Online Black-Box Algorithm Portfolios for Continuous Optimization

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00220064" target="_blank" >RIV/68407700:21230/14:00220064 - isvavai.cz</a>
Result on the web
<a href="http://pasky.or.cz/sci/cocopf-opt13/optpf-extended.pdf" target="_blank" >http://pasky.or.cz/sci/cocopf-opt13/optpf-extended.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Online Black-Box Algorithm Portfolios for Continuous Optimization
Original language description
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 apriori 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.
Czech name
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Czech description
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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