Effect of transformations of numerical parameters in automatic algorithm configuration

We study the impact of altering the sampling space of parameters in automatic algorithm configurators. We show that a proper transformation can strongly improve the convergence towards better configurations; at the same time, biases about good parameter values, possibly based on misleading prior kno...

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Vydáno v:Optimization letters Ročník 12; číslo 8; s. 1741 - 1753
Hlavní autoři: Franzin, Alberto, Pérez Cáceres, Leslie, Stützle, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018
Témata:
Computational Intelligence
Mathematics
Mathematics and Statistics
Numerical and Computational Physics
Operations Research/Decision Theory
Optimization
Original Paper
Simulation
Parameter transformation
Automatic algorithm configuration
Parameter tuning
Numerical parameters
ISSN:1862-4472, 1862-4480
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Shrnutí:We study the impact of altering the sampling space of parameters in automatic algorithm configurators. We show that a proper transformation can strongly improve the convergence towards better configurations; at the same time, biases about good parameter values, possibly based on misleading prior knowledge, may lead to wrong choices in the transformations and be detrimental for the configuration process. To emphasize the impact of the transformations, we initially study their effect on configuration tasks with a single parameter in different experimental settings. We also propose a mechanism for how to adapt towards an appropriate transformation and give exemplary experimental results of that scheme.
ISSN:1862-4472
1862-4480
DOI:10.1007/s11590-018-1240-3