Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization

Recent research in Cooperative Coevolution~(CC) have achieved promising progress in solving large-scale global optimization problems. However, existing CC paradigms have a primary limitation in that they require deep expertise for selecting or designing effective variable decomposition strategies. Inspired by advancements in Meta-Black-Box Optimization, this paper introduces LCC, a pioneering learning-based cooperative coevolution framework that dynamically schedules decomposition strategies during optimization processes. The decomposition strategy selector is parameterized through a neural network, which processes a meticulously crafted set of optimization status features to determine the optimal strategy for each optimization step. The network is trained via the Proximal Policy Optimization method in a reinforcement learning manner across a collection of representative problems, aiming to maximize the expected optimization performance. Extensive experimental results demonstrate that LCC not only offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption, but it also exhibits promising transferability towards unseen problems.

@article{guo2025_2504.17578,
  title={ Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization },
  author={ Hongshu Guo and Wenjie Qiu and Zeyuan Ma and Xinglin Zhang and Jun Zhang and Yue-Jiao Gong },
  journal={arXiv preprint arXiv:2504.17578},
  year={ 2025 }
}