Exploring module interactions in modular CMA-ES across problem classes

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-08-14 DOI:10.1016/j.swevo.2025.102116

Ana Nikolikj , Tome Eftimov

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引用次数: 0

Abstract

This study presents an in-depth analysis of module importance within the modular CMA-ES (modCMA-ES) algorithm using exploratory data analysis and large-scale benchmarking across the BBOB suite. Rather than introducing new algorithms, our contribution lies in uncovering how individual modules and their interactions influence optimization performance across diverse black-box problem classes. We evaluate 324 modCMA-ES variants across 24 problem classes using functional ANOVA (f-ANOVA) to quantify the variance in performance attributable to individual, pairwise, and triplet module interactions. Results reveal substantial variation in module importance across problem classes and highlight strong alignment between module interaction patterns and high-level landscape features, particularly multi-modality. Further, we demonstrate that configuring only the most important modules — identified via f-ANOVA — achieves performance comparable to or better than the single-best solver, especially in high-dimensional settings. This analysis, conducted at both low (5D) and high (30D) dimensions, offers actionable insights into module interactions within the mod-CMA-ES framework.

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探索模块化CMA-ES中跨问题类的模块交互

本研究使用探索性数据分析和跨BBOB套件的大规模基准测试，对模块化CMA-ES （modCMA-ES）算法中的模块重要性进行了深入分析。我们的贡献不是引入新的算法，而是揭示各个模块及其相互作用如何影响不同黑箱问题类的优化性能。我们使用功能方差分析（f-ANOVA）评估了24个问题类别中的324个modCMA-ES变体，以量化可归因于单个、两两和三元组模块相互作用的性能差异。结果揭示了不同问题类别中模块重要性的实质性变化，并突出了模块交互模式和高级景观特征之间的强烈一致性，特别是多模态。此外，我们证明仅配置最重要的模块-通过f-ANOVA识别-可以实现与单一最佳解算器相当或更好的性能，特别是在高维设置中。该分析在低（5D）和高（30D）维度上进行，为modcma - es框架内的模块交互提供了可操作的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.