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

• URL: http://arxiv.org/abs/2504.17578v1
• Date: Thu, 24 Apr 2025 14:09:22 GMT
• Title: Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization
• Authors: Hongshu Guo, Wenjie Qiu, Zeyuan Ma, Xinglin Zhang, Jun Zhang, Yue-Jiao Gong,
• Abstract summary: This paper introduces LCC, a pioneering learning-based cooperative coevolution framework.<n> LCC dynamically schedules decomposition strategies during optimization processes.<n>It offers certain advantages over state-of-the-art baselines in terms of optimization effectiveness and resource consumption.
• Score: 12.899626317088885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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