| Presentation | 2002/1/22 A Study on Modeling Genetic Algorithms by using Neural Networks Jun-ichi IMAI, Hiroyuki SHIOYA, Tsutomu DA-TE, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this paper, we propose a method for modeling genetic algorithms by using neural networks. We use a neural network for estimating deterministic transitions of infinite populations from stochastic data obtained through observing a process of a genetic algorithm for finite populations. Then, the trained neural network approximates a mapping (or a vector field) which characterizes the genetic algorithm. In this paper, we use a mixture-of-experts architecture for modeling and show that an optimization problem, which the genetic algorithm is applied to, is represented as a combination of some other optimization problems corresponding to expert networks. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | genetic algorithm / neural network / vector field / mixture-of-experts architecture |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2002/1/22(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Modeling Genetic Algorithms by using Neural Networks |
| Sub Title (in English) | |
| Keyword(1) | genetic algorithm |
| Keyword(2) | neural network |
| Keyword(3) | vector field |
| Keyword(4) | mixture-of-experts architecture |
| 1st Author's Name | Jun-ichi IMAI |
| 1st Author's Affiliation | Graduate School of Engineering, Hokkaido University() |
| 2nd Author's Name | Hiroyuki SHIOYA |
| 2nd Author's Affiliation | Muroran Institute of Technology |
| 3rd Author's Name | Tsutomu DA-TE |
| 3rd Author's Affiliation | Graduate School of Engineering, Hokkaido University |
| Date | 2002/1/22 |
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| Volume (vol) | vol.101 |
| Number (no) | 616 |
| Page | pp.pp.- |
| #Pages | 8 |
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