GECCO 2022に論文が採択されました

国際会議 Genetic and Evolutionary Computation Conference (GECCO) 2022 に我々の論文(Full Paper 2件,Poster Paper 1件,Workshop Paper 1件)が採択されました.

  1. Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi, Hiroshi Umeki, Shinichi Shirakawa, and Naoki Hamada: A Two-phase Framework with a Bezier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization, GECCO 2022 (Accepted as a Full Paper). [arXiv]
    • ベジエ単体を利用したExpensiveな多目的最適化のためのフレームワークの提案
  2. Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa: CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization, GECCO 2022 (Accepted as a Full Paper).
    • 混合整数ブラックボックス最適化のためのCMA-ESの改良
  3. Ryoki Hamano and Shinichi Shirakawa: Reduction of Genetic Drift in Population-Based Incremental Learning via Entropy Regularization, GECCO 2022 (Accepted as a Poster Paper).
    • エントロピー正則化を利用したブラックボックスバイナリ変数最適化法PBILの改良
  4. Ryoki Hamano, Shota Saito, Masahiro Nomura, and Shinichi Shirakawa: Benchmarking CMA-ES with Margin on the bbob-mixint Testbed, GECCO Workshop on Black-Box Optimization Benchmarking (BBOB 2022) (Accepted).
    • 混合整数ブラックボックス最適化のためのCMA-ES with Marginのbbob-mixint Testbedでの評価