ノイズを有する関数最適化のための進化戦略

DOI
  • 益富 和之
    東京工業大学 大学院総合理工学研究科
  • 永田 裕一
    徳島大学 大学院ソシオテクノサイエンス研究部
  • 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
  • A Novel Evolution Strategy for Noisy Function Optimization

説明

This paper proposes a novel evolution strategy for noisy function optimization. We consider minimization of the expectation of a continuous domain function with stochastic parameters. The proposed method is an extended variant of distance-weighted exponential evolution strategy (DX-NES), which is a state-of-the-art algorithm for deterministic function optimization. We name it DX-NES for uncertain environments (DX-NES-UE). DX-NES-UE estimates the objective function by a quadratic surrogate function. In order to make a balance between speed and accuracy, DX-NES-UE uses surrogate function values when the noise is strong; otherwise it uses observed objective function values. We conduct numerical experiments on 20-dimensional benchmark problems to compare the performance of DX-NES-UE and that of uncertainty handling covariance matrix adaptation evolution strategy (UH-CMA-ES). UH-CMA-ES is one of the most promising methods for noisy function optimization. Benchmark problems include a multimodal function, ill-scaled functions and a non-C2 function with additive noise and decision variable perturbation (sometime called actuator noise). The experiments show that DX-NES-UE requires about 1/100 times as many observations as UH-CMA-ES does on well-scaled functions. The performance difference is greater on ill-scaled functions.

収録刊行物

詳細情報 詳細情報について

  • CRID
    1390282680341421184
  • NII論文ID
    130005068740
  • DOI
    10.11394/tjpnsec.6.1
  • ISSN
    21857385
  • 本文言語コード
    ja
  • データソース種別
    • JaLC
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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