Presentation 2006-11-13
Improvement of Efficiency of GP using Cross-Cultural Island Model
Yuichi HARA, Akihiro KANAGAWA, Hitoshi YAMAUCHI, Hiromitsu TAKAHASHI,
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Abstract(in English) Recently, one of parallel model of Genetic Algorithms (GAs) which called island model occupies the attention of the researchers. This model enables GAs to maintain variousness of chromosome in GAs. Genetic Programmings (GPs) are one of development from of GAs. GPs can treat a chromosome as a list structure. There are few studies os the island model in GPs compared with GAs, and it is reported that effect is not necesarily better than GAs by the previous studies. In this report, we proposed an island model for GPs. The number of island is limited to two. One island is esteemed for fitneses, and the other island is esteemed for variousness. Hence, we call this model cross-cultural island model. Effectiveness of this model is shown by some problems including of the Traveling Salesman Problem.
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Keyword(in English) Genetic Programming / Island Model / Fitness / Variousness
Paper # NLP2006-67
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Committee NLP
Conference Date 2006/11/6(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Improvement of Efficiency of GP using Cross-Cultural Island Model
Sub Title (in English)
Keyword(1) Genetic Programming
Keyword(2) Island Model
Keyword(3) Fitness
Keyword(4) Variousness
1st Author's Name Yuichi HARA
1st Author's Affiliation Grauate School of System Engineering, Okayama Prefectural University()
2nd Author's Name Akihiro KANAGAWA
2nd Author's Affiliation Faculty of Computer Science and System Engineering, Okayama Prefectural University
3rd Author's Name Hitoshi YAMAUCHI
3rd Author's Affiliation Faculty of Computer Science and System Engineering, Okayama Prefectural University
4th Author's Name Hiromitsu TAKAHASHI
4th Author's Affiliation Faculty of Computer Science and System Engineering, Okayama Prefectural University
Date 2006-11-13
Paper # NLP2006-67
Volume (vol) vol.106
Number (no) 344
Page pp.pp.-
#Pages 6
Date of Issue