Our papers (two full papers, one poster paper, and one workshop paper) have been accepted to the Genetic and Evolutionary Computation Conference (GECCO) 2022.
- 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]
- 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]
- Ryoki Hamano and Shinichi Shirakawa: Reduction of Genetic Drift in Population-Based Incremental Learning via Entropy Regularization, GECCO 2022 (Accepted as a Poster Paper).
- 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).