Extension and Performance Improvement of Estimation of Distribution Algorithms with Graph Kernels
| Project/Area Number |
17K00353
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Soft computing
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| Research Institution | Kindai University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | graph kernel / 進化計算 / 分布推定アルゴリズム / グラフカーネル / EDA-GK / ソフトコンピューティング |
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| Outline of Final Research Achievements |
The distribution of estimation algorithm EDA-GK using graph kernels is extended in this study. Conventionally, it has been difficult to achieve good performance in evolutionary algorithms using graphs as individuals because the mapping from genotype to phenotype is rugged. In this study, we aim to extend the application area of the algorithm. A mixed kernel is constructed and applied to graph identification problems with scale-free and small-world properties. Simultaneously, it is applied to the order/degree problem and the eigenvalue maximization problem, which are coped with in the field of graph theory.
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| Academic Significance and Societal Importance of the Research Achievements |
グラフの最適化は，社会ネットワークの分析や，新しい化合物の作成などに応用ができる．これまでにグラフカーネルを用いたアプローチでは分類問題などに適用されていたが，これを進化計算の枠組みにのせることにより，新しいものを創造・設計する問題へと拡張することができます．研究の最終年度では，理学科の先生と共同で，有機薄膜電池の化学式を案出する問題に取り組んでおり，本研究課題の提案手法を用いることでより高性能な電池が実現できることが期待されます．
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Report
(4 results)
Research Products
(7 results)
