家族横断選択を取り入れたMGGの提案とDS-GAへの適用による解探索性能の向上

概要

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MGG (Minimal Generation Gap) is one of popular generation alternation models for Genetic Algorithms (GAs). The conventional MGG is effective for single population GAs, but not for multi-population GAs. This paper proposes ``MGG with global selection (MGGGS) that is designed for multi-population GAs. In MGGGS, the selection operation is carried out through the whole population, while the crossover operation is restricted in sub-populations. Experiments are carried out to analyze the characteristics of MGGGS with Dynamically Separating Genetic Algorithm (DS-GA). In DS-GA the sub-populations are reconstructed during the evolution, which is suitable for MGGGS. The experimental results show that MGGGS outperforms the conventional MGG especially for multimodal functions, since sub-populations explore various areas by MGGGS.