Abstract
In black-box function optimization, we can choose from a wide variety of heuristic algorithms that are suited to different functions and computation budgets. Given a particular function to be optimized, the problem we consider in this paper is how to select the appropriate algorithm. In general, this problem is studied in the field of algorithm portfolios; we treat the algorithms as black boxes themselves and consider online selection (without learning mapping from problem features to best algorithms a priori and dynamically switching between algorithms during the optimization run).
We study some approaches to algorithm selection and present two original selection strategies based on the UCB1 multi-armed bandit policy applied to unbounded rewards. We benchmark our strategies on the BBOB workshop reference functions and demonstrate that algorithm portfolios are beneficial in practice even with some fairly simple strategies, though choosing a good strategy is important.
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Baudiš, P., Pošík, P. (2014). Online Black-Box Algorithm Portfolios for Continuous Optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_4
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DOI: https://doi.org/10.1007/978-3-319-10762-2_4
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