Statistical analysis of the main parameters involved in the design of a genetic algorithm

被引：78

作者：

Rojas, I ^{[1
]}

González, J ^{[1
]}

Pomares, H ^{[1
]}

Merelo, JJ ^{[1
]}

Castillo, PA ^{[1
]}

Romero, G ^{[1
]}

机构：

[1] Univ Granada, Dept Comp Architecture & Technol, E-18071 Granada, Spain

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS | 2002年 / 32卷 / 01期

关键词：

ANalysis Of the VAriance (ANOVA); genetic algorithm (GA); statistical analysis;

D O I：

10.1109/TSMCC.2002.1009128

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most genetic algorithm (GA) users adjust the main parameters of the design of a GA (crossover and mutation probability, population size, number of generations, crossover, mutation, and selection opera. tors) manually. Nevertheless, when GA applications are being developed it is very important to know which parameters have the greatest influence on the behavior and performance of a GA. The purpose of this study was to analyze the dynamics of GAs when confronted with modifications to the principal parameters that define them, taking into account the two main characteristics of GAs; their capacity for exploration and exploitation. Therefore, the dynamics of GAs have been analyzed from two viewpoints. The first is to study the best solution found by the system, i.e., to observe its capacity to obtain a local or global optimum. The second viewpoint is the diversity within the population of GAs; to examine this, the average fitness was calculated. The relevancy and relative importance of the parameters involved in GA design are investigated by using a powerful statistical tool, the ANalysis Of the VAriance (ANOVA).

引用

页码：31 / 37

页数：7

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