Parallel Distributed Genetic Programming using Long-term Memory for Dynamic Scheduling Problems

DOI Web Site 3 References Open Access
  • Hayashida Tomohiro
    Graduate School of Advanced Science and Engineering, Hiroshima University
  • Hirotani Daisuke
    Faculty of Regional Development, Prefectural University of Hiroshima
  • Nishizaki Ichiro
    Graduate School of Advanced Science and Engineering, Hiroshima University
  • Sekizaki Shinya
    Graduate School of Advanced Science and Engineering, Hiroshima University
  • Maeda Ibuki
    Graduate School of Advanced Science and Engineering, Hiroshima University

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Other Title
  • 動的スケジューリング問題のための長期メモリを用いた並列分散遺伝的プログラミング

Abstract

<p>Genetic Programming (GP) is an evolutionary computation method that optimizes the rules defining the relationship between environmental states and system output. GP is an effective method for dynamic environments in which the information repeatedly changes multiple times. On the other hand, in GP, the rules are evaluved as the environmental state changes, so the rules acquired in the distant past disappear in time, and re-learning is required in a dynamic environment. This paper proposes an optimization method for the dynamic scheduling problem where new jobs arrive intermittently. Specifically, a method to improve the learning efficiency of GP in such a periodic dynamic environment by dividing the population into several subpopulations and recording the environmental states or their characteristics. This paper conducts some numerical experiments on the dynamic scheduling problems in which new jobs arrive irregularly to verify the usefulness of the proposed method.</p>

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