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The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection

Abstract : Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regression methods are able to achieve meaningful results for ELA-based algorithm selection. For those three models-random forests, decision trees, and bagging decision trees-the quality of the regression models is highly impacted by the chosen hyper-parameters. This has significant effects also on the quality of the algorithm selectors that are built on top of these regressions. By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection. We point out that a choice of the machine learning model merits to be carefully undertaken and further investigated.
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Contributor : Carola Doerr <>
Submitted on : Tuesday, May 25, 2021 - 9:56:01 AM
Last modification on : Tuesday, July 13, 2021 - 3:27:40 AM
Long-term archiving on: : Thursday, August 26, 2021 - 6:22:21 PM


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Anja Jankovic, Gorjan Popovski, Tome Eftimov, Carola Doerr. The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection. Genetic and Evolutionary Computation Conference (GECCO 2021), Jul 2021, Lille, France. ⟨10.1145/3449639.3459406⟩. ⟨hal-03233811⟩



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