Evolutionary Algorithm Using Marginal Histogram Models in Continuous Domain
作者： Martin Pelikan , David E. Goldberg , Shigeyoshi Tsutsui
DOI:
关键词: Mutation operator 、 Histogram 、 Algorithm 、 Probability density function 、 Evolutionary algorithm 、 Mathematics 、 Mathematical optimization 、 Probabilistic logic 、 Domain (mathematical analysis) 、 Global optimum 、 Population
摘要: 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 estimated probability density model of parents instead using recombination and mutation operators. paper, we propose an algorithm marginal histogram parent continuous domain. We two types models: fixed-width (FWH) fixed-height (FHH). The results showed that both models worked fairly well test functions with no or weak interactions among variables. Especially, FHH could find global optimum very high accuracy effectively good scale-up problem size.
researchgate.net 参考文章(16)
H. Satoh, Minimal generation gap model for GAs considering both exploration and exploitation soft computing. pp. 494- 497 ,(1996)
D. Thierens, P.A.N. Bosman, An algorithmic framework for density estimation based evolutionary algorithms Utrecht University: Information and Computing Sciences. ,(1999)
Isabelle Servet, Louise Travé-Massuyès, Daniel Stern, Telephone network traffic overloading diagnosis and evolutionary computation techniques Lecture Notes in Computer Science. pp. 137- 144 ,(1998) , 10.1007/BFB0026596
Michèle Sebag, Antoine Ducoulombier, Extending population-based incremental learning to continuous search spaces Lecture Notes in Computer Science. pp. 418- 427 ,(1998) , 10.1007/BFB0056884
D. Thierens, P.A.N. Bosman, Continuous iterated density estimation evolutionary algorithms within the IDEA framework ,(2000)
Larry J. Eshelman, J. David Schaffer, Real-Coded Genetic Algorithms and Interval-Schemata foundations of genetic algorithms. ,vol. 2, pp. 187- 202 ,(1993) , 10.1016/B978-0-08-094832-4.50018-0
James E. Baker, Reducing bias and inefficiency in the selection algorithm international conference on genetic algorithms. pp. 14- 21 ,(1987)
Shumeet Baluja, Scott Davies, Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space ,(1997)
H. Mühlenbein, G. Paaß, From Recombination of Genes to the Estimation of Distributions I. Binary Parameters parallel problem solving from nature. pp. 178- 187 ,(1996) , 10.1007/3-540-61723-X_982
Martin Pelikan, David E. Goldberg, Erick Cantú-Paz, Linkage Problem, Distribution Estimation, and Bayesian Networks Evolutionary Computation. ,vol. 8, pp. 311- 340 ,(2000) , 10.1162/106365600750078808