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On Benchmarking Stochastic Global Optimization Algorithms
Volume 26, Issue 4 (2015), pp. 649–662
Eligius M.T. Hendrix   Algirdas Lančinskas  

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https://doi.org/10.15388/Informatica.2015.69
Pub. online: 1 January 2015      Type: Research Article     

Received
1 March 2015
Accepted
1 June 2015
Published
1 January 2015

Abstract

A multitude of heuristic stochastic optimization algorithms have been described in literature to obtain good solutions of the box-constrained global optimization problem often with a limit on the number of used function evaluations. In the larger question of which algorithms behave well on which type of instances, our focus is here on the benchmarking of the behavior of algorithms by applying experiments on test instances. We argue that a good minimum performance benchmark is due to pure random search; i.e. algorithms should do better. We introduce the concept of the cumulative distribution function of the record value as a measure with the benchmark of pure random search and the idea of algorithms being dominated by others. The concepts are illustrated using frequently used algorithms.

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Vilnius University

Keywords
stochastic global optimization benchmark black-box meta-heuristic

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INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
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