Two Novel Instance Selection Methods Combining Algorithm Performance and Landscape Analysis: A Comparative Study in Continuous Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F26%3A0201668" target="_blank" >RIV/00216305:26210/26:0201668 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/11224009" target="_blank" >https://ieeexplore.ieee.org/abstract/document/11224009</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCYB.2025.3625095" target="_blank" >10.1109/TCYB.2025.3625095</a>
Alternative languages
Result language
angličtina
Original language name
Two Novel Instance Selection Methods Combining Algorithm Performance and Landscape Analysis: A Comparative Study in Continuous Optimization
Original language description
A reliable benchmark library is essential for advancing research in global optimization by enabling fair comparisons and rigorous testing of optimization algorithms across diverse problem landscapes. In this article, we focus on instance selection methods, which aim to choose representative problems for evaluating algorithm performance. We present a comprehensive review of existing instance selection methods, highlighting their strengths and limitations, particularly in balancing the consideration of algorithm performance and the analysis of problem characteristics using exploratory landscape analysis. Building on these insights, we introduce two novel instance selection methods that leverage both algorithm performance data and landscape analysis information to construct diverse and informative benchmark sets. For evaluation, we benchmark our approaches against four existing instance selection methods on the recently expanded DIRECTGOLib v2.0 library. Our results demonstrate that the proposed methods effectively identify representative instances that capture a wide range of problem characteristics, enabling a more comprehensive evaluation of algorithm performance. These findings have significant implications for the development and assessment of new optimization algorithms, ultimately contributing to more reliable and robust solutions for real-world optimization problems.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking derivative-free global optimization methods</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2026
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Cybernetics
ISSN
2168-2267
e-ISSN
2168-2275
Volume of the periodical
56
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
1202-1215
UT code for WoS article
001612903100001
EID of the result in the Scopus database
2-s2.0-105020878186