Abstract
We present an approach that is able to automatically choose the best meta-heuristic and configuration for solving a real optimization problem. Our approach allows the researcher to indicate which meta-heuristics to choose from and, for each meta-heuristic, which parameters should be automatically configured to find good solutions for the optimization problem. We show that our approach is sound using ten well know real optimization problems and five meta-heuristics. As a side effect, we were also able to provide an unbiased way of assessing meta-heuristics concerning their performance to address one or more classes of real optimization problems. Our approach improved the results found for all the meta-heuristics in all problems and was also able to find very competitive results for all optimization problems when given the liberty to choose which meta-heuristic to use.
This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Kevin Martins thanks FCT for the grant SFRH/BD/151434/2021.
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Martins, K., Mendes, R. (2022). Cherry-Picking Meta-heuristic Algorithms and Parameters for Real Optimization Problems. In: Marreiros, G., Martins, B., Paiva, A., Ribeiro, B., Sardinha, A. (eds) Progress in Artificial Intelligence. EPIA 2022. Lecture Notes in Computer Science(), vol 13566. Springer, Cham. https://doi.org/10.1007/978-3-031-16474-3_41
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