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Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives

In: Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Author

Listed:
  • Thomas Bartz-Beielstein

    (TH Köln, Institute for Data Science, Engineering, and Analytics)

  • Frederik Rehbach

    (TH Köln, Institute for Data Science, Engineering, and Analytics)

  • Margarita Rebolledo

    (TH Köln, Institute for Data Science, Engineering, and Analytics)

Abstract

The focus of this paper lies on automatic and interactive tuning methods for stochastic optimization algorithms, e.g., evolutionary algorithms. Algorithm tuning is important because it helps to avoid wrong parameter settings, to improve the existing algorithms, to select the best algorithm for working with a real-world problem, to show the value of a novel algorithm, to evaluate the performance of an optimization algorithm when different option settings are used, and to obtain an algorithm instance that is robust to changes in problem specification. This chapter discusses strategical issues and defines eight key topics for tuning, namely, optimization algorithms, test problems, experimental setup, performance metrics, reporting, parallelization, tuning methods, and software. Features of established tuning software packages such as IRACE, SPOT, SMAC, and ParamILS are compared.

Suggested Citation

  • Thomas Bartz-Beielstein & Frederik Rehbach & Margarita Rebolledo, 2021. "Tuning Algorithms for Stochastic Black-Box Optimization: State of the Art and Future Perspectives," Springer Optimization and Its Applications, in: Panos M. Pardalos & Varvara Rasskazova & Michael N. Vrahatis (ed.), Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, pages 67-108, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-66515-9_3
    DOI: 10.1007/978-3-030-66515-9_3
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