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COCO: Performance Assessment

Nikolaus Hansen 1 Anne Auger 1 Dimo Brockhoff 2 Dejan Tusar 2, 1 Tea Tušar 2 
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : We present an any-time performance assessment for benchmarking numerical optimization algorithms in a black-box scenario, applied within the COCO benchmarking platform. The performance assessment is based on runtimes measured in number of objective function evaluations to reach one or several quality indicator target values. We argue that runtime is the only available measure with a generic, meaningful, and quantitative interpretation. We discuss the choice of the target values, runlength-based targets, and the aggregation of results by using simulated restarts, averages, and empirical distribution functions.
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Contributor : Nikolaus Hansen Connect in order to contact the contributor
Submitted on : Friday, May 13, 2016 - 12:52:37 AM
Last modification on : Tuesday, October 25, 2022 - 4:16:28 PM

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  • HAL Id : hal-01315318, version 1
  • ARXIV : 1605.03560


Nikolaus Hansen, Anne Auger, Dimo Brockhoff, Dejan Tusar, Tea Tušar. COCO: Performance Assessment. {date}. ⟨hal-01315318⟩



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