模块化CMA-ES：现代进化策略的框架
The Modular CMA-ES: A Framework for Modern Evolution Strategies
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中文总结 AI 辅助
本文提出模块化CMA-ES框架，整合现代进化策略多种机制，支持系统探索设计空间，并通过案例展示其用于研究算法组件及自动配置的效用。
中文摘要 AI 辅助
自现代进化策略（如协方差矩阵自适应进化策略（CMA-ES））被提出以来，它们已成为连续黑箱优化中强大的方法。这一成功催生了大量旨在在特定优化场景中提升性能或行为的改进方案。然而，由于这些发展大多是在孤立环境中引入和研究的，它们之间的相互作用相对未被充分探索。在本文中，我们提出了模块化CMA-ES（ModCMA），一个可配置的框架，它在单一实现中整合了现代进化策略中的多种机制。通过将CMA-ES分解为具有可互换选项的模块，涵盖采样、选择与重组、步长自适应、矩阵自适应以及重启，ModCMA能够系统地探索现代进化策略的大规模设计空间，并促进新算法变体的构建、比较和自动配置。我们通过两个示例研究来说明这种模块化方法的优势。首先，我们比较了几种矩阵自适应机制在计算成本和优化性能方面的差异。其次，我们使用自动算法配置将ModCMA专门化到各个基准问题上，并分析由此产生的配置。这些示例共同展示了该框架如何既可用于研究单个算法设计选择，也可用于以系统和可重复的方式探索它们的组合。
英文摘要
Since their introduction, modern evolution strategies, such as the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), have become established as powerful methods for continuous black-box optimization. This success has led to a wide range of proposed modifications, each designed to improve performance or behavior in specific optimization scenarios. However, because these developments have largely been introduced and studied in isolation, their interactions remain comparatively underexplored. In this paper, we present the Modular CMA-ES (ModCMA), a configurable framework that integrates a wide range of mechanisms from modern evolution strategies within a single implementation. By decomposing CMA-ES into modules with interchangeable options for sampling, selection and recombination, step-size adaptation, matrix adaptation, and restarting, ModCMA enables systematic exploration of a large design space of modern evolution strategies and facilitates the construction, comparison, and automated configuration of new algorithm variants. We illustrate the benefits of this modular approach through two example studies. First, we compare several matrix-adaptation mechanisms in terms of their computational cost and optimization performance. Second, we use automated algorithm configuration to specialize ModCMA to individual benchmark problems and analyze the resulting configurations. Together, these examples demonstrate how the framework can be used both to study individual algorithmic design choices and to explore their combinations in a systematic and reproducible manner.
