem.
Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm
NASA Technical Reports Server (NTRS)
Le Riche, Rodolphe; Haftka, Raphael T.
1992-01-01
The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.
An Evaluation of Potentials of Genetic Algorithm in Shortest Path Problem
NASA Astrophysics Data System (ADS)
Hassany Pazooky, S.; Rahmatollahi Namin, Sh; Soleymani, A.; Samadzadegan, F.
2009-04-01
One of the most typical issues considered in combinatorial systems in transportation networks, is the shortest path problem. In such networks, routing has a significant impact on the network's performance. Due to natural complexity in transportation networks and strong impact of routing in different fields of decision making, such as traffic management and vehicle routing problem (VRP), appropriate solutions to solve this problem are crucial to be determined. During last years, in order to solve the shortest path problem, different solutions are proposed. These techniques are divided into two categories of classic and evolutionary approaches. Two well-known classic algorithms are Dijkstra and A*. Dijkstra is known as a robust, but time consuming algorithm in finding the shortest path problem. A* is also another algorithm very similar to Dijkstra, less robust but with a higher performance. On the other hand, Genetic algorithms are introduced as most applicable evolutionary algorithms. Genetic Algorithm uses a parallel search method in several parts of the domain and is not trapped in local optimums. In this paper, the potentiality of Genetic algorithm for finding the shortest path is evaluated by making a comparison between this algorithm and classic algorithms (Dijkstra and A*). Evaluation of the potential of these techniques on a transportation network in an urban area shows that due to the problem of classic methods in their small search space, GA had a better performance in finding the shortest path.
Developing robotic behavior using a genetic programming model
Pryor, R.J.
1998-01-01
This report describes the methodology for using a genetic programming model to develop tracking behaviors for autonomous, microscale robotic vehicles. The use of such vehicles for surveillance and detection operations has become increasingly important in defense and humanitarian applications. Through an evolutionary process similar to that found in nature, the genetic programming model generates a computer program that when downloaded onto a robotic vehicle`s on-board computer will guide the robot to successfully accomplish its task. Simulations of multiple robots engaged in problem-solving tasks have demonstrated cooperative behaviors. This report also discusses the behavior model produced by genetic programming and presents some results achieved during the study.
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
NASA Astrophysics Data System (ADS)
Chen, Chao; Xia, Jianghai; Liu, Jiangping; Feng, Guangding
2006-03-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant
Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
Chen, C.; Xia, J.; Liu, J.; Feng, G.
2006-01-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant
NASA Astrophysics Data System (ADS)
Bigdeli, Kasra; Hare, Warren; Tesfamariam, Solomon
2012-04-01
Passive dampers can be used to connect two adjacent structures in order to mitigate earthquakes induced pounding damages. Theoretical and experimental studies have confirmed efficiency and applicability of various connecting devices, such as viscous damper, MR damper, etc. However, few papers employed optimization methods to find the optimal mechanical properties of the dampers, and in most papers, dampers are assumed to be uniform. In this study, we optimized the optimal damping coefficients of viscous dampers considering a general case of non-uniform damping coefficients. Since the derivatives of objective function to damping coefficients are not known, to optimize damping coefficients, a heuristic search method, i.e. the genetic algorithm, is employed. Each structure is modeled as a multi degree of freedom dynamic system consisting of lumped-masses, linear springs and dampers. In order to examine dynamic behavior of the structures, simulations in frequency domain are carried out. A pseudo-excitation based on Kanai-Tajimi spectrum is used as ground acceleration. The optimization results show that relaxing the uniform dampers coefficient assumption generates significant improvement in coupling effectiveness. To investigate efficiency of genetic algorithm, solution quality and solution time of genetic algorithm are compared with those of Nelder-Mead algorithm.
Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A
2015-06-01
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.
Road Traffic Control Based on Genetic Algorithm for Reducing Traffic Congestion
NASA Astrophysics Data System (ADS)
Shigehiro, Yuji; Miyakawa, Takuya; Masuda, Tatsuya
In this paper, we propose a road traffic control method for reducing traffic congestion with genetic algorithm. In the not too distant future, the system which controls the routes of all vehicles in a certain area must be realized. The system should optimize the routes of all vehicles, however the solution space of this problem is enormous. Therefore we apply the genetic algorithm to this problem, by encoding the route of all vehicles to a fixed length chromosome. To improve the search performance, a new genetic operator called “path shortening” is also designed. The effectiveness of the proposed method is shown by the experiment.
The convergence analysis of parallel genetic algorithm based on allied strategy
NASA Astrophysics Data System (ADS)
Lin, Feng; Sun, Wei; Chang, K. C.
2010-04-01
Genetic algorithms (GAs) have been applied to many difficult optimization problems such as track assignment and hypothesis managements for multisensor integration and data fusion. However, premature convergence has been a main problem for GAs. In order to prevent premature convergence, we introduce an allied strategy based on biological evolution and present a parallel Genetic Algorithm with the allied strategy (PGAAS). The PGAAS can prevent premature convergence, increase the optimization speed, and has been successfully applied in a few applications. In this paper, we first present a Markov chain model in the PGAAS. Based on this model, we analyze the convergence property of PGAAS. We then present the proof of global convergence for the PGAAS algorithm. The experiments results show that PGAAS is an efficient and effective parallel Genetic algorithm. Finally, we discuss several potential applications of the proposed methodology.
An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints
Rao, Yunqing; Qi, Dezhong; Li, Jinling
2013-01-01
For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491
Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating
Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.; Taylor, A.J.
1999-04-12
The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a general parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.
A dynamic programming algorithm for finding alternative RNA secondary structures.
