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arXiv AI By Wenjie Qiu, Hepei Li, Jianfeng Wang, Jun Zhang, Yue-Jiao Gong

WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization

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WCC is a learning‑based framework that decides dynamically when to apply Cooperative Coevolution (CC) versus Non‑Decomposition Algorithms (NDAs) in large‑scale global optimization. It models this decision as a Markov decision process, using a tailored feature set, reward scheme, and network architecture to enable fine‑grained, adaptive switching and conditional budget allocation without expert input. Experiments show WCC competes strongly and generalizes across problems, horizons, and baseline optimizers, supporting a low‑budget "train‑once and reuse" approach while providing new practical insights into CC deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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