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Quantile and mean value measures of search process complexity

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Abstract

Performance measures of metaheuristic algorithms assess the quality of a search process by statistically analysing its performance. Such criteria serve two purposes: they provide the verdict on which algorithm is better for what task, and they help applying an algorithm on a given task in the most effective way. The latter goal may be achieved by an appropriate restart strategy of the search process. Furthermore, these criteria are traditionally based on analysis of the search step mean value. Our aim is to elaborate the mean value analysis as well, but via a novel and more general quantile-based analytic approach, which can be used to define new measures. We prove and demonstrate this purpose on three quantile-based performance measures.

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Authors and Affiliations

  1. Department of Software Engineering, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Trojanova 13, 120 00, Prague, Czech Republic

    Jaromír Kukal & Matej Mojzeš

Authors
  1. Jaromír Kukal
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  2. Matej Mojzeš
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Corresponding author

Correspondence to Matej Mojzeš.

Additional information

This paper was created under the support of Grant SGS14/208/OHK4/3T/14 and SGS17/196/OHK4/3T/14 Czech Technical University in Prague.

Appendix A: MATLAB source code

Appendix A: MATLAB source code

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Kukal, J., Mojzeš, M. Quantile and mean value measures of search process complexity. J Comb Optim 35, 1261–1285 (2018). https://doi.org/10.1007/s10878-018-0251-4

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  • DOI: https://doi.org/10.1007/s10878-018-0251-4

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