An experimental study of benchmarking functions for genetic algorithms

被引：107

作者：

Digalakis, JG ^{[1
]}

Margaritis, KG ^{[1
]}

机构：

[1] Univ Macedonia, Dept Appl Informat, Thessaloniki, Greece

来源：

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2002年 / 79卷 / 04期

关键词：

genetic algorithms performance; generational replacement model; steady-state replacement model; population size; pseudo-random number generators; ISAAC PNG;

D O I：

10.1080/00207160210939

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper presents a review and experimental results oil the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the eect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

引用

页码：403 / 416

页数：14

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