出版社：The Japanese Society for Artificial Intelligence
摘要：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.
关键词：multi-population GAs ; generation alternation models ; MGG ; global selection ; DS-GA
