Group extinction heuristics in evolution strategy

  • Authors:

  • Chun-Kit Au;Ho-Fung Leung

  • Affiliations:

  • Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China;Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China

  • Venue:
  • CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
  • Year:
  • 2009

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Abstract

In this paper, we propose a new heuristics called "group extinction". The heuristics is inspired by the existence of the extinction in the nature that groups of individuals, which have been consuming a large amount of the ecological resources, are not always the best groups in the evolutionary process. Ideally, these groups should be forced to become extinct such that the resources they use can be released to the other individuals or groups. In the context of optimization, the motivation of using the group extinction is to reduce the computational resources used by groups of candidate solutions that do not have any significant contribution to the overall performances of the optimization algorithms. The proposed heuristics is tested in the well-known framework of evolution strategy and their performances on the common unimodal and multimodal optimization problems are investigated. Experimental results show that using the group extinction heuristics can significantly reduce the average numbers of function evaluations to reach the optima, in particular when large populations are used.