A parallel genetic algorithm with distributed environment scheme
作者： M. Miki , T. Hiroyasu , M. Kaneko , K. Hatanaka
DOI: 10.1109/ICSMC.1999.814176
关键词: Function (mathematics) 、 Parallel algorithm 、 Crossover 、 Mutation rate 、 Mutation (genetic algorithm) 、 Population 、 Population size 、 Distributed Computing Environment 、 Mathematical optimization 、 Genetic algorithm 、 Computer science
摘要: Introduces an alternative approach to relieving the task of choosing optimal mutation and crossover rates by using a parallel distributed GA with environments. It is shown that best depend on population sizes problems, those are different between single multiple populations. The proposed environment uses various combination parameters as fixed values in subpopulations. excellent performance new scheme experimentally recognized for standard test function. concluded fastest way gain good solution under given size uncertainty appropriate rates.
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