Differential evolution with self adaptive local search

Authors:
Nasimul Noman;Danushaka Bollegala;Hitoshi Iba
Affiliations:
University of Tokyo, Tokyo, Japan;University of Tokyo, Tokyo, Japan;University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

Citing 15
Cited 1

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Abstract

The performance of a memetic algorithm (MA) largely depends on the synergy between its global and local search counterparts. The amount of global exploration and local exploitation to be carried out, for optimal performance, varies with problem type. Therefore, an algorithm should intelligently allocate its computational efforts between genetic search and local search. In this work we propose an adaptive local search method that adjusts the effort for local tuning of individuals, taking feedback from the search. We implemented an MA hybridizing this adaptive local search method with differential evolution algorithm. Experimenting with a standard benchmark suite it was found that the proposed MA can utilize its global and local search components adaptively. The proposed algorithm also exhibited very competitive performance with other existing algorithms.