Williams, A L; Tinoco, I
1986-01-10
Dynamic programming algorithms that predict RNA secondary structure by minimizing the free energy have had one important limitation. They were able to predict only one optimal structure. Given the uncertainties of the thermodynamic data and the effects of proteins and other environmental factors on structure, the optimal structure predicted by these methods may not have biological significance. We present a dynamic programming algorithm that can determine optimal and suboptimal secondary structures for an RNA. The power and utility of the method is demonstrated in the folding of the intervening sequence of the rRNA of Tetrahymena. By first identifying the major secondary structures corresponding to the lowest free energy minima, a secondary structure of possible biological significance is derived.
Abejuela, Harmony Raylen; Osser, David N
2016-01-01
This revision of previous algorithms for the pharmacotherapy of generalized anxiety disorder was developed by the Psychopharmacology Algorithm Project at the Harvard South Shore Program. Algorithms from 1999 and 2010 and associated references were reevaluated. Newer studies and reviews published from 2008-14 were obtained from PubMed and analyzed with a focus on their potential to justify changes in the recommendations. Exceptions to the main algorithm for special patient populations, such as women of childbearing potential, pregnant women, the elderly, and those with common medical and psychiatric comorbidities, were considered. Selective serotonin reuptake inhibitors (SSRIs) are still the basic first-line medication. Early alternatives include duloxetine, buspirone, hydroxyzine, pregabalin, or bupropion, in that order. If response is inadequate, then the second recommendation is to try a different SSRI. Additional alternatives now include benzodiazepines, venlafaxine, kava, and agomelatine. If the response to the second SSRI is unsatisfactory, then the recommendation is to try a serotonin-norepinephrine reuptake inhibitor (SNRI). Other alternatives to SSRIs and SNRIs for treatment-resistant or treatment-intolerant patients include tricyclic antidepressants, second-generation antipsychotics, and valproate. This revision of the GAD algorithm responds to issues raised by new treatments under development (such as pregabalin) and organizes the evidence systematically for practical clinical application. PMID:27384395
Abejuela, Harmony Raylen; Osser, David N
2016-01-01
This revision of previous algorithms for the pharmacotherapy of generalized anxiety disorder was developed by the Psychopharmacology Algorithm Project at the Harvard South Shore Program. Algorithms from 1999 and 2010 and associated references were reevaluated. Newer studies and reviews published from 2008-14 were obtained from PubMed and analyzed with a focus on their potential to justify changes in the recommendations. Exceptions to the main algorithm for special patient populations, such as women of childbearing potential, pregnant women, the elderly, and those with common medical and psychiatric comorbidities, were considered. Selective serotonin reuptake inhibitors (SSRIs) are still the basic first-line medication. Early alternatives include duloxetine, buspirone, hydroxyzine, pregabalin, or bupropion, in that order. If response is inadequate, then the second recommendation is to try a different SSRI. Additional alternatives now include benzodiazepines, venlafaxine, kava, and agomelatine. If the response to the second SSRI is unsatisfactory, then the recommendation is to try a serotonin-norepinephrine reuptake inhibitor (SNRI). Other alternatives to SSRIs and SNRIs for treatment-resistant or treatment-intolerant patients include tricyclic antidepressants, second-generation antipsychotics, and valproate. This revision of the GAD algorithm responds to issues raised by new treatments under development (such as pregabalin) and organizes the evidence systematically for practical clinical application.
Gregurick, S. K.
2001-04-20
During the period from September 1, 1998 until September 1, 2000 I was awarded a Sloan/DOE postdoctoral fellowship to work in collaboration with Professor John Moult at the Center for Advanced Research in Biotechnology (CARB). Our research project, ''Ab Initio Protein Tertiary Structure Prediction and a Comparative Genetic algorithm'', yielded promising initial results. In short, the project is designed to predict the native fold, or native tertiary structure, of a given protein by inputting only the primary sequence of the protein (one or three letter code). The algorithm is based on a general learning, or evolutionary algorithm and is called Genetic Algorithm (GAS). In our particular application of GAS, we search for native folds, or lowest energy structures, using two different descriptions for the interactions of the atoms and residues in a given protein sequence. One potential energy function is based on a free energy description, while the other function is a threading potential derived by Moult and Samudrala. This modified genetic algorithm was loosely termed a Comparative Genetic Algorithm and was designed to search for native folded structures on both potential energy surfaces, simultaneously. We tested the algorithm on a series of peptides ranging from 11 to 15 residues in length, which are thought to be independent folding units and thereby will fold to native structures independent of the larger protein environment. Our initial results indicated a modest increase in accuracy, as compared to a standard Genetic Algorithm. We are now in the process of improving the algorithm to increase the sensitivity to other inputs, such as secondary structure requirements. The project did not involve additional students and as of yet, the work has not been published.
Reveal, A General Reverse Engineering Algorithm for Inference of Genetic Network Architectures
NASA Technical Reports Server (NTRS)
Liang, Shoudan; Fuhrman, Stefanie; Somogyi, Roland
1998-01-01
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(exp 15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
Reveal, a general reverse engineering algorithm for inference of genetic network architectures.
Liang, S; Fuhrman, S; Somogyi, R
1998-01-01
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n = 50 (elements) and k = 3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10(15)) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean networks, the algorithm is generalizable to include multi-state models, essentially allowing direct application to realistic biological data sets. The ability to adequately solve the inverse problem may enable in-depth analysis of complex dynamic systems in biology and other fields.
Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G
2014-01-01
Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances. PMID:24102647
Martins, F V C; Carrano, E G; Wanner, E F; Takahashi, R H C; Mateus, G R; Nakamura, F G
2014-01-01
Recent works raised the hypothesis that the assignment of a geometry to the decision variable space of a combinatorial problem could be useful both for providing meaningful descriptions of the fitness landscape and for supporting the systematic construction of evolutionary operators (the geometric operators) that make a consistent usage of the space geometric properties in the search for problem optima. This paper introduces some new geometric operators that constitute the realization of searches along the combinatorial space versions of the geometric entities descent directions and subspaces. The new geometric operators are stated in the specific context of the wireless sensor network dynamic coverage and connectivity problem (WSN-DCCP). A genetic algorithm (GA) is developed for the WSN-DCCP using the proposed operators, being compared with a formulation based on integer linear programming (ILP) which is solved with exact methods. That ILP formulation adopts a proxy objective function based on the minimization of energy consumption in the network, in order to approximate the objective of network lifetime maximization, and a greedy approach for dealing with the system's dynamics. To the authors' knowledge, the proposed GA is the first algorithm to outperform the lifetime of networks as synthesized by the ILP formulation, also running in much smaller computational times for large instances.
A genetic algorithm that seeks native states of peptides and proteins.
Sun, S
1995-01-01
We describe a computer algorithm to predict native structures of proteins and peptides from their primary sequences, their known native radii of gyration, and their known disulfide bonding patterns, starting from random conformations. Proteins are represented as simplified real-space main chains with single-bead side chains. Nonlocal interactions are taken from structural database-derived statistical potentials, as in an earlier treatment. Local interactions are taken from simulations of (phi, psi) energy surfaces for each amino acid generated using the Biosym Discover program. Conformational searching is done by a genetic algorithm-based method. Reasonable structures are obtained for melittin (a 26-mer), avian pancreatic polypeptide inhibitor (a 36-mer), crambin (a 46-mer), apamin (an 18-mer), tachyplesin (a 17-mer), C-peptide of ribonuclease A (a 13-mer), and four different designed helical peptides. A hydrogen bond interaction was tested and found to be generally unnecessary for helical peptides, but it helps fold some sheet regions in these structures. For the few longer chains we tested, the method appears not to converge. In those cases, it appears to recover native-like secondary structures, but gets incorrect tertiary folds. PMID:8527647
Bloat free genetic programming: application to human oral bioavailability prediction.
Silva, Sara; Vanneschi, Leonardo
2012-01-01
Being able to predict the human oral bioavailability for a potential new drug is extremely important for the drug discovery process. This problem has been addressed by several prediction tools, with Genetic Programming providing some of the best results ever achieved. In this paper we use the newest developments of Genetic Programming, in particular the latest bloat control method, Operator Equalisation, to find out how much improvement we can achieve on this problem. We show examples of some actual solutions and discuss their quality, comparing them with previously published results. We identify some unexpected behaviours related to overfitting, and discuss the way for further improving the practical usage of the Genetic Programming approach.
A Genetic Algorithm Approach to Nonlinear Least Squares Estimation
ERIC Educational Resources Information Center
Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.
2004-01-01
A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…
The application of a genetic algorithm for solving crystal structures from powder diffraction data
NASA Astrophysics Data System (ADS)
Kariuki, Benson M.; Serrano-González, Heliodoro; Johnston, Roy L.; Harris, Kenneth D. M.
1997-12-01
We report the successful application of a genetic algorithm to tackle crystal structure solution from powder diffraction data in the case of a previously unknown structure — ortho-thymotic acid. In the structure solution calculation, the structural fragment was subjected to combined translation and rotation within the unit cell, together with variation of selected intramolecular degrees of freedom under the control of a genetic algorithm, in which a population of trial structures is allowed to evolve subject to well-defined procedures for mating, mutation and natural selection. Importantly, the genetic algorithm approach adopts the `direct-space' philosophy for structure solution, and implicitly avoids the problematic step of extracting the intensities of individual reflections from the powder diffraction data. The structure solution was found efficiently in the genetic algorithm calculation, and was then used as the initial structural model in Rietveld refinement calculations. The work reported in this Letter paves the way for the future application of the genetic algorithm approach to a much wider array of structural problems.
A probabilistic coding based quantum genetic algorithm for multiple sequence alignment.
Huo, Hongwei; Xie, Qiaoluan; Shen, Xubang; Stojkovic, Vojislav
2008-01-01
This paper presents an original Quantum Genetic algorithm for Multiple sequence ALIGNment (QGMALIGN) that combines a genetic algorithm and a quantum algorithm. A quantum probabilistic coding is designed for representing the multiple sequence alignment. A quantum rotation gate as a mutation operator is used to guide the quantum state evolution. Six genetic operators are designed on the coding basis to improve the solution during the evolutionary process. The features of implicit parallelism and state superposition in quantum mechanics and the global search capability of the genetic algorithm are exploited to get efficient computation. A set of well known test cases from BAliBASE2.0 is used as reference to evaluate the efficiency of the QGMALIGN optimization. The QGMALIGN results have been compared with the most popular methods (CLUSTALX, SAGA, DIALIGN, SB_PIMA, and QGMALIGN) results. The QGMALIGN results show that QGMALIGN performs well on the presenting biological data. The addition of genetic operators to the quantum algorithm lowers the cost of overall running time.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
Mohammad, Othman; Osser, David N
2014-01-01
This new algorithm for the pharmacotherapy of acute mania was developed by the Psychopharmacology Algorithm Project at the Harvard South Shore Program. The authors conducted a literature search in PubMed and reviewed key studies, other algorithms and guidelines, and their references. Treatments were prioritized considering three main considerations: (1) effectiveness in treating the current episode, (2) preventing potential relapses to depression, and (3) minimizing side effects over the short and long term. The algorithm presupposes that clinicians have made an accurate diagnosis, decided how to manage contributing medical causes (including substance misuse), discontinued antidepressants, and considered the patient's childbearing potential. We propose different algorithms for mixed and nonmixed mania. Patients with mixed mania may be treated first with a second-generation antipsychotic, of which the first choice is quetiapine because of its greater efficacy for depressive symptoms and episodes in bipolar disorder. Valproate and then either lithium or carbamazepine may be added. For nonmixed mania, lithium is the first-line recommendation. A second-generation antipsychotic can be added. Again, quetiapine is favored, but if quetiapine is unacceptable, risperidone is the next choice. Olanzapine is not considered a first-line treatment due to its long-term side effects, but it could be second-line. If the patient, whether mixed or nonmixed, is still refractory to the above medications, then depending on what has already been tried, consider carbamazepine, haloperidol, olanzapine, risperidone, and valproate first tier; aripiprazole, asenapine, and ziprasidone second tier; and clozapine third tier (because of its weaker evidence base and greater side effects). Electroconvulsive therapy may be considered at any point in the algorithm if the patient has a history of positive response or is intolerant of medications.
