A study on combination of differential evolution and evolution strategy
Keiichiro Yasuda, Kengo Makise, Kenichi Tamura
Abstract
Keiichiro Yasuda, Kengo Makise, Kenichi Tamura
Abstract
In this paper, a new optimization method based on a combination of Differential Evolution (DE) and Evolution Strategy (ES), which belong to both Meta-Heuristics and Evolutionary Computation, are developed as a fast approximation optimization method. A weak point of DE is weak local search ability and considerable computation time for obtaining a good approximate solution. The proposed method aims at compensation for the weak points of DE by connecting ES with strong local search capacity. The effectiveness of the proposed method is confirmed through the numerical experiments.
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In this paper, a new optimization method based on a combination of Differential Evolution (DE) and Evolution Strategy (ES), which belong to both Meta-Heuristics and Evolutionary Computation, are developed as a fast approximation optimization method. A weak point of DE is weak local search ability and considerable computation time for obtaining a good approximate solution. The proposed method aims at compensation for the weak points of DE by connecting ES with strong local search capacity. The effectiveness of the proposed method is confirmed through the numerical experiments.
Key concepts: Differential evolution, Evolutionary computation, Mathematical optimization, Heuristics, Computation, Evolution strategy, Local search (optimization), Computer science