Results 1  10
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19
Tackling RealCoded Genetic Algorithms: Operators and Tools for Behavioural Analysis
 Artificial Intelligence Review
, 1998
"... . Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of ..."
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Cited by 123 (24 self)
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. Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of realcoded genetic algorithms. Different models of genetic operators and some me...
Finite Markov Chain Results in Evolutionary Computation: A Tour d'Horizon
, 1998
"... . The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms ..."
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Cited by 57 (2 self)
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. The theory of evolutionary computation has been enhanced rapidly during the last decade. This survey is the attempt to summarize the results regarding the limit and finite time behavior of evolutionary algorithms with finite search spaces and discrete time scale. Results on evolutionary algorithms beyond finite space and discrete time are also presented but with reduced elaboration. Keywords: evolutionary algorithms, limit behavior, finite time behavior 1. Introduction The field of evolutionary computation is mainly engaged in the development of optimization algorithms which design is inspired by principles of natural evolution. In most cases, the optimization task is of the following type: Find an element x 2 X such that f(x ) f(x) for all x 2 X , where f : X ! IR is the objective function to be maximized and X the search set. In the terminology of evolutionary computation, an individual is represented by an element of the Cartesian product X \Theta A, where A is a possibly...
On the convergence of a class of estimation of distribution algorithms, conditionally
 IEEE Trans. Evol. Comput
"... Abstract—We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements. In this paper, we prove that: 1) i ..."
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Cited by 23 (6 self)
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Abstract—We investigate the global convergence of estimation of distribution algorithms (EDAs). In EDAs, the distribution is estimated from a set of selected elements, i.e., the parent set, and then the estimated distribution model is used to generate new elements. In this paper, we prove that: 1) if the distribution of the new elements matches that of the parent set exactly, the algorithms will converge to the global optimum under three widely used selection schemes and 2) a factorized distribution algorithm converges globally under proportional selection. Index Terms—Convergence, estimation of distribution algorithms (EDAs), factorized distribution algorithms (FDA). I.
Convergence of a hillclimbing genetic algorithm for graph matching
 Pattern Recognition
, 2000
"... This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorp ..."
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Cited by 12 (1 self)
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This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$ciently optimised using a hybrid genetic search procedure which incorporated a hillclimbing step. In the present study we return to the algorithm and provide some theoretical justi"cation for its observed convergence behaviour. The novelty of the analysis is to demonstrate analytically that the hillclimbing step signi"cantly accelerates convergence, and that the convergence rate is polynomial in the size of the nodeset of the graphs being matched. � 2000 Pattern Recognition
Extraction of audio features specific to speech production for multimodal speaker detection
 IEEE Trans. Multimedia
, 2008
"... Abstract—A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidate ..."
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Cited by 11 (2 self)
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Abstract—A method that exploits an information theoretic framework to extract optimized audio features using video information is presented. A simple measure of mutual information (MI) between the resulting audio and video features allows the detection of the active speaker among different candidates. This method involves the optimization of an MIbased objective function. No approximation is needed to solve this optimization problem, neither for the estimation of the probability density functions (pdfs) of the features, nor for the cost function itself. The pdfs are estimated from the samples using a nonparametric approach. The challenging optimization problem is solved using a global method: the differential evolution algorithm. Two information theoretic optimization criteria are compared and their ability to extract audio features specific to speech production is discussed. Using these specific audio features, candidate video features are then classified as member of the “speaker ” or “nonspeaker” class, resulting in a speaker detection scheme. As a result, our method achieves a speaker detection rate of 100 % on inhouse test sequences, and of 85 % on most commonly used sequences. Index Terms—Audio features, differential evolution, multimodal, mutual information, speaker detection, speech. I.
Empirical Modeling of Genetic Algorithms
 EVOLUTIONARY COMPUTATION
, 2001
"... This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a gra ..."
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Cited by 7 (1 self)
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This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graecolatin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models thus derived can be used first to determine optimal algorithm parameters, and second to shed light on interactions between the parameters and their relative importance. The initial models do not extrapolate well. However, an advantage of this approach is that the modelling process is under the control of the experimenter, and is hence very flexible. Refined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.