The Use of Genetic Programming for Learning 3D Craniofacial Shape Quantifications.
Atmosukarto, Indriyati; Shapiro, Linda G; Heike, Carrie
2010-01-01
Craniofacial disorders commonly result in various head shape dysmorphologies. The goal of this work is to quantify the various 3D shape variations that manifest in the different facial abnormalities in individuals with a craniofacial disorder called 22q11.2 Deletion Syndrome. Genetic programming (GP) is used to learn the different 3D shape quantifications. Experimental results show that the GP method achieves a higher classification rate than those of human experts and existing computer algorithms [1], [2].
Genetic algorithms and their applications in accelerator physics
Hofler, Alicia S.
2013-12-01
Multi-objective optimization techniques are widely used in an extremely broad range of fields. Genetic optimization for multi-objective optimization was introduced in the accelerator community in relatively recent times and quickly spread becoming a fundamental tool in multi-dimensional optimization problems. This discussion introduces the basics of the technique and reviews applications in accelerator problems.
An algorithm for the identification of genetically modified animals.
Forabosco, Flavio; Sundström, Fredrik L; Rydhmer, Lotta
2013-05-01
The diffusion of genetically modified (GM) animals has generated a demand for accurate and unique identification to assure compliance with relevant national and international legislation. Individual identification of GM animals is essential to improve safety and traceability, as well as to fulfill the present and future expectations of producers, consumers, and authorities.
Downscaling GCM Output with Genetic Programming Model
NASA Astrophysics Data System (ADS)
Shi, X.; Dibike, Y. B.; Coulibaly, P.
2004-05-01
Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily
Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.
Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun
2015-01-01
This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity. PMID:25802512
Pfeffer, Patrick; Fober, Thomas; Hüllermeier, Eyke; Klebe, Gerhard
2010-09-27
In combinatorial chemistry, molecules are assembled according to combinatorial principles by linking suitable reagents or decorating a given scaffold with appropriate substituents from a large chemical space of starting materials. Often the number of possible combinations greatly exceeds the number feasible to handle by an in-depth in silico approach or even more if it should be experimentally synthesized. Therefore, powerful tools to efficiently enumerate large chemical spaces are required. They can be provided by genetic algorithms, which mimic Darwinian evolution. GARLig (genetic algorithm using reagents to compose ligands) has been developed to perform subset selection in large chemical compound spaces subject to target-specific 3D-scoring criteria. GARLig uses different scoring schemes, such as AutoDock4 Score, GOLDScore, and DrugScore(CSD), as fitness functions. Its genetic parameters have been optimized to characterize combinatorial libraries with respect to the binding to various targets of pharmaceutical interest. A large tripeptide library of 20(3) members has been used to profile amino acid frequencies in putative substrates for trypsin, thrombin, factor Xa, and plasmin. A peptidomimetic scaffold assembled from a selection of a 25(3) building block was used to test the performance of the evolutionary algorithm in suggesting potent inhibitors of the enzyme cathepsin D. In a final case study, our program was used to characterize and rank a combinatorial drug-like library comprising 33,750 potential thrombin inhibitors. These case studies demonstrate that GARLig finds experimentally confirmed potent leads by processing a significantly smaller subset of the fully enumerated combinatorial library. Furthermore, the profiles of amino acids computed by the genetic algorithm match the observed amino acid frequencies found by screening peptide libraries in substrate cleavage assays.
A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...
Using genetic algorithm to solve a new multi-period stochastic optimization model
NASA Astrophysics Data System (ADS)
Zhang, Xin-Li; Zhang, Ke-Cun
2009-09-01
This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.
A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
A high fuel consumption efficiency management scheme for PHEVs using an adaptive genetic algorithm.
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day.
Song, Jiancai; Xue, Guixiang; Kang, Yanan
2016-01-01
In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm that can select a subset of the satellites represented by specific numbers in the interval (4 ∼ n) while maintaining position accuracy. A comprehensive simulation demonstrates that the MGA-based satellite selection method effectively selects the correct number of optimal satellite subsets using receiver autonomous integrity monitoring (RAIM) or fault detection and exclusion (FDE). This method is more adaptable and flexible for GPS receivers, particularly for those used in handset equipment and mobile phones. PMID:26943638
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
Song, Jiancai; Xue, Guixiang; Kang, Yanan
2016-01-01
In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm that can select a subset of the satellites represented by specific numbers in the interval (4 ∼ n) while maintaining position accuracy. A comprehensive simulation demonstrates that the MGA-based satellite selection method effectively selects the correct number of optimal satellite subsets using receiver autonomous integrity monitoring (RAIM) or fault detection and exclusion (FDE). This method is more adaptable and flexible for GPS receivers, particularly for those used in handset equipment and mobile phones. PMID:26943638
NASA Astrophysics Data System (ADS)
Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.
