20.500.12556/DiRROS-20968 Using machine learning methods to assess module performance contribution in modular optimization frameworks Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of algorithms. In this study, we propose a methodology for analyzing the impact of the different modules on the overall performance. We consider modular frameworks for two widely used families of derivative-free black-box optimization algorithms, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE). More specifically, we use performance data of 324 modCMA-ES and 576 modDE algorithm variants (with each variant corresponding to a specific configuration of modules) obtained on the 24 BBOB problems for 6 different runtime budgets in 2 dimensions. Our analysis of these data reveals that the impact of individual modules on overall algorithm performance varies significantly. Notably, among the examined modules, the elitism module in CMA-ES and the linear population size reduction module in DE exhibit the most significant impact on performance. Furthermore, our exploratory data analysis of problem landscape data suggests that the most relevant landscape features remain consistent regardless of the configuration of individual modules, but the influence that these features have on regression accuracy varies. In addition, we apply classifiers that exploit feature importance with respect to the trained models for performance prediction and performance data, to predict the modular configurations of CMA-ES and DE algorithm variants. The results show that the predicted configurations do not exhibit a statistically significant difference in performance compared to the true configurations, with the percentage varying depending on the setup (from 49.1% to 95.5% for modCMA and 21.7% to 77.1% for DE) evolutionary computation modular algorithm frameworks DE mreže algoritmi true false true Massachusetts Institute of Technology ZDA Angleški jezik Slovenski jezik © 2024 Massachusetts Institute of Technology Neznano 2024-12-11 12:11:31 2024-12-11 12:11:31 2024-12-12 03:35:31 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Opis vira z dne 10. 9. 2024; 31 str. 0000-00-00 PostprintKoncna Objavljeno NiDoloceno 2023-07-16 2024-07-03 2024-10-17 004.85 1530-9304 10.1162/evco_a_00356 206993155 https://direct.mit.edu/evco/article-abstract/doi/10.1162/evco_a_00356/123899/Using-Machine-Learning-Methods-to-Assess-Module?redirectedFrom=fulltext 1 ad724830-b7b0-11ef-ba3f-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=29664 RAZ_Kostovska_Ana_2024.pdf RAZ_Kostovska_Ana_2024.pdf 1 7D7A574A260449EFC752BD8129275844 970e46c2531e16302e950814a85c536370b7ecdb5ab694273303f56f852d3311 3cbb99de-b7b2-11ef-ba3f-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=29666 Institut Jožef Stefan 0 0 0