Enhancing Dynamic Algorithm Configuration via Theory-guided Benchmarks

2025-2026 Publications

• 2026
  • T. Nguyen, Ph. Le, A. Biedenkapp, C. Doerr, N. Dang : “Deep Reinforcement Learning for Dynamic Algorithm Configuration: A Case Study on Optimizing OneMax with the $(1+(\lambda,\lambda))$-GA”, ACM Transactions on Evolutionary Learning and Optimization, (ACM) (2026)
• 2025
  • T. Nguyen, Ph. Le, A. Biedenkapp, C. Doerr, N. Dang : “On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(L,L))-GA”, GECCO '25: Proceedings of the Genetic and Evolutionary Computation Conference, Malaga, Spain, pp. 1162-1171, (Association for Computing Machinery), (ISBN: 979-8-4007-1465-8) (2025)
  • T. Nguyen, Ph. Le, C. Doerr, N. Dang : “Multi-parameter Control for the (1+(L,L))-GA on OneMax via Deep Reinforcement Learning”, Proc. of Foundations of Genetic Algorithms (FOGA), Leiden, Netherlands (2025)