An Anticipating Hybrid Genetic Algorithm for Fuzzy Modeling

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概要

• 論文の詳細を見る

• Although it is often claimed that due to their probabilistic character genetic algorithm(GA's)are able to avoid getting trapped in local minima, this statement is only valid in a very narrow sense. Especially when it comes to apply GA's for fuzzy model and controller optimization one faces several problems. The reason for this lack of performance lies in the nature of the optimization task itself. For a better understanding of the problem, we first compare the simple genetic algorithm with the simplex downhill optimization method under three different initial conditions. Consequently, we propose an anticipating GA to solve the above mentioned problem. To enhance computing time, this proposed GA is further combined with the downhill simpl exmethod in the final stage of the optimization. Thus, resulting in a hybrid algorithm.

• 日本知能情報ファジィ学会の論文
• 1995-10-15

著者

• Hayashi Isao

  Department of Metabolic Medicine, Graduate School of Medicine, Osaka University

• Hayashi I

  Department Of Management And Computer Sciences Hannan University

• BASTIAN Andreas

  Electronic Research, Volkwagen AG.

• Bastian A

  Volkswagen Ag

• HAYASHI Isao

  Department of Management and Computer Sciences, Hannan University

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• An Anticipating Hybrid Genetic Algorithm for Fuzzy Modeling

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