徳島大学 教育・研究者情報データベース(EDB)

1. (日) 大嶋 彈

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2. (日) 宮前 惇

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3. 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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4. (日) 小林 重信

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5. 小野 功

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6. (日) 佐久間 淳

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(英) A New Real-coded Genetic Algorithm with an Adaptive Mating Selection for UV-landscapes

(日) UV構造を考慮した適応的複製選択による実数値GAの提案

(英) The purpose of this paper is to propose a new real-coded genetic algorithm (RCGA) named Networked Genetic Algorithm (NGA) that intends to find multiple optima simultaneously in deceptive globally multimodal landscapes. Most current techniques such as <I>niching</I> for finding multiple optima take into account <I>big valley</I> landscapes or non-deceptive <I>globally multimodal</I> landscapes but not deceptive ones called <I>UV-landscapes</I>. Adaptive Neighboring Search (ANS) is a promising approach for finding multiple optima in UV-landscapes. ANS utilizes a restricted mating scheme with a crossover-like mutation in order to find optima in deceptive globally multimodal landscapes. However, ANS has a fundamental problem that it does not find all the optima simultaneously in many cases. NGA overcomes the problem by an adaptive parent-selection scheme and an improved crossover-like mutation. We show the effectiveness of NGA over ANS in terms of the number of detected optima in a single run on Fletcher and Powell functions as benchmark problems that are known to have multiple optima, ill-scaledness, and UV-landscapes.

1. 遺伝的アルゴリズム(genetic algorithm)
2. (英) function optimization
3. (英) UV-structure
4. (英) global multimodality
5. (英) networked genetic algorithm
6. (英) deceptive problem