Eugenics-Based Genetic Algorithm
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概要
- 論文の詳細を見る
The problem of genetic algorithm's efficiency has been attracting the attention of genetic algorithm community. Over the last decade, considerable researches have focused on improving genetic algorithm's performance. However, they are generally under the framework of natural evolutionary mechanism and the major genetic operators, crossover and mutation, are activated by the prior probabilities. An operator based on a prior probability possesses randomness, that is, the unexpected individuals are frequently operated, but the expected individuals are sometimes not operated. Moreover, as the evaluation function is the link between the genetic algorithm and the problem to be solved, the evaluation function provides the heuristic information for evolutionary search. Therefore, how to use this kind of heuristic information (present and past) is influential in the efficiency of evolutionary search. This paper, as an attempt, presents a eugenics-based genetic algorithm (EGA)-a genetic algorithm that reflects the human's decision will (eugenics), and fully utilizes the heuristic information provided by the evaluation function for the decisions. In other words, EGA=evolutionary mechanisms+human's decision will+heuristic information. In EGA, the ideas of the positive eugenics and the negative eugenics are applied as the principle of selections and the selections are not activated by the prior probabilities but by the evaluation values of individuals. A method of genealogical chain-based selection for mutation is proposed, which avoids the blindness of stochastic mutation and the disruptive problem of mutation. A control strategy of reasonable competition is proposed, which brings the effects of crossover and mutation into full play. Three examples, the minimum problem of a standard optimizing function-De Jong's test function F_2, a typical combinatorial optimization problem-the traveling salesman problem, and a problem of identifying nonlinear system, are given to show the good performance of EGA.
- 社団法人電子情報通信学会の論文
- 1996-05-25
著者
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Tanaka Masahiro
Department Of Applied Chemistry
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Tanaka Masahiro
Department Of Information Technology Okayama University
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YE Ju
Graduate School of Natural Science and Technology, Okayama University
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TANINO Tetsuzo
Department of Information Technology, Okayama University
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Ye Ju
Graduate School Of Natural Science And Technology Okayama University
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Tanino Tetsuzo
Department Of Information Technology Okayama University
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Tanino Tetsuzo
Department Of Electronic And Information Systems Graduate School Of Engineering Osaka University
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Tanaka Masahiro
Department Fixed Prosthodontics And Occlusion Osaka Dental University
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