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Racing with a Fixed Budget and a Self-Adaptive Significance Level

  • Juergen Branke
  • Jawad Elomari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)

Abstract

F-Race is an offline parameter tuning method which efficiently allocates samples in order to identify the parameter setting with the best expected performance, out of a given set of parameter settings. Using non parametric statistical tests, F-Race discards parameter settings which perform significantly worse than the current best, allowing the surviving parameter settings to be tested on more instances and hence obtaining better estimates for their performance. The statistical tests require setting significance levels which directly affect the algorithm’s ability of detecting the best parameter setting, and the total runtime. In this paper, we show that it is not straightforward to set the significance level and propose a simple modification to automatically adapt the significance level such that the failure rate is minimized. This is tested empirically using data drawn from probability distributions with pre-defined characteristics. Results indicate that, under a strict computational budget, F-Race with online adaptation performs significantly better than its counterpart with even the best fixed value.

Keywords

Quadratic Assignment Problem Timetabling Problem Covariance Matrix Adaptation Evolution Strategy Equal Allocation Good Parameter Setting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Warwick Business SchoolCoventryUK

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