Search Space Boundary Extension Method in RealCoded Genetic Algorithms
 Information Sciences
, 2001
"... In realcoded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed a boundary extension methods which allows individuals to be located within a limited space beyond the bound ..."
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Cited by 7 (0 self)
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In realcoded genetic algorithms, some crossover operators do not work well on functions which have their optimum at the corner of the search space. To cope with this problem, we have proposed a boundary extension methods which allows individuals to be located within a limited space beyond the boundary of the search space. In this paper, we give an analysis of the boundary extension methods from the view point of sampling bias and perform a comparative study on the effect of applying two boundary extension methods, namely the boundary extension by mirroring BEM) and the boundary extension with extended selection (BES). We were able to confirm that to use sampling methods which have smaller sampling bias had good performance on both functions which have their optimum at or near the boundaries of the search space, and functions which have their optimum at the center of the search space. The BES/SD/A (BES by shortest distance selection with aging) had good performance on functions which have their optimum at or near the boundaries of the search space. We also confirmed that applying the BES/SD/A did not cause any performance degradation on functions which have their optimum at the center of the search space. 1.
Simplex Crossover and Linkage Identification: SingleStage Evolution VS. MultiStage Evolution
 in: Proceedings IEEE International Conference on Evolutionary Computation, 2002
, 2002
"... Previous studies have proposed simplex crossover (SPX) for realcoded GAs. In this paper, we propose two types of linkage identification for simplex crossover; linkage identification with singlestage evolution (LISS) and linkage identification with multistage evolution (LIMS), and perform their com ..."
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Cited by 6 (1 self)
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Previous studies have proposed simplex crossover (SPX) for realcoded GAs. In this paper, we propose two types of linkage identification for simplex crossover; linkage identification with singlestage evolution (LISS) and linkage identification with multistage evolution (LIMS), and perform their comparative study. Results showed LIMS has more stable performance than LISS. I.
Evolving walking: The anatomy of an evolutionary search
 FROM ANIMALS TO ANIMATS 8
, 2004
"... The evolution of a continuous time recurrent neural network central pattern generation for walking is characterized and found to proceed in two phases. The first phase spans the beginning of the search through the generation at which a “breakthrough” individual is discovered. The second phase procee ..."
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Cited by 6 (2 self)
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The evolution of a continuous time recurrent neural network central pattern generation for walking is characterized and found to proceed in two phases. The first phase spans the beginning of the search through the generation at which a “breakthrough” individual is discovered. The second phase proceeds from that generation forward. The first phase is most quickly completed if each succeeding population is as random as possible. Hence GA searches performed at lower mutation variances require more generations to discover a breakthrough individual than higher mutation variances searches. In the second phase the best fitness in the population most rapidly increases at low mutation variances. The role of parameter space structure in these trends will be examined.
Modeling Genetic Algorithms with Interacting Particle Systems
 In Theoretical Aspects of Evolutionary Computing
, 2001
"... We present in this work a natural Interacting Particle System (IPS) approach for modeling and studying the asymptotic behavior of Genetic Algorithms (GAs). In this model, a population is seen as a distribution (or measure) on the search space, and the Genetic Algorithm as a measure valued dynamical ..."
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Cited by 5 (0 self)
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We present in this work a natural Interacting Particle System (IPS) approach for modeling and studying the asymptotic behavior of Genetic Algorithms (GAs). In this model, a population is seen as a distribution (or measure) on the search space, and the Genetic Algorithm as a measure valued dynamical system. This model allows one to apply recent convergence results from the IPS literature for studying the convergence of genetic algorithms when the size of the population tends to infinity. We first review a number of approaches to Genetic Algorithms modeling and related convergence results. We then describe a general and abstract discrete time Interacting Particle System model for GAs, an we propose a brief review of some recent asymptotic results about the convergence of the NIPS approximating model (of finite Nsizedpopulation GAs) towards the IPS model (of infinite population GAs), including law of large number theorems, IL p mean and exponential bounds as well as large deviations...