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101
Linkage Identification by Nonmonotonicity Detection for Overlapping Functions
 Evolutionary Computation
, 1999
"... This paper presents the linkage identification by nonmonotonicity detection (LIMD) procedure and its extension for overlapping functions by introducing the tightness detection (TD) procedure. The LIMD identifies linkage groups directly by performing order2 simultaneous perturbations on a pair of l ..."
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Cited by 36 (11 self)
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This paper presents the linkage identification by nonmonotonicity detection (LIMD) procedure and its extension for overlapping functions by introducing the tightness detection (TD) procedure. The LIMD identifies linkage groups directly by performing order2 simultaneous perturbations on a pair of loci to detect monotonicity/nonmonotonicity of fitness changes. The LIMD can identify linkage groups with at most order of k when it is applied to O(2 k ) strings. The TD procedure calculates tightness of linkage between a pair of loci based on the linkage groups obtained by the LIMD. By removing loci with weak tightness from linkage groups, correct linkage groups are obtained for overlapping functions, which were considered difficult for linkage identification procedures. 1 Introduction The power of genetic search lies in its processing of building blocks (BBs)  essential subcomponents of solutions  through crossover and selection. Recent work (Goldberg, Deb, & Thierens, 1993; Thie...
Hierarchical Problem Solving by the Bayesian Optimization Algorithm
 PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE 2000
, 2000
"... The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimi ..."
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Cited by 34 (10 self)
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The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It defines the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimization algorithm (BOA) is extended in order to solve the proposed class of problems.
Genetic Algorithms, Clustering, and the Breaking of Symmetry
, 2000
"... This paper introduces clustering as a tool to improve the effects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is first clustered and the solutions in each of the clusters are processed separately. This alle ..."
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Cited by 27 (7 self)
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This paper introduces clustering as a tool to improve the effects of recombination and incorporate niching in evolutionary algorithms. Instead of processing the entire set of parent solutions, the set is first clustered and the solutions in each of the clusters are processed separately. This alleviates the problem of symmetry which is often a major difficulty of many evolutionary algorithms in combinatorial optimization. Furthermore, it incorporates niching into genetic algorithms and, for the first time, the probabilistic modelbuilding genetic algorithms. The dynamics and performance of the proposed method are illustrated on example problems.
Parallel estimation of distribution algorithms
, 2002
"... The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion ..."
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Cited by 26 (4 self)
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The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the generality of various probabilistic models by comparing the properties of underlying mappings. Also, some convergence issues are discussed and theoretical ways for further improvements are proposed. 2. Development of new probabilistic model and methods capable of dealing with continuous parameters. The resulting Mixed Bayesian Optimization Algorithm (MBOA) uses a set of decision trees to express the probability model. Its main advantage against the mostly used IDEA and EGNA approach is its backward compatibility with discrete domains, so it is uniquely capable of learning linkage between mixed continuousdiscrete genes. MBOA handles the discretization of continuous parameters as an integral part of the learning process, which outperforms the histogrambased
Neutrality: A necessity for selfadaptation
 In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002
, 2002
"... Abstract—Selfadaptation is used in all main paradigms of evolutionary computation to increase efficiency. We claim that the basis of selfadaptation is the use of neutrality. In the absence of external control neutrality allows a variation of the search distribution without the risk of fitness loss ..."
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Cited by 26 (5 self)
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Abstract—Selfadaptation is used in all main paradigms of evolutionary computation to increase efficiency. We claim that the basis of selfadaptation is the use of neutrality. In the absence of external control neutrality allows a variation of the search distribution without the risk of fitness loss. I.
Identifying Linkage Groups by Nonlinearity/Nonmonotonicity Detection
 Proc. of the Genetic and Evolutionary Computation Conference
, 1999
"... This paper presents and discusses direct linkage identification procedures based on nonlinearity/nonmonotonicity detection. The algorithm we propose checks arbitrary nonlinearity/nonmonotonicity of fitness change by perturbations in a pair of loci to detect their linkage. We first discuss c ..."
