| Project/Area Number |
10650104
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Materials/Mechanics of materials
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| Research Institution | Doshisha University |
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Principal Investigator |
MIKI Mitsunori Doshisha University, Deot. of Knowledge Engineering, Professor, 工学部, 教授 (90150755)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 1999: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1998: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | Parallel Processing / Genetic Algorithms / Optimum Design / Crossover rate / Mutation rate / Stuructural Optimization / PC cluster / Evolutionary computation / 分散処理 / 移住 |
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| Research Abstract |
Genetic algorithms (GA) are useful methods for complicated optimization problems.
However, their main drawback is computationally expensive. Therefore, parallel processing is inevitable, but the researches on parallel genetic algorithms are not enough. This research aims to evaluate the effectiveness of parallel genetic algorithms and to propose a new distributed GA which shows a high efficiency in parallel processing.
First, some distributed GA models with divided sub-populations are compared and the performance of the models are investigated. The effect of the GA parameters are investigated, and a new approach is proposed. The following are the summaries of the search.
1) The distributed population model with divided sub-populations with migration is suitable for parallel processing and its performance for providing good solutions in very high. The distribution population model suppress the early convergence.
2) The optimum crossover and mutation rates for the distributed GA are different from those for single population GA, and a new distributed GA model with distributed environment is proposed to relive the difficulty in choosing optimum parameters.
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