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COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting

Nikolaus Hansen 1 Anne Auger 1 Olaf Mersmann 2 Tea Tušar 3 Dimo Brockhoff 3
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : COCO is a platform for Comparing Continuous Optimizers in a black-box setting. It aims at automatizing the tedious and repetitive task of benchmarking numerical optimization algorithms to the greatest possible extent. We present the rationals behind the development of the platform as a general proposition for a guideline towards better benchmarking. We detail underlying fundamental concepts of COCO such as its definition of a problem, the idea of instances, the relevance of target values, and runtime as central performance measure. Finally, we give a quick overview of the basic code structure and the available test suites.
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Contributor : Nikolaus Hansen <>
Submitted on : Thