Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910918045073408 |
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| author | Guo, Hongshu Qiu, Wenjie Ma, Zeyuan Zhang, Xinglin Zhang, Jun Gong, Yue-Jiao |
| author_facet | Guo, Hongshu Qiu, Wenjie Ma, Zeyuan Zhang, Xinglin Zhang, Jun Gong, Yue-Jiao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_17578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization Guo, Hongshu Qiu, Wenjie Ma, Zeyuan Zhang, Xinglin Zhang, Jun Gong, Yue-Jiao Machine Learning Neural and Evolutionary Computing 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. |
| title | Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2504.17578 |