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Cited by 24 (10 self)
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This paper presents and discusses direct linkage identification procedures based on nonlinearity/nonmonotonicity detection. The algorithm we propose checks arbitrary nonlinearity/nonmonotonicity of fitness change by perturbations in a pair of loci to detect their linkage. We first discuss condition of the linkage identification by nonlinearity check (LINC) procedure (Munetomo & Goldberg, 1998) and its allowable nonlinearity. Then we propose another condition of the linkage identification by nonmonotonicity detection (LIMD) and prove its equality to the LINC with allowable nonlinearity (LINCAN). The procedures can identify linkage groups for problems with at most orderk difficulty by checking O(2 k ) strings and the computational cost for each string is O(l 2 ) where l is the string length. 1 Introduction The definition of linkage in genetics is `the tendency for alleles of different genes to be passed together from one generation to the next' (Winter, Hickey...
On the Evolution of Phenotypic Exploration Distributions
 Foundations of Genetic Algorithms 7 (FOGA VII
, 2003
"... In nature, phenotypic variability is highly structured with respect to correlations between different phenotypic traits. In this paper we argue that this structuredness can be understood as the outcome of an adaptive process of phenotypic exploration distributions, similar to the adaptation of the s ..."
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Cited by 21 (8 self)
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In nature, phenotypic variability is highly structured with respect to correlations between different phenotypic traits. In this paper we argue that this structuredness can be understood as the outcome of an adaptive process of phenotypic exploration distributions, similar to the adaptation of the search distribution in heuristic search schemes or EstimationofDistribution Algorithms. The key ingredient of this process is a nontrivial genotypephenotype mapping: We rigorously define nontriviality, in which case neutral traits (as a generalization of strategy parameters) influence phenotype evolution by determining exploration distributions. Our main result is the description of the evolution of exploration distributions themselves in terms of an ordinary evolution equation. Accordingly, the ``fitness'' of an exploration distribution is proportional to its similarity (in the sense of the KullbackLeibler divergence) to the fitness distribution over phenotype space. Hence, exploration distributions evolve such that dependencies and correlations between phenotypic variables in selection are naturally adopted by the way evolution explores phenotype space.
Probabilistic ModelBuilding Genetic Algorithms in Permutation Representation Domain Using Edge Histogram
 Proc. of the 7th Int. Conf. on Parallel Problem Solving from Nature (PPSN VII
, 2002
"... Abstract. Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. ..."
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Cited by 19 (10 self)
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Abstract. Recently, there has been a growing interest in developing evolutionary algorithms based on probabilistic modeling. In this scheme, the offspring population is generated according to the estimated probability density model of the parent instead of using recombination and mutation operators. In this paper, we have proposed probabilistic modelbuilding genetic algorithms (PMBGAs) in permutation representation domain using edge histogram based sampling algorithms (EHBSAs). Two types of sampling algorithms, without template (EHBSA/WO) and with template (EHBSA/WT), are presented. The results were tested in the TSP and showed EHBSA/WT worked fairly well with a small population size in the test problems used. It also worked better than wellknown traditional twoparent recombination operators. 1
SelfAdaptive Exploration in Evolutionary Search
, 2001
"... We address a primary question of computational as well as biological research on evolution: How can an exploration strategy adapt in such a way as to exploit the information gained about the problem at hand? We first introduce an integrated formalism of evolutionary search which provides a unified v ..."
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Cited by 18 (6 self)
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We address a primary question of computational as well as biological research on evolution: How can an exploration strategy adapt in such a way as to exploit the information gained about the problem at hand? We first introduce an integrated formalism of evolutionary search which provides a unified view on different specific approaches. On this basis we discuss the implications of indirect modeling (via a ``genotypephenotype mapping'') on the exploration strategy. Notions such as modularity, pleiotropy and functional phenotypic complex are discussed as implications. Then, rigorously reflecting the notion of selfadaptability, we introduce a new definition that captures selfadaptability of exploration: different genotypes that map to the same phenotype may represent (also topologically) different exploration strategies; selfadaptability requires a variation of exploration strategies along such a ``neutral space''. By this definition, the concept of neutrality becomes a central concern of this paper. Finally, we present examples of these concepts: For a specific grammartype encoding, we observe a large variability of exploration strategies for a fixed phenotype, and a selfadaptive drift towards short representations with highly structured exploration strategy that matches the ``problem's structure''.
An introduction and survey of estimation of distribution algorithms
 SWARM AND EVOLUTIONARY COMPUTATION
, 2011
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