Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm Selection

Urban Škvorc, Tome Eftimov and Peter Korošec
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Urban Škvorc: Computer Systems Department, Jožef Stefan Institute, Jamova Cesta 39, SI-1000 Ljubljana, Slovenia
Tome Eftimov: Computer Systems Department, Jožef Stefan Institute, Jamova Cesta 39, SI-1000 Ljubljana, Slovenia
Peter Korošec: Computer Systems Department, Jožef Stefan Institute, Jamova Cesta 39, SI-1000 Ljubljana, Slovenia

Mathematics, 2022, vol. 10, issue 3, 1-21

Abstract: In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used to train and test the model, and existing optimization benchmarks only provide a limited amount of data. To help with this problem, artificial problem generation has been shown to be a useful tool for augmenting existing benchmark problems. In this paper, we are interested in the problem of knowledge transfer between the artificially generated and existing handmade benchmark problems in the domain of continuous numerical optimization. That is, can an algorithm selection model trained purely on artificially generated problems correctly provide algorithm recommendations for existing handmade problems. We show that such a model produces low-quality results, and we also provide explanations about how the algorithm selection model works and show the differences between the problem data sets in order to explain the model’s performance.

Keywords: machine learning; transfer learning; exploratory landscape analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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