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．
- 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
- 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, Best Paper Nomination). [arXiv] [Code]
- 混合整数ブラックボックス最適化のためのCMA-ESの改良
- 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
- 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