2016-06-01
The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.
Genetic algorithm based image binarization approach and its quantitative evaluation via pooling
NASA Astrophysics Data System (ADS)
Hu, Huijun; Liu, Ya; Liu, Maofu
2015-12-01
The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.
Song, Jiancai; Xue, Guixiang; Kang, Yanan
2016-01-01
In this paper, a novel method for selecting a navigation satellite subset for a global positioning system (GPS) based on a genetic algorithm is presented. This approach is based on minimizing the factors in the geometric dilution of precision (GDOP) using a modified genetic algorithm (MGA) with an elite conservation strategy, adaptive selection, adaptive mutation, and a hybrid genetic algorithm that can select a subset of the satellites represented by specific numbers in the interval (4 ∼ n) while maintaining position accuracy. A comprehensive simulation demonstrates that the MGA-based satellite selection method effectively selects the correct number of optimal satellite subsets using receiver autonomous integrity monitoring (RAIM) or fault detection and exclusion (FDE). This method is more adaptable and flexible for GPS receivers, particularly for those used in handset equipment and mobile phones.
Genetic algorithms and classifier systems: Foundations and future directions
Holland, J.H.
1987-01-01
Theoretical questions about classifier systems, with rare exceptions, apply equally to other adaptive nonlinear networks (ANNs) such as the connectionist models of cognitive psychology, the immune system, economic systems, ecologies, and genetic systems. This paper discusses pervasive properties of ANNs and the kinds of mathematics relevant to questions about these properties. It discusses relevant functional extensions of the basic classifier system and extensions of the extant mathematical theory. An appendix briefly reviews some of the key theorems about classifier systems. 6 refs.
Zhang Changjiang; Wang Xiaodong
2008-11-06
An efficient typhoon cloud image restoration algorithm is proposed. Having implemented contourlet transform to a typhoon cloud image, noise is reduced in the high sub-bands. Weight median value filter is used to reduce the noise in the contourlet domain. Inverse contourlet transform is done to obtain the de-noising image. In order to enhance the global contrast of the typhoon cloud image, in-complete Beta transform (IBT) is used to determine non-linear gray transform curve so as to enhance global contrast for the de-noising typhoon cloud image. Genetic algorithm is used to obtain the optimal gray transform curve. Information entropy is used as the fitness function of the genetic algorithm. Experimental results show that the new algorithm is able to well enhance the global for the typhoon cloud image while well reducing the noises in the typhoon cloud image.
Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian
2015-01-01
A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following. PMID:26343655
Hybrid Genetic Algorithm - Local Search Method for Ground-Water Management
NASA Astrophysics Data System (ADS)
Chiu, Y.; Nishikawa, T.; Martin, P.
2008-12-01
Ground-water management problems commonly are formulated as a mixed-integer, non-linear programming problem (MINLP). Relying only on conventional gradient-search methods to solve the management problem is computationally fast; however, the methods may become trapped in a local optimum. Global-optimization schemes can identify the global optimum, but the convergence is very slow when the optimal solution approaches the global optimum. In this study, we developed a hybrid optimization scheme, which includes a genetic algorithm and a gradient-search method, to solve the MINLP. The genetic algorithm identifies a near- optimal solution, and the gradient search uses the near optimum to identify the global optimum. Our methodology is applied to a conjunctive-use project in the Warren ground-water basin, California. Hi- Desert Water District (HDWD), the primary water-manager in the basin, plans to construct a wastewater treatment plant to reduce future septic-tank effluent from reaching the ground-water system. The treated wastewater instead will recharge the ground-water basin via percolation ponds as part of a larger conjunctive-use strategy, subject to State regulations (e.g. minimum distances and travel times). HDWD wishes to identify the least-cost conjunctive-use strategies that control ground-water levels, meet regulations, and identify new production-well locations. As formulated, the MINLP objective is to minimize water-delivery costs subject to constraints including pump capacities, available recharge water, water-supply demand, water-level constraints, and potential new-well locations. The methodology was demonstrated by an enumerative search of the entire feasible solution and comparing the optimum solution with results from the branch-and-bound algorithm. The results also indicate that the hybrid method identifies the global optimum within an affordable computation time. Sensitivity analyses, which include testing different recharge-rate scenarios, pond
Stochastic optimization of a cold atom experiment using a genetic algorithm
Rohringer, W.; Buecker, R.; Manz, S.; Betz, T.; Koller, Ch.; Goebel, M.; Perrin, A.; Schmiedmayer, J.; Schumm, T.
2008-12-29
We employ an evolutionary algorithm to automatically optimize different stages of a cold atom experiment without human intervention. This approach closes the loop between computer based experimental control systems and automatic real time analysis and can be applied to a wide range of experimental situations. The genetic algorithm quickly and reliably converges to the most performing parameter set independent of the starting population. Especially in many-dimensional or connected parameter spaces, the automatic optimization outperforms a manual search.
