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oai:hiroshima.repo.nii.ac.jp:02000871 2025-02-18T02:56:03Z 1730444917621

An Extended ISM for Globally Multimodal Function Optimization by Genetic Algorithms Karatsu, Naoya Karatsu Naoya Nagata, Yuichi Nagata Yuichi Ono, Isao Ono Isao Kobayashi, Shigenobu Kobayashi Shigenobu open access (c) Copyright by IEEE SMC Hiroshima Chapter. 500 When attempting to optimize a function where exists several big-valley structures, conventional GAs often fail to find the global optimum. Innately Split Model (ISM) is a framework of GAs, which is designed to avoid this phenomenon called UV -Phenomenon. However, ISM doesn't care about previouslysearched areas by the past populations. Thus, it is possible that populations of ISM waste evaluation cost for redundant searches reaching previously-found optima. In this paper, we introduce Extended ISM (EISM) that uses search information of past populations as trap to suppress overlapping searches. To show performance of EISM, we apply it to some test functions, and analyze the behavior. IEEE SMC Hiroshima Chapter 2009-11 eng conference paper VoR https://hiroshima.repo.nii.ac.jp/records/2000871 http://www.hil.hiroshima-u.ac.jp/iwcia/2009/ 1883-3977 5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009 5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009 284 284 289 https://hiroshima.repo.nii.ac.jp/record/2000871/files/A1206.pdf 746.9 KB