| Summary: | 碩士 === 義守大學 === 資訊管理學系碩士班 === 93 === In recent years, multi-population genetic algorithms (MGAs) have been recognized as being more effective both in speed and solution quality than single-population genetic algorithms (SGAs). Despite of these advantages, the behavior and performance of MGAs, like SGAs, are still heavily affected by a judicious choice of parameters, such as connection topology, migration method, migration interval, migration rate and population number. In this thesis, the issue of adapting migration parameters for MGAs is investigated. We examine, in particular, the effect of adapting the migration interval as well as migration rate on the performance and solution quality of MGAs. Thereby, we propose several adaptive schemes to evolve appropriate migration intervals and migration rates for MGAs. Experiments on the 0/1 knapsack problem are conducted to show the effectiveness of our approaches. Though not yet comprehensive, the results of our work have illustrated the effectiveness of self-adaptation for MGAs and paved the way for this unexplored area.
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