Representative selection for cooperative co-evolutionary genetic algorithms

Authors:
Sun Xiao-yan;Gong Dun-wei;Hao Guo-sheng
Affiliations:
School of Information and Electrical Engineering, China University of Mining & Technology Xuzhou, Jiangsu, China;School of Information and Electrical Engineering, China University of Mining & Technology Xuzhou, Jiangsu, China;School of Information and Electrical Engineering, China University of Mining & Technology Xuzhou, Jiangsu, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 4
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Abstract

The performance of cooperative co-evolutionary genetic algorithms is highly affected by the representative selection strategy. But rational method is absent now. Oriented to the shortage, the representative selection strategy is studied based on the parallel implementation of cooperative co-evolutionary genetic algorithms in LAN. Firstly, the active cooperation ideology for representative selection and the dynamical determinate method on cooperation pool size are put forward. The methods for determining cooperation pool size, selecting cooperators and permuting cooperations are presented based on the evolutionary ability of sub-population and distributive performance of the individuals. Thirdly, the implementation steps are given. Lastly, the results of benchmark functions optimization show the validation of the method.