Development of an evolutionary algorithm for function optimization problems with an equality constraint and its application to the inference of genetic networks
| Project/Area Number |
23700266
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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 |
Sensitivity informatics/Soft computing
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| Research Institution | Tottori University |
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Principal Investigator |
KIMURA Shuhei 鳥取大学, 工学(系)研究科(研究院), 教授 (20342777)
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2013: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2012: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2011: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | 遺伝的アルゴリズム / 遺伝子ネットワーク同定 / S-systemモデル / Vohradskyモデル / 進化的アルゴリズム / 遺伝子ネットワーク / 制約付き関数最適化 / S-system |
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| Research Abstract |
In this study, I proposed a technique that makes evolutionary algorithms possible to solve function optimization problems with several inequality and a single equality constraints. The proposed technique simply forces candidate solutions newly generated to satisfy the equality constraint. To generate these candidate solutions, this study uses a Markov chain Monte Carlo method and crossover kernels. This technique is developed for improving the performances of the genetic network inference methods. I found however that, even when the inference of genetic networks is not defined as a function optimization problem without having an equality constraint, reasonable networks are inferred. Thus, this study then proposed two genetic network inference methods that do not use the technique described above.
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Report
(4 results)
Research Products
(12 results)
