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

DOI Web Site 13 References
  • Oshima Dan
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology
  • Miyamae Atsushi
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology
  • Nagata Yuichi
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology
  • Kobayashi Shigenobu
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology
  • Ono Isao
    Graduate School of Interdisciplinary Science and Engineering, Tokyo Institute of Technology
  • Sakuma Jun
    Graduate School of Systems and Information Engineering, University of Tsukuba

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  • 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

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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 niching for finding multiple optima take into account big valley landscapes or non-deceptive globally multimodal landscapes but not deceptive ones called UV-landscapes. 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.

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