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34
Tackling realcoded genetic algorithms: operators and tools for the behavioural analysis
 Arti Intelligence Reviews
, 1998
"... Abstract. 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 prin ..."
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Cited by 178 (26 self)
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Abstract. 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 mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared. Key words: genetic algorithms, real coding, continuous search spaces Abbreviations: GAs – genetic algorithms; BCGA – binarycoded genetic algorithm; RCGA – realcoded genetic algorithm
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 68 (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 33 (8 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.
Inexact graph matching using genetic search
 Pattern Recognition
, 1997
"... AbstractThis paper describes a framework for performing relational graph matching using genetic search. There are three novel ingredients to the work. Firstly, we cast the optimisation process into a Bayesian framework by exploiting the recently reported global consistency measure of Wilson and Ha ..."
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Cited by 28 (3 self)
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AbstractThis paper describes a framework for performing relational graph matching using genetic search. There are three novel ingredients to the work. Firstly, we cast the optimisation process into a Bayesian framework by exploiting the recently reported global consistency measure of Wilson and Hancock as a fitness measure. The second novel idea is to realise the crossover process at the level of subgraphs, rather than employing stringbased or random crossover. Finally, we accelerate convergence by employing a deterministic hillclimbing process prior to selection. Since we adopt he Bayesian consistency measure as a fitness function, the basic measure of relational distance underpinning the technique isHamming distance. Our standpoint isthat genetic search provides a more attractive means of performing stochastic discrete optimisation on the global consistency measure than alternatives such as simulated annealing. Moreover, the action of the optimisation process is easily understood interms of its action in the Hamming distance domain. We demonstrate empirically not only that the method possesses polynomial convergence time but also that the convergence rate is more rapid than simulated annealing. We provide some experimental evaluation of the method in the matching of aerial stereograms and evaluate its sensitivity on synthetically generated graphs. ~ 1997 Pattern Recognition Society.
A novel iron loss reduction technique for distribution transformers based on a combined genetic algorithmneural network approach
 IEEE Trans. Syst., Man, Cybern. C
, 2001
"... Abstract—This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transf ..."
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Cited by 17 (7 self)
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Abstract—This paper presents an effective method to reduce the iron losses of wound core distribution transformers based on a combined neural network genetic algorithm approach. The originality of the work presented in this paper is that it tackles the iron loss reduction problem during the transformer production phase, while previous works were concentrated on the design phase. More specifically, neural networks effectively use measurements taken at the first stages of core construction in order to predict the iron losses of the assembled transformers, while genetic algorithms are used to improve the grouping process of the individual cores by reducing iron losses of assembled transformers. The proposed method has been tested on a transformer manufacturing industry. The results demonstrate the feasibility and practicality of this approach. Significant reduction of transformer iron losses is observed in comparison to the current practice leading to important economic savings for the transformer manufacturer. Index Terms—Core grouping process, decision trees, genetic algorithms, intelligent core loss modeling, iron loss reduction, neural networks. 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$ciently optimised using a hybrid genetic search procedure which incorp ..."
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Cited by 16 (1 self)
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This paper presents a convergence analysis for the problem of consistent labelling using genetic search. The work builds on a recent empirical study of graph matching where we showed that a Bayesian consistency measure could be e$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&quot;cation for its observed convergence behaviour. The novelty of the analysis is to demonstrate analytically that the hillclimbing step signi&quot;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 14 (1 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.
Evolutionary model type selection for global surrogate modeling
 2054 SURROGATE MODELING AND ADAPTIVE SAMPLING TOOLBOX FOR COMPUTER BASED DESIGN
"... Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualizati ..."
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Cited by 11 (3 self)
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Due to the scale and computational complexity of currently used simulation codes, global surrogate (metamodels) models have become indispensable tools for exploring and understanding the design space. Due to their compact formulation they are cheap to evaluate and thus readily facilitate visualization, design space exploration, rapid prototyping, and sensitivity analysis. They can also be used as accurate building blocks in design packages or larger simulation environments. Consequently, there is great interest in techniques that facilitate the construction of such approximation models while minimizing the computational cost and maximizing model accuracy. Many surrogate model types exist (Support Vector Machines, Kriging, Neural Networks, etc.) but no type is optimal in all circumstances. Nor is there any hard theory available that can help make this choice. In this paper we present an automatic approach to the model type selection problem. We describe an adaptive global surrogate modeling environment with adaptive sampling, driven by speciated evolution. Different model types are evolved cooperatively using a Genetic Algorithm (heterogeneous evolution) and compete to approximate the iteratively selected data. In this way the optimal model type and complexity for a given data set or simulation code can be dynamically determined. Its utility and performance is demonstrated on a number of problems where it outperforms traditional sequential execution of each model type.
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 8 (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.
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 8 (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.