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28
Bayesian Optimization Algorithm: From Single Level to Hierarchy
, 2002
"... There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decompositi ..."
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Cited by 98 (18 self)
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There are four primary goals of this dissertation. First, design a competent optimization algorithm capable of learning and exploiting appropriate problem decomposition by sampling and evaluating candidate solutions. Second, extend the proposed algorithm to enable the use of hierarchical decomposition as opposed to decomposition on only a single level. Third, design a class of difficult hierarchical problems that can be used to test the algorithms that attempt to exploit hierarchical decomposition. Fourth, test the developed algorithms on the designed class of problems and several realworld applications. The dissertation proposes the Bayesian optimization algorithm (BOA), which uses Bayesian networks to model the promising solutions found so far and sample new candidate solutions. BOA is theoretically and empirically shown to be capable of both learning a proper decomposition of the problem and exploiting the learned decomposition to ensure robust and scalable search for the optimum across a wide range of problems. The dissertation then identifies important features that must be incorporated into the basic BOA to solve problems that are not decomposable on a single level, but that can still be solved by decomposition over multiple levels of difficulty. Hierarchical
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 30 (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.
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 25 (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
DE/EDA: A new evolutionary algorithm for global optimization
 Information Sciences
, 2005
"... Differential Evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of Distribution Algorithm (EDA) samples new solutions from a probability mo ..."
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Cited by 19 (3 self)
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Differential Evolution (DE) was very successful in solving the global continuous optimization problem. It mainly uses the distance and direction information from the current population to guide its further search. Estimation of Distribution Algorithm (EDA) samples new solutions from a probability model which characterizes the distribution of promising solutions. This paper proposes a combination of DE and EDA (DE/EDA) for the global continuous optimization problem. DE/EDA combines global information extracted by EDA with differential information obtained by DE to create promising solutions. DE/EDA has been compared with the best version of the DE algorithm and an EDA on several commonly utilized test problems. Experimental results demonstrate that DE/EDA outperforms the DE algorithm and the EDA. The effect of the parameters of DE/EDA to its performance is investigated experimentally.
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 15 (9 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
An introduction and survey of estimation of distribution algorithms
 SWARM AND EVOLUTIONARY COMPUTATION
, 2011
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A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
, 2007
"... This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms ..."
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Cited by 6 (0 self)
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This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.
Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model
 In Proceedings of Parallel Problem Solving from Nature VIII
, 2004
"... Abstract. In evolutionary continuous optimization by building and using probabilistic models, the multivariate Gaussian distribution and their variants or extensions such as the mixture of Gaussians have been used popularly. However, this Gaussian assumption is often violated in many real problems. ..."
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Cited by 4 (0 self)
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Abstract. In evolutionary continuous optimization by building and using probabilistic models, the multivariate Gaussian distribution and their variants or extensions such as the mixture of Gaussians have been used popularly. However, this Gaussian assumption is often violated in many real problems. In this paper, we propose a new continuous estimation of distribution algorithms (EDAs) with the variational Bayesian independent component analyzers mixture model (vbICAMM) for allowing any distribution to be modeled. We examine how this sophisticated density estimation technique has influence on the performance of the optimization by employing the same selection and population alternation schemes used in the previous EDAs. Our experimental results support that the presented EDAs achieve better performance than previous EDAs with ICA and Gaussian mixture or kernelbased approaches. 1
Adaptive discretization for probabilistic model building genetic algorithms
 In Proceedings of ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO2006
, 2006
"... This paper proposes an adaptive discretization method, called SplitonDemand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integ ..."
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Cited by 4 (2 self)
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This paper proposes an adaptive discretization method, called SplitonDemand (SoD), to enable the probabilistic model building genetic algorithm (PMBGA) to solve optimization problems in the continuous domain. The procedure, effect, and usage of SoD are described in detail. As an example, the integration of SoD and the extended compact genetic algorithm (ECGA), named realcoded ECGA (rECGA), is presented and numerically examined. The experimental results indicate that rECGA works well and SoD is effective. The behavior of SoD is analyzed and discussed, followed by the potential future work for SoD. 1
Combinations of Estimation of Distribution Algorithms and Other Techniques
"... Abstract: This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we hav ..."
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Cited by 3 (2 self)
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Abstract: This paper summaries our recent work on combining estimation of distribution algorithms (EDA) and other techniques for solving hard search and optimization problems: a) guided mutation, an offspring generator in which the ideas from EDAs and genetic algorithms are combined together, we have shown that an evolutionary algorithm with guided mutation outperforms the best GA for the maximum clique problem, b) evolutionary algorithms refining a heuristic, we advocate a strategy for solving a hard optimization problem with complicated data structure, and c) combination of two different local search techniques and EDA for numerical global optimization problems, its basic idea is that not all the new generated points are needed to be improved by an expensive local search. Keywords: Estimation distribution algorithm, guided mutation, memetic algorithms, global optimization.