Big-valley Explorer: A Framework of Real-coded Genetic Algorithms for Multi-funnel Function Optimization

DOI
  • Uemura Kento
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology; Research Fellowship for Young Scientists (DC1) of the Japan Society for the Promotion of Science
  • Kinoshita Shun-ichi
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology (Present: Cyberscience Center, Tohoku University)
  • Nagata Yuichi
    Education Academy of Computational Life Sciences, Tokyo Institute of Technology
  • Kobayashi Shigenobu
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology (Present: Professor Emeritus, Tokyo Institute of Technology)
  • Ono Isao
    Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

Bibliographic Information

Other Title
  • 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

Description

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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Details 詳細情報について

  • CRID
    1390001205366429312
  • NII Article ID
    130004965145
  • DOI
    10.11394/tjpnsec.4.1
  • ISSN
    21857385
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
    • KAKEN
  • Abstract License Flag
    Disallowed

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