Digital repository of Slovenian research organisations

Show document
A+ | A- | Help | SLO | ENG

Title:Exploring module interactions in modular CMA-ES across problem classes
Authors:ID Nikolikj, Ana, Institut "Jožef Stefan" (Author)
ID Eftimov, Tome, Institut "Jožef Stefan" (Author)
Files:URL URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S2210650225002743?via%3Dihub
 
.pdf PDF - Presentation file, download (3,41 MB)
MD5: D8727E997E2C27D543D0C70DD8814B34
 
Language:English
Typology:1.01 - Original Scientific Article
Organization:Logo IJS - Jožef Stefan Institute
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.
Keywords:module importance, empirical study, black-box optimization, benchmarking
Publication status:Published
Publication version:Version of Record
Submitted for review:26.04.2025
Article acceptance date:31.07.2025
Publication date:14.08.2025
Publisher:Elsevier
Year of publishing:2025
Number of pages:1-18 str.
Numbering:Vol. 98, [article no.] 102116
Source:Nizozemska
PID:20.500.12556/DiRROS-23341 New window
UDC:519.6
ISSN on article:2210-6510
DOI:10.1016/j.swevo.2025.102116 New window
COBISS.SI-ID:245926915 New window
Copyright:© 2025 The Authors.
Note:Nasl. z nasl. zaslona; Opis vira z dne 19. 8. 2025;
Publication date in DiRROS:20.08.2025
Views:403
Downloads:176
Metadata:XML DC-XML DC-RDF
:
Copy citation
  
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Record is a part of a journal

Title:Swarm and evolutionary computation
Publisher:Elsevier
ISSN:2210-6510
COBISS.SI-ID:175366403 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0098
Name:Računalniške strukture in sistemi

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:GC-0001
Name:Umetna inteligenca za znanost

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:J2-4460
Name:Auto-OPT: Avtomatizirana izbira in konfiguracija eno-kriterijskih zveznih optimizacijskih algoritmov

Funder:ARIS - Slovenian Research and Innovation Agency
Funding programme:Young Researcher Grant
Project number:PR-12897

Funder:EC - European Commission
Funding programme:HE
Project number:101187010
Name:Leveraging Benchmarking Data for Automated Machine Learning and Optimization
Acronym:AutoLearn-SI

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:14.08.2025
Applies to:VoR

Secondary language

Language:Slovenian
Keywords:pomen modula, empirična študija, črna skrinjica, optimizacija


Back