Titlebook: Genetic and Evolutionary Computation — GECCO 2003; Genetic and Evolutio Erick Cantú-Paz,James A. Foster,Julian Miller Conference proceeding

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书目名称Genetic and Evolutionary Computation — GECCO 2003影响因子(影响力)




书目名称Genetic and Evolutionary Computation — GECCO 2003影响因子(影响力)学科排名




书目名称Genetic and Evolutionary Computation — GECCO 2003网络公开度




书目名称Genetic and Evolutionary Computation — GECCO 2003网络公开度学科排名




书目名称Genetic and Evolutionary Computation — GECCO 2003被引频次




书目名称Genetic and Evolutionary Computation — GECCO 2003被引频次学科排名




书目名称Genetic and Evolutionary Computation — GECCO 2003年度引用




书目名称Genetic and Evolutionary Computation — GECCO 2003年度引用学科排名




书目名称Genetic and Evolutionary Computation — GECCO 2003读者反馈




书目名称Genetic and Evolutionary Computation — GECCO 2003读者反馈学科排名

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Reinforcement Learning Estimation of Distribution Algorithmpromising solutions to generate a new population of solutions. We call it Reinforcement Learning Estimation of Distribution Algorithm (RELEDA). For the estimation of the joint probability distribution we consider each variable as univariate. Then we update the probability of each variable by applyin

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Hierarchical BOA Solves Ising Spin Glasses and MAXSAT anything easier. This paper applies hBOA to two important classes of real-world problems: Ising spin-glass systems and maximum satisfiability (MAXSAT). The paper shows how easy it is to apply hBOA to real-world optimization problems—in most cases hBOA can be applied without any prior problem analys

NOTCH

ERA: An Algorithm for Reducing the Epistasis of SAT Problemsroduces a more suited representation (with lower epistasis) for a Genetic Algorithm (GA) by preprocessing the original SAT problem; and b) A Genetic Algorithm that solves the preprocesed instances..ERA is implemented by a simulated annealing algorithm (SA), which transforms the original SAT problem

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Learning a Procedure That Can Solve Hard Bin-Packing Problems: A New GA-Based Approach to Hyper-heurnge of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messy-GA-based approach that learns such a heuristic combination for solving one-dimensional bin-packing problems. When applied to a large set of benchmark pro

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Population Sizing for the Redundant Trivial Voting Mappingundant representation for binary phenotypes. A population sizing model is presented that quantitatively predicts the influence of the TV mapping and variants of this encoding on the performance of GAs. The results indicate that when using this encoding GA performance depends on the influence of the

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Non-stationary Function Optimization Using Polygenic Inheritanceing target, and tend to converge on a local optimum that appears early in a run..It is generally accepted that diploid GAs can cope with these problems because they have a ., that is, genes that may be required in the future are maintained in the current population. This paper describes a haploid GA

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Scalability of Selectorecombinative Genetic Algorithms for Problems with Tight Linkage the BB mixing time and the population sizing dictated by BB mixing for single-point crossover. The population-sizing model suggests that for moderate-to-large problems, BB mixing – instead of BB decision making and BB supply – bounds the population size required to obtain a solution of constant qua

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New Entropy-Based Measures of Gene Significance and Epistasissuggest three epistasis-related measures: gene significance, gene epistasis, and problem epistasis. The measures are believed to be helpful to investigate both the individual epistasis of a gene group and the overall epistasis that a problem has. The experimental results on various well-known proble

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