Statistical mechanics theory of genetic algorithms

被引:0
|
作者
Shapiro, JL [1 ]
机构
[1] Univ Manchester, Dept Comp Sci, Manchester M13 9PL, Lancs, England
来源
THEORETICAL ASPECTS OF EVOLUTIONARY COMPUTING | 2001年
关键词
macroscopic dynamics; maximum entropy; statistical mechanics; crossover;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This tutorial gives an introduction to the statistical mechanics method of analysing genetic algorithm (GA) dynamics. The goals are to study GAs acting on specific problems which include realistic features such as: finite population effects, crossover, large search spaces, and realistic cost functions. Statistical mechanics allows one to derive deterministic equations of motion which describe average quantities of the population after selection, mutation, and crossover in terms of those before. The general ideas of this approach are described here, and some details given via consideration of a specific problem. Finally, a description of the literature is given.
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页码:87 / 108
页数:22
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