Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization
This work addresses the need for expert knowledge in decomposition strategies for researchers and practitioners in optimization, offering an incremental improvement through automation.
The paper tackles the challenge of selecting variable decomposition strategies in cooperative coevolution for large-scale optimization by introducing LCC, a learning-based framework that dynamically schedules strategies using a neural network trained with reinforcement learning. Results show LCC outperforms state-of-the-art baselines in optimization effectiveness and resource consumption, with promising transferability to unseen problems.
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.