徳島大学 教育・研究者情報データベース(EDB)

1. (日) 益富 和之

   役割	任意
   貢献度	任意
   学籍番号	推奨

2. 永田 裕一([徳島大学.大学院社会産業理工学研究部.理工学域.知能情報系.情報工学分野]/[徳島大学.理工学部.理工学科.知能情報コース.情報工学講座])

   役割	任意
   貢献度	任意
   学籍番号	推奨

3. (日) 小野 功

   役割	任意
   貢献度	任意
   学籍番号	推奨

(英) A Novel Evolution Strategy for Noisy Function Optimization

(日) ノイズを有する関数最適化のための進化戦略

(英) 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-C<SUP>2</SUP> 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.

1. (英) DX-NES
2. (英) UH-CMA-ES
3. (英) noisy function optimization
4. (英) surrogate function
5. (英) stochastic descent
6. (英) additive noise
7. (英) decision variable perturbation