徳島大学 教育・研究者情報データベース(EDB)

1. (日) 秋本 洋平

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2. 永田 裕一([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

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3. (日) 佐久間 淳

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4. (日) 小野 功

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5. (日) 小林 重信

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(英) Proposal and Evaluation of Adaptive Real-coded Crossover AREX

(日) 適応的実数値交叉 AREX の提案と評価

(英) Since once premature convergence happens evolutionary algorithms for function optimization can no longer explore areas of the search space and fail to find the optimum, it is required to handle the notorious drawback. This paper proposes two novel approaches to overcome premature convergence of real-coded genetic algorithms (RCGAs). The first idea is to control the sampling region of crossover by adaptation of expansion rate. The second idea is to cause the acceleration of the movement of population by descending the mean of crossover. Finally, we propose a crossover that combines the adaptation of expansion rate technique and the crossover mean descent technique, called AREX (adaptive real-coded ensemble crossover). The performance of the real-coded GA using AREX is evaluated on several benchmark functions including functions whose landscape forms ridge structure or multi-peak structure, both of which are likely to lead to the miserable convergence phenomenon. The experimental results show not only that the proposed method can locate the global optima of functions on which it is difficult for the existing GAs to discover it but also that our approach outperforms the existing one in number of function evaluations on all functions. Our approach enlarges the classes of functions that real-coded GAs can solve.

1. (英) function optimization
2. (英) adaptation of expansion rate
3. (英) crossover mean descent
4. (英) adaptive real-coded ensemble crossover