Accepted to GECCO 2022

Our papers (two full papers, one poster paper, and one workshop paper) have been accepted to the Genetic and Evolutionary Computation Conference (GECCO) 2022.

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]
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, Best Paper Nomination). [arXiv] [Code]
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).
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).