Chen, S; Wu, Y; Luk, B L
1999-01-01
The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
A study on ionospheric TEC forecast using genetic algorithm and neural network
NASA Astrophysics Data System (ADS)
Huang, Zhi; Yuan, Hong
Back propagation artificial neural network (ANN) augmented by genetic algorithm (GA) is introduced to forecast ionospheric TEC with the dual-frequency GPS measurements from the low and high solar activity years in this paper due to ionosphere space characterizing by the highly nonlinear and time-varying with random variations. First, with different number of neurons in the hidden layer, different transfer function and training function, the training performance of network model is analyzed and then optimized network structure is determined. The ionospheric TEC values one hour in advance are forecasted and further the prediction performance of the developed network model is evaluated at the given criterions. The results show that predicted TEC using BP neural network improved by genetic algorithm has good agreement with observed data. In addition, the prediction errors are smaller in middle and high latitudes than in low latitudes, smaller in low solar activity than in high solar activity. Compared with BP Network with three layers structure, Prediction precision of network model optimized by genetic algorithm is further improved. The resolution quality indicate that the proposed algorithm can offer a powerful and reliable alternative to the design of ionospheric TEC forecast technologies, and provide advice for the regional ionospheric TEC maps. Key words: Neural network, Genetic algorithm, Ionospheric TEC, Forecast,
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
Navigation of Autonomous Mobile Robot under Decision-making Strategy tuned by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Wang, Fei; Kamano, Takuya; Yasuno, Takashi; Suzuki, Takayuki; Harada, Hironobu
This paper describes a novel application of genetic algorithm for navigation of an autonomous mobile robot (AMR) under unknown environments. In the navigation system, the AMR is controlled by the decision-making block, which consists of neural network. To achieve both successful navigation to the goal and the suitable obstacle avoidance, the connection weights of the neural network and speed gains for predefined actions are encoded as genotypes and are tuned simultaneously by genetic algorithm so that the static and dynamic danger-degrees, the energy consumption and the distance and direction errors decrease during the navigation. Experimental results demonstrate the validity of the proposed navigation system.
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.
Use of a genetic algorithm to solve two-fluid flow problems on an NCUBE multiprocessor computer
Pryor, R.J.; Cline, D.D.
1992-01-01
A method of solving the two-phase fluid flow equations using a genetic algorithm on a NCUBE multiprocessor computer is presented. The topics discussed are the two-phase flow equations, the genetic representation of the unknowns, the fitness function, the genetic operators, and the implementation of the algorithm on the NCUBE computer. The efficiency of the implementation is investigated using a pipe blowdown problem. Effects of varying the genetic parameters and the number of processors are presented.
Use of a genetic algorithm to solve two-fluid flow problems on an NCUBE multiprocessor computer
Pryor, R.J.; Cline, D.D.
1992-12-31
A method of solving the two-phase fluid flow equations using a genetic algorithm on a NCUBE multiprocessor computer is presented. The topics discussed are the two-phase flow equations, the genetic representation of the unknowns, the fitness function, the genetic operators, and the implementation of the algorithm on the NCUBE computer. The efficiency of the implementation is investigated using a pipe blowdown problem. Effects of varying the genetic parameters and the number of processors are presented.
Dynamic programming and graph algorithms in computer vision.
Felzenszwalb, Pedro F; Zabih, Ramin
2011-04-01
Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.
Research on Formation of Microsatellite Communication with Genetic Algorithm
Wu, Guoqiang; Bai, Yuguang; Sun, Zhaowei
2013-01-01
For the formation of three microsatellites which fly in the same orbit and perform three-dimensional solid mapping for terra, this paper proposes an optimizing design method of space circular formation order based on improved generic algorithm and provides an intersatellite direct spread spectrum communication system. The calculating equation of LEO formation flying satellite intersatellite links is guided by the special requirements of formation-flying microsatellite intersatellite links, and the transmitter power is also confirmed throughout the simulation. The method of space circular formation order optimizing design based on improved generic algorithm is given, and it can keep formation order steady for a long time under various absorb impetus. The intersatellite direct spread spectrum communication system is also provided. It can be found that, when the distance is 1 km and the data rate is 1 Mbps, the input wave matches preferably with the output wave. And LDPC code can improve the communication performance. The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. So, the presented method provides a significant theory foundation for formation-flying and intersatellite communication. PMID:24078796
Research on formation of microsatellite communication with genetic algorithm.
Wu, Guoqiang; Bai, Yuguang; Sun, Zhaowei
2013-01-01
For the formation of three microsatellites which fly in the same orbit and perform three-dimensional solid mapping for terra, this paper proposes an optimizing design method of space circular formation order based on improved generic algorithm and provides an intersatellite direct spread spectrum communication system. The calculating equation of LEO formation flying satellite intersatellite links is guided by the special requirements of formation-flying microsatellite intersatellite links, and the transmitter power is also confirmed throughout the simulation. The method of space circular formation order optimizing design based on improved generic algorithm is given, and it can keep formation order steady for a long time under various absorb impetus. The intersatellite direct spread spectrum communication system is also provided. It can be found that, when the distance is 1 km and the data rate is 1 Mbps, the input wave matches preferably with the output wave. And LDPC code can improve the communication performance. The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. So, the presented method provides a significant theory foundation for formation-flying and intersatellite communication.
Biotech 101: an educational outreach program in genetics and biotechnology.
East, Kelly M; Hott, Adam M; Callanan, Nancy P; Lamb, Neil E
2012-10-01
Recent advances in research and biotechnology are making genetics and genomics increasingly relevant to the lives and health of the general public. For the public to make informed healthcare and public policy decisions relating to genetic information, there is a need for increased genetic literacy. Biotech 101 is a free, short-course for the local community introducing participants to topics in genetics, genomics, and biotechnology, created at the HudsonAlpha Institute for Biotechnology. This study evaluated the effectiveness of Biotech 101 in increasing the genetic literacy of program participants through pre-and-post surveys. Genetic literacy was measured through increases in self-perceived knowledge for each content area covered through the course and the self-reported impact the course had on various aspects of participants' lives. Three hundred ninety-two individuals attended Biotech 101 during the first three course offerings. Participants reported a significant increase in self-perceived knowledge for each content area (p < 0.01). Participants also reported the program had high levels of impact on their lives and decision-making, a high likelihood for continued self-learning, and overwhelming satisfaction with course content and logistics. Biotech 101 is an effective mechanism for impacting participants' lives and genetic literacy and serves as a model for other similar programs, adding to the currently limited evidence base regarding public educational strategies in genetics and biotechnology.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three
Learning Cue Phrase Patterns from Radiology Reports Using a Genetic Algorithm
Patton, Robert M; Beckerman, Barbara G; Potok, Thomas E
2009-01-01
Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns in radiology reports that utilizes a genetic algorithm (GA) as the learning method. The approach described here successfully learned cue phrase patterns for two distinct classes of radiology reports. These patterns can then be used as a basis for automatically categorizing, clustering, or retrieving relevant data for the user.
Analysis of charge-exchange spectroscopy data by combining genetic and Gauss-Newton algorithms
NASA Astrophysics Data System (ADS)
Qian, Ma; Haoyi, Zuo; Yanling, Wei; Liang, Liu; Wenjin, Chen; Xiaoxue, He; Shirong, Luo
2015-11-01
The temperature and rotation velocity profile of ions in a tokamak are two characteristic parameters that reflect the plasma's behavior. Measurement of the two parameters relies on analyzing an active charge exchange spectroscopy diagnostic. However, a very challenging problem in such a diagnostic is the existence of interfering spectral lines, which can mislead the spectrum analysis process. This work proposes combining a genetic algorithm with the Gauss-Newton method (GAGN) to address this problem. Using this GAGN algorithm, we can effectively distinguish between the useful spectrum line and the interfering spectral lines within the spectroscopic output. The accuracy and stability of this algorithm are verified using both numerical simulation and actual measurements.
The RHSA strategy for the allocation of outbound containers based on the hybrid genetic algorithm
NASA Astrophysics Data System (ADS)
Le, Meilong; Yu, Hang
2013-09-01
Secure storage yard is one of the optimal core goals of container transportation; thus, making the necessary storage arrangements has become the most crucial part of the container terminal management systems (CTMS). This paper investigates a random hybrid stacking algorithm (RHSA) for outbound containers that randomly enter the yard. In the first stage of RHSA, the distribution among blocks was analyzed with respect to the utilization ratio. In the second stage, the optimization of bay configuration was carried out by using the hybrid genetic algorithm. Moreover, an experiment was performed to test the RHSA. The results show that the explored algorithm is useful to increase the efficiency.
Application of BP Neural Network Based on Genetic Algorithm in Quantitative Analysis of Mixed GAS
NASA Astrophysics Data System (ADS)
Chen, Hongyan; Liu, Wenzhen; Qu, Jian; Zhang, Bing; Li, Zhibin
Aiming at the problem of mixed gas detection in neural network and analysis on the principle of gas detection. Combining BP algorithm of genetic algorithm with hybrid gas sensors, a kind of quantitative analysis system of mixed gas is designed. The local minimum of network learning is the main reason which affects the precision of gas analysis. On the basis of the network study to improve the learning algorithms, the analyses and tests for CO, CO2 and HC compounds were tested. The results showed that the above measures effectively improve and enhance the accuracy of the neural network for gas analysis.
Color tongue image segmentation using fuzzy Kohonen networks and genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Aimin; Shen, Lansun; Zhao, Zhongxu
2000-04-01
A Tongue Imaging and Analysis System is being developed to acquire digital color tongue images, and to automatically classify and quantify the tongue characteristics for traditional Chinese medical examinations. An important processing step is to segment the tongue pixels into two categories, the tongue body (no coating) and the coating. In this paper, we present a two-stage clustering algorithm that combines Fuzzy Kohonen Clustering Networks and Genetic Algorithm for the segmentation, of which the major concern is to increase the interclass distance and at the same time decrease the intraclass distance. Experimental results confirm the effectiveness of this algorithm.
NASA Astrophysics Data System (ADS)
Fustes, Diego; Ordóñez, Diego; Dafonte, Carlos; Manteiga, Minia; Arcay, Bernardino
This work presents an algorithm that was developed to select the most relevant areas of a stellar spectrum to extract its basic atmospheric parameters. We consider synthetic spectra obtained from models of stellar atmospheres in the spectral region of the radial velocity spectrograph instrument of the European Space Agency's Gaia space mission. The algorithm that demarcates the areas of the spectra sensitive to each atmospheric parameter (effective temperature and gravity, metallicity, and abundance of alpha elements) is a genetic algorithm, and the parameterization takes place through the learning of artificial neural networks. Due to the high computational cost of processing, we present a distributed implementation in both multiprocessor and multicomputer environments.
NASA Astrophysics Data System (ADS)
Jiang, Chen; Guo, Yinbiao; Yang, Qingqing; Han, Chunguang
2010-10-01
A new approach based on an artificial neural network (ANN) was presented for the prediction of machining precision of optical aspheric grinding. The ANN model is based on Globally Convergent Adaptive Quick Back Propagation algorithm (GCAOBP). A genetic algorithm (GA) was then applied to the trained ANN model to predict the gridding precision. The integrated GCAOBP-GA algorithm was successful in predicting the Root Mean Square of profile error (RMS) of optical aspheric workpiece in parallel grinding method using machining parameters. The results of experiments have shown that RMS of machined workpiece in parallel grinding can be predicted effectively through this approach.
Optimal groundwater remediation using artificial neural networks and the genetic algorithm
Rogers, L.L.
1992-01-01
An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the trained network searches through realizations or patterns of pumping selected by the GA, predicting the outcome. This approach has advantages of parallel processing of the groundwater simulations and the ability to [open quotes]recycle[close quotes] or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models. The ANN technology, inspired by neurobiological theories of massive interconnection and parallelism, has been applied to a variety of optimization problems. In the ANN groundwater management approach presented here, the behavior of complex groundwater scenarios with spatially-variable transport parameters and multiple contaminant plumes are simulated with 2-D flow and transport codes. An ANN is trained upon a set of examples developed from groundwater simulations. The input of the ANN characterizes the different realizations of pumping. The output characterizes the objectives and constraints of the optimization, such as whether regulatory goals have been met, value of cost functions or cleanup time, and mass of contaminant removal. The supervised learning algorithm of backpropagation is used to train the network. The conjugate gradient method and weight-elimination procedures are used to speed convergence and improve performance, respectively. Then a search is made through possible pumping realizations to find optimal realizations.
Probabilistic Model Building Genetic Programming based on Estimation of Bayesian Network
NASA Astrophysics Data System (ADS)
Hasegawa, Yoshihiko; Iba, Hitoshi
Genetic Programming (GP) is a powerful optimization algorithm, which employs the crossover for genetic operation. Because the crossover operator in GP randomly selects sub-trees, the building blocks may be destroyed by the crossover. Recently, algorithms called PMBGPs (Probabilistic Model Building GP) based on probabilistic techniques have been proposed in order to improve the problem mentioned above. We propose a new PMBGP employing Bayesian network for generating new individuals with a special chromosome called expanded parse tree, which much reduces a number of possible symbols at each node. Although the large number of symbols gives rise to the large conditional probability table and requires a lot of samples to estimate the interactions among nodes, a use of the expanded parse tree overcomes these problems. Computational experiments on two subjects demonstrate that our new PMBGP is much superior to prior probabilistic models.
An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.
Yoon, Yourim; Kim, Yong-Hyuk
2013-10-01
Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.
ERIC Educational Resources Information Center
Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.
2012-01-01
Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…
Solution of basic tasks in eclipsing binary period analysis by genetic and LSM algorithms
NASA Astrophysics Data System (ADS)
Chrastina, M.; Mikulášek, Z.; Zejda, M.
2014-03-01
A period analysis of eclipsing binaries can be performed effectively when using fine-tuned phenomenological models. The combination of a regression analysis and genetic algorithms is a powerful tool for such astrophysical tasks as light curve analysis, mid-eclipse time determination and O-C diagram investigation — even the apsidal motion and the light time effect can be resolved.
Automatic 3D image registration using voxel similarity measurements based on a genetic algorithm
NASA Astrophysics Data System (ADS)
Huang, Wei; Sullivan, John M., Jr.; Kulkarni, Praveen; Murugavel, Murali
2006-03-01
An automatic 3D non-rigid body registration system based upon the genetic algorithm (GA) process is presented. The system has been successfully applied to 2D and 3D situations using both rigid-body and affine transformations. Conventional optimization techniques and gradient search strategies generally require a good initial start location. The GA approach avoids the local minima/maxima traps of conventional optimization techniques. Based on the principles of Darwinian natural selection (survival of the fittest), the genetic algorithm has two basic steps: 1. Randomly generate an initial population. 2. Repeated application of the natural selection operation until a termination measure is satisfied. The natural selection process selects individuals based on their fitness to participate in the genetic operations; and it creates new individuals by inheritance from both parents, genetic recombination (crossover) and mutation. Once the termination criteria are satisfied, the optimum is selected from the population. The algorithm was applied on 2D and 3D magnetic resonance images (MRI). It does not require any preprocessing such as threshold, smoothing, segmentation, or definition of base points or edges. To evaluate the performance of the GA registration, the results were compared with results of the Automatic Image Registration technique (AIR) and manual registration which was used as the gold standard. Results showed that our GA implementation was a robust algorithm and gives very close results to the gold standard. A pre-cropping strategy was also discussed as an efficient preprocessing step to enhance the registration accuracy.
Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida
By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.
Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed
NASA Technical Reports Server (NTRS)
Rakoczy, John; Steincamp, James; Taylor, Jaime
2003-01-01
A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.
By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...
Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method
ERIC Educational Resources Information Center
Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen
2008-01-01
In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…
Technology Transfer Automated Retrieval System (TEKTRAN)
In this paper, the Genetic Algorithms (GA) and Bayesian model averaging (BMA) were combined to simultaneously conduct calibration and uncertainty analysis for the Soil and Water Assessment Tool (SWAT). In this hybrid method, several SWAT models with different structures are first selected; next GA i...
Credit card fraud detection: An application of the gene expression messy genetic algorithm
Kargupta, H.; Gattiker, J.R.; Buescher, K.
1996-05-01
This paper describes an application of the recently introduced gene expression messy genetic algorithm (GEMGA) (Kargupta, 1996) for detecting fraudulent transactions of credit cards. It also explains the fundamental concepts underlying the GEMGA in the light of the SEARCH (Search Envisioned As Relation and Class Hierarchizing) (Kargupta, 1995) framework.
Genetic Algorithm-Based Relevance Feedback for Image Retrieval Using Local Similarity Patterns.
ERIC Educational Resources Information Center
Stejic, Zoran; Takama, Yasufumi; Hirota, Kaoru
2003-01-01
Proposes local similarity pattern (LSP) as a new method for computing digital image similarity. Topics include optimizing similarity computation based on genetic algorithm; relevance feedback; and an evaluation of LSP on five databases that showed an increase in retrieval precision over other methods for computing image similarity. (Author/LRW)
Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872