Zi Kang Peng

Selective-Candidate Framework with Similarity Selection Rule for Evolutionary Optimization

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This paper proposes to resolve limitations of the traditional one-reproduction (OR) framework which produces only one candidate in a single reproduction procedure. A selective-candidate framework with similarity selection rule (SCSS) is suggested to make possible, a selective direction of search. In the SCSS framework, M (M > 1) candidates are generated from each current solution by independently conducting the reproduction procedure M times. The winner is then determined by employing a similarity selection rule. To maintain balanced exploitation and exploration capabilities, an efficient similarity selection rule based on the Euclidian distances between each of the M candidates and the corresponding current solution is proposed. The SCSS framework can be easily applied to any evolutionary algorithms or swarm intelligences. Experiments conducted with 60 benchmark functions show the superiority of SCSS over OR in three classic, four state-of-the-art and four up-to-date algorithms.

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