Feature-based Evolutionary Diversity Optimization of Discriminating Instances for Chance-constrained Optimization Problems
Date
2025
Authors
Ahouei, S.S.
Antipov, D.
Neumann, A.
Neumann, F.
Editors
Krejca, M.S.
Wagner, M.
Wagner, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Proceedings of the 25th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2025), as publiushed in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025 / Krejca, M.S., Wagner, M. (ed./s), vol.15610, pp.184-199
Statement of Responsibility
Saba Sadeghi Ahouei, B, Denis Antipov, Aneta Neumann, and Frank Neumann
Conference Name
25th European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP) (23 Apr 2025 - 25 Apr 2025 : Trieste, Italy)
Abstract
Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a given set of algorithms for a given problem requires a detailed analysis of the problem’s features. To do so, it is important to have a diverse set of benchmarking instances highlighting the difference in algorithms’ performance. In this paper, we evolve diverse benchmarking instances for chance-constrained optimization problems that contain stochastic components characterized by their expected values and variances. These instances clearly differentiate the performance of two given algorithms, meaning they are easy to solve by one algorithm and hard to solve by the other. We introduce a (μ + 1) EA for feature-based diversity optimization to evolve such differentiating instances. We study the chance-constrained maximum coverage problem with stochastic weights on the vertices as an example of chance-constrained optimization problems. The experimental results demonstrate that our method successfully generates diverse instances based on different features while effectively distinguishing the performance between a pair of algorithms.
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Description
Held as Part of EvoStar 2025 (evo*)
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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025