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Conference Poster Year : 2023

Comparing Algorithm Selection Approaches on Black-Box Optimization Problems

Ana Kostovska
Anja Jankovic
Diederick Vermetten
Sašo Džeroski
Tome Eftimov
Carola Doerr

Abstract

Performance complementarity of solvers available to tackle blackbox optimization problems gives rise to the important task of algorithm selection (AS). Automated AS approaches can help replace tedious and labor-intensive manual selection, and have already shown promising performance in various optimization domains. Automated AS relies on machine learning (ML) techniques to recommend the best algorithm given the information about the problem instance. Unfortunately, there are no clear guidelines for choosing the most appropriate one from a variety of ML techniques. Treebased models such as Random Forest or XGBoost have consistently demonstrated outstanding performance for automated AS. Transformers and other tabular deep learning models have also been increasingly applied in this context. We investigate in this work the impact of the choice of the ML technique on AS performance. We compare four ML models on the task of predicting the best solver for the BBOB problems for 7 different runtime budgets in 2 dimensions. While our results confirm that a per-instance AS has indeed impressive potential, we also show that the particular choice of the ML technique is of much minor importance.
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Dates and versions

hal-04180599 , version 1 (12-08-2023)

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Ana Kostovska, Anja Jankovic, Diederick Vermetten, Sašo Džeroski, Tome Eftimov, et al.. Comparing Algorithm Selection Approaches on Black-Box Optimization Problems. GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Jul 2023, Lisbon, Portugal. ACM, GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp.495-498, 2023, ⟨10.1145/3583133.3590697⟩. ⟨hal-04180599⟩
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