An Extended ISM for Globally Multimodal Function Optimization by Genetic Algorithms
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
Page 284-289
published_at 2009-11
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| File | |
| Title ( eng ) |
An Extended ISM for Globally Multimodal Function Optimization by Genetic Algorithms
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| Creator |
Karatsu Naoya
Nagata Yuichi
Ono Isao
Kobayashi Shigenobu
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| Source Title |
5th International Workshop on Computational Intelligence & Applications Proceedings : IWCIA 2009
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| Start Page | 284 |
| End Page | 289 |
| Abstract |
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.
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| NDC |
Technology. Engineering [ 500 ]
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| Language |
eng
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| Resource Type | conference paper |
| Publisher |
IEEE SMC Hiroshima Chapter
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| Date of Issued | 2009-11 |
| Rights |
(c) Copyright by IEEE SMC Hiroshima Chapter.
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| Publish Type | Version of Record |
| Access Rights | open access |
| Source Identifier |
[ISSN] 1883-3977
[URI] http://www.hil.hiroshima-u.ac.jp/iwcia/2009/
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