Evolutionary search framework based on the information geometry
| Project/Area Number |
25880012
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Single-year Grants |
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| Research Field |
Soft computing
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| Research Institution | Shinshu University |
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Principal Investigator |
AKIMOTO Yohei 信州大学, 学術研究院工学系, 助教 (20709654)
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| Project Period (FY) |
2013-08-30 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 確率的探索法 / 進化計算 / 情報幾何 / 確率近似 / 高次元最適化 / 制約付き最適化 / ノイズ付き最適化 / 勾配推定 / 確率的最適化法 / 国際情報交換 (INRIA, フラ ンス) / Markov Chain 解析 / 国際情報交換 (INRIA, フランス) |
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| Outline of Final Research Achievements |
In this work, we have studied the Information-Geometric Optimization (IGO) that is a recently proposed framework of probability model based search algorithm for arbitrary optimization problems based on the information geometry. We have designed a novel variant of the covariance matrix adaptation evolution strategy for high dimensional continuous optimization, a new algorithm for constraint continuous optimization, both of which are based on the IGO principle. We have also studied the runtime of a general comparison based algorithm with fitness averaging strategy on noisy discrete optimization problems and derived the required number of fitness resampling.
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Report
(3 results)
Research Products
(11 results)
