Self-learning genetic algorithm

被引：11

作者：

Kostenko, V. A. ^{[1
]}

Frolov, A. V. ^{[1
]}

机构：

[1] Moscow MV Lomonosov State Univ, Moscow 119991, Russia

来源：

JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL | 2015年 / 54卷 / 04期

关键词：

Iterative methods - Combinatorial optimization - Problem solving - Learning algorithms;

D O I：

10.1134/S1064230715040103

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a self-learning genetic algorithm for solving combinatorial optimization problems is considered. The self-learning consists in changing the values of the probabilities of crossing and mutation in accordance with changing the value of the fitness function after operations in the next iteration of the algorithm. The results of comparing the proposed algorithm with the Holland algorithm by the problems of multiprocessor job scheduling and subset sum problem are presented.

引用

页码：525 / 539

页数：15

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