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Conference Papers Year : 2021

MATE: A Model-based Algorithm Tuning Engine - A proof of concept towards transparent feature-dependent parameter tuning using symbolic regression

Mohamed El Yafrani
  • Function : Author
Marcella Scoczynski
  • Function : Author
Inkyung Sung
  • Function : Author
Peter Nielsen
  • Function : Author

Abstract

In this paper, we introduce a Model-based Algorithm Turning Engine, namely MATE, where the parameters of an algorithm are represented as expressions of the features of a target optimisation problem. In contrast to most static (feature-independent) algorithm tuning engines such as irace and SPOT, our approach aims to derive the best parameter configuration of a given algorithm for a specific problem, exploiting the relationships between the algorithm parameters and the features of the problem. We formulate the problem of finding the relationships between the parameters and the problem features as a symbolic regression problem and we use genetic programming to extract these expressions. For the evaluation, we apply our approach to configuration of the (1+1) EA and RLS algorithms for the OneMax, LeadingOnes, BinValue and Jump optimisation problems, where the theoretically optimal algorithm parameters to the problems are available as functions of the features of the problems. Our study shows that the found relationships typically comply with known theoretical results, thus demonstrating a new opportunity to consider model-based parameter tuning as an effective alternative to the static algorithm tuning engines.
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Dates and versions

hal-03233689 , version 1 (25-05-2021)

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Mohamed El Yafrani, Marcella Scoczynski, Inkyung Sung, Markus Wagner, Carola Doerr, et al.. MATE: A Model-based Algorithm Tuning Engine - A proof of concept towards transparent feature-dependent parameter tuning using symbolic regression. Evolutionary Computation in Combinatorial Optimization (EvoCOP'21), Apr 2021, Sevilla (on line), Spain. pp.51-67, ⟨10.1007/978-3-030-72904-2_4⟩. ⟨hal-03233689⟩
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