| Project/Area Number |
26730133
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Soft computing
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| Research Institution | Ryukoku University |
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Principal Investigator |
Ono Keiko 龍谷大学, 理工学部, 講師 (80550235)
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| Research Collaborator |
HANADA Yoshiko 関西大学, システム理工学部, 准教授 (30511711)
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| Project Period (FY) |
2014-04-01 – 2016-03-31
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| Project Status |
Completed (Fiscal Year 2015)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2014: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 遺伝的プログラミング / 並列モデル / 遺伝的多様性 / ネットワーククラスタリング / 遺伝的アルゴリズム / 遺伝的プロウグラミング / 探索バイアス / 木の形質 / 類似度 / ネットワーク |
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| Outline of Final Research Achievements |
A parallel model encourages genetic diversity and frequently shows a better search performance than do single population models. To enhance the parallel model, it is important to consider a balance between local and genetic search. In GP, individuals have various features, and, so it is difficult to determine which feature is the most effective. Therefore, we proposed a novel adaptive subpopulation model (cuSASGP). The proposed method automatically generates a correlation network on the basis of the difference between individuals in terms of not only a fitness value but also node size and generates subpopulations by network clustering.
Using three benchmark problems, we demonstrate that performance improvement can be achieved, and that the proposed method significantly outperforms a typical method. Moreover, we verify that genetic diversity can be achieved by adopting subpopulation models such as the island method and cuSASGP in the lighting control problem.
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