Combinations of estimation of distribution algorithms and other techniques

被引：26

作者：

Zhang Q. ^{[1
]}

Sun J. ^{[2
]}

Tsang E. ^{[1
]}

机构：

[1] Department of Computer Science, University of Essex, Colchester CO4 3SQ, Wivenhoe Park

[2] School of Computer Science, University of Birmingham

来源：

International Journal of Automation and Computing | 2007年 / 4卷 / 3期

关键词：

Estimation distribution algorithm; Global optimization; Guided mutation; Memetic algorithms;

D O I：

10.1007/s11633-007-0273-3

中图分类号：

学科分类号：

摘要：

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. © 2007 Institute of Automation, Chinese Academy of Sciences.

引用

页码：273 / 280

页数：7

共 30 条

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