arXiv cs.AI· Wenjie Qiu, Hepei Li, Jianfeng Wang, Jun Zhang, Yue-Jiao Gong·· 13 小时前AI 评分18
WCC：一个基于学习的框架，决定大规模优化中何时应用协同进化
WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
AI 导读
WCC 是一个基于学习的框架，用于动态决定大规模全局优化（LSGO）中何时部署协同进化（CC）。它将决策建模为马尔可夫决策过程，通过自回归策略在 CC 与非分解算法（NDA）之间进行细粒度动态切换，并无需专家设定即可条件性分配计算预算。WCC 在跨问题、优化视野和基线优化器上展现出强竞争力与泛化能力，以低预算实现“一次训练、重复使用”范式，显著降低 LSGO 成本。
正文 · 原文
Abstract:Cooperative Coevolution (CC) and Non-Decomposition Algorithms (NDAs) are two primary approaches for large-scale global optimization (LSGO). Although both have achieved success, they exhibit markedly different performance across problem types and each faces approach-specific limitations, leaving the question of "when to use CC" unresolved. We address this gap with WCC, a novel learning-based framework that dynamically decides when to deploy CC in LSGO. WCC employs a streamlined design for efficient adaptive learning. We formulate the decision as a Markov decision process, designing a targeted feature set, a robust reward, and a tailored network architecture to provide the agent with rich information about the problem state and optimization progress. Our auto-regressive policy enables WCC to perform fine-grained, dynamic switching between CC and NDAs while conditionally allocating the computational budget without expert specifications. WCC shows strong competitiveness and superior generalization across problems, optimization horizons, and baseline optimizers. It realizes a "train-once and reuse" paradigm under a low budget, significantly curbing LSGO cost. Additionally, in-depth analyses yield insights not captured by existing theory, offering practical guidance on when and how to effectively deploy CC.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37329 [cs.LG] |
| (or arXiv:2610.37329v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37329 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenjie Qiu [view email]
[v1]
Tue, 27 Oct 2026 14:37:10 UTC (1,015 KB)
来源：arXiv cs.AI · arxiv.org