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 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.
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Keyword(in English) genetic algorithm / sparse / high-dimensional / optimization / food / recipe
Paper # NLP2013-78
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Committee NLP
Conference Date 2013/10/21(1days)
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Registration To Nonlinear Problems (NLP)
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
Date of Issue