The irace package: Iterated racing for automatic algorithm configuration
| Autor: | Mauro Birattari, Leslie Pérez Cáceres, Thomas Stützle, Jérémie Dubois-Lacoste, Manuel López-Ibáñez |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Algorithm configuration Control and Optimization Automatic algorithm configuration Computer science Strategy and Management 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Software ddc:330 0202 electrical engineering electronic engineering information engineering 021103 operations research Optimization algorithm business.industry lcsh:Mathematics Généralités lcsh:QA1-939 Software package Sampling distribution Iterated function Parameter tuning 020201 artificial intelligence & image processing business Racing Algorithm Premature convergence |
| Zdroj: | Lopez-Ibanez, M, Dubois-Lacoste, J, Cáceres, L P, Birattari, M & Stützle, T 2016, ' The irace Package: Iterated Racing for Automatic Algorithm Configuration ', Operations Research Perspectives, vol. 3, no. 0, pp. 43-58 . https://doi.org/10.1016/j.orp.2016.09.002 Operations Research Perspectives, 3 Operations Research Perspectives, Vol 3, Iss C, Pp 43-58 (2016) |
| ISSN: | 2214-7160 |
| DOI: | 10.1016/j.orp.2016.09.002 |
| Popis: | Modern optimization algorithms typically require the setting of a large number of parameters to optimize their performance. The immediate goal of automatic algorithm configuration is to find, automatically, the best parameter settings of an optimizer. Ultimately, automatic algorithm configuration has the potential to lead to new design paradigms for optimization software. Theirace package is a software package that implements a number of automatic configuration procedures. In particular, it offers iterated racing procedures, which have been used successfully to automatically configure various state-of-the-art algorithms. The iterated racing procedures implemented inirace include the iterated F-race algorithm and several extensions and improvements over it. In this paper, we describe the rationale underlying the iterated racing procedures and introduce a number of recent extensions. Among these, we introduce a restart mechanism to avoid premature convergence, the use of truncated sampling distributions to handle correctly parameter bounds, and an elitist racing procedure for ensuring that the best configurations returned are also those evaluated in the highest number of training instances. We experimentally evaluate the most recent version ofirace and demonstrate with a number of example applications the use and potential ofirace, in particular, and automatic algorithm configuration, in general. SCOPUS: ar.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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