大域的多峰性関数最適化のための実数値GAの枠組みBig-valley Explorerの提案

DOI
  • 上村 健人
    東京工業大学 大学院総合理工学研究科・日本学術振興会特別研究員DC
  • 木下 峻一
    東京工業大学 大学院総合理工学研究科(現 東北大学 サイバーサイエンスセンター)
  • 永田 裕一
    東京工業大学情報生命博士教育院
  • 小林 重信
    東京工業大学 大学院総合理工学研究科(現 東京工業大学名誉教授)
  • 小野 功
    東京工業大学 大学院総合理工学研究科

書誌事項

タイトル別名
  • Big-valley Explorer: A Framework of Real-coded Genetic Algorithms for Multi-funnel Function Optimization

説明

This paper proposes a new framework of real-coded genetic algorithms (RCGAs) for the multi-funnel function optimization. The RCGA is one of the most powerful function optimization methods. Most conventional RCGAs work effectively on the single-funnel function that consists of a single big-valley. However, it is reported that they show poor performance or, sometimes, fail to find the optimum on the multi-funnel function that consists of multiple big-valleys. In order to remedy this deterioration, Innately Split Model (ISM) has been proposed as a framework of RCGAs. ISM initializes an RCGA in a small region and repeats a search with the RCGA as changing the position of the region randomly. ISM outperforms conventional RCGAs on the multi-funnel functions. However, ISM has two problems in terms of the search efficiency and the difficulty of setting parameters. Our proposed method, Big-valley Explorer (BE), is a framework of RCGAs like ISM and it has two novel mechanisms to overcome these problems, the big-valley estimation mechanism and the adaptive initialization mechanism. Once the RCGA finishes a search, the big-valley estimation mechanism estimates a big-valley that the RCGA already explored and removes the region from the search space to prevent the RCGA from searching the same big-valley many times. After that, the adaptive initialization mechanism initializes the RCGA in a wide unexplored region adaptively to find unexplored big-valleys. We evaluate BE through some numerical experiments with both single-funnel and multi-funnel benchmark functions.

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詳細情報 詳細情報について

  • CRID
    1390001205366429312
  • NII論文ID
    130004965145
  • DOI
    10.11394/tjpnsec.4.1
  • ISSN
    21857385
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
    • CiNii Articles
    • KAKEN
  • 抄録ライセンスフラグ
    使用不可

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