Work

Similarity-based Portfolio Construction for Black-box Optimization

Year: 2026

Type: conference-paper

Abstract: In black-box optimization, a central question is which algorithm to use to solve a given, previously unseen, problem. Selecting a single algorithm, however, entails inherent risks: inaccuracies in the selector may lead to poor choices, and even well-performing algorithms with high variance can yield unsatisfactory results in a single run. A natural remedy is to split the evaluation budget across multiple runs of potentially different algorithms. Such sequential algorithm portfolios benefit from variance reduction and complementarities between algorithms, often outperforming approaches that allocate the entire budget to a single solver.

Source: Genetic and Evolutionary Computation Conference (GECCO)

DOI: 10.1145/3795095.3805145

Authors: Catalin-Viorel Dinu, Diederick Vermetten, Carola Doerr

Institutions: Centre National de la Recherche Scientifique, Sorbonne Université, LIP6

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche

Award: Allier l'optimisation boîte noire et l'apprentissage automatique pour la configuration dynamiques d'algorithmes