Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms-Reference-Cited by-同舟云学术

Dynamically Adjusting Migration Rates for Multi-Population Genetic Algorithms

Published:2007-04-20 Issue:4 Volume:11 Page:410-415
ISSN:1883-8014
Container-title:Journal of Advanced Computational Intelligence and Intelligent Informatics
language:en
Short-container-title:JACIII

Author:

Hong Tzung-Pei, ,Lin Wen-Yang,Liu Shu-Min,Lin Jiann-Horng,

Abstract

In this paper, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration rates on the performance and solution quality of MGAs. Thereby, we propose an adaptive scheme to adjust the appropriate migration rates for MGAs. If the individuals from a neighboring sub-population can greatly improve the solution quality of a current population, then the migration from the neighbor has a positive effect. In this case, the migration rate from the neighbor should be increased; otherwise, it should be decreased. According to the principle, an adaptive multi-population genetic algorithm which can adjust the migration rates is proposed. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of our approach. The results of our work have illustrated the effectiveness of self-adaptation for MGAs and paved the way for this unexplored area.

Publisher

Fuji Technology Press Ltd.

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction

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