Are State-of-the-Art Fine-Tuning Algorithms Able to Detect a Dummy Parameter? View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

Elizabeth Montero , María-Cristina Riff , Leslie Pérez-Caceres , Carlos A. Coello Coello

ABSTRACT

Currently, there exist several offline calibration techniques that can be used to fine-tune the parameters of a metaheuristic. Such techniques require, however, to perform a considerable number of independent runs of the metaheuristic in order to obtain meaningful information. Here, we are interested on the use of this information for assisting the algorithm designer to discard components of a metaheuristic (e.g., an evolutionary operator) that do not contribute to improving its performance (we call them “ineffective components”). In our study, we experimentally analyze the information obtained from three offline calibration techniques: F-Race, ParamILS and Revac. Our preliminary results indicate that these three calibration techniques provide different types of information, which makes it necessary to conduct a more in-depth analysis of the data obtained, in order to detect the ineffective components that are of our interest. More... »

PAGES

306-315

References to SciGraph publications

  • 2008. New Ways to Calibrate Evolutionary Algorithms in ADVANCES IN METAHEURISTICS FOR HARD OPTIMIZATION
  • 2002-01. A Survey of Optimization by Building and Using Probabilistic Models in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2010-10. Analyzing bandit-based adaptive operator selection mechanisms in ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE
  • 2012-03. Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms in SWARM INTELLIGENCE
  • 2010-12. Autonomous operator management for evolutionary algorithms in JOURNAL OF HEURISTICS
  • Book

    TITLE

    Parallel Problem Solving from Nature - PPSN XII

    ISBN

    978-3-642-32936-4
    978-3-642-32937-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-32937-1_31

    DOI

    http://dx.doi.org/10.1007/978-3-642-32937-1_31

    DIMENSIONS

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