Presentation | 2013-10-28 Nonlinear Sparse Optimization with Based on Genetic Algorithm : Optimization for Food Ingredients and their Quantities Hideki SATOH, Masako SATOH, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Approximate sparse solutions for a high-dimensional nonlinear optimization problem were derived using a genetic algorithm. First, chromosomes were set using genes defined by the serial numbers on the state variables of the problem. Next, the problem was reduced to low-dimensional nonlinear problems for the state variables with the serial numbers given by the chromosomes. Using the fitness functions of the chromosomes defined by the evaluation functions of the reduced problems, the chromosomes were improved using a genetic algorithm. Approximate sparse solutions for the high-dimensional nonlinear optimization problem were derived by solving the reduced problems. This algorithm was used to solve a recipe optimization problem, and optimum food ingredients and their quantities were obtained for target nutritional values. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | genetic algorithm / sparse / high-dimensional / optimization / food / recipe |
Paper # | NLP2013-78 |
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Committee | NLP |
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Conference Date | 2013/10/21(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Nonlinear Sparse Optimization with Based on Genetic Algorithm : Optimization for Food Ingredients and their Quantities |
Sub Title (in English) | |
Keyword(1) | genetic algorithm |
Keyword(2) | sparse |
Keyword(3) | high-dimensional |
Keyword(4) | optimization |
Keyword(5) | food |
Keyword(6) | recipe |
1st Author's Name | Hideki SATOH |
1st Author's Affiliation | Shool of Systems Information Science, Future University Hakodate() |
2nd Author's Name | Masako SATOH |
2nd Author's Affiliation | Intelligent Sensor Technology, Inc. |
Date | 2013-10-28 |
Paper # | NLP2013-78 |
Volume (vol) | vol.113 |
Number (no) | 271 |
Page | pp.pp.- |
#Pages | 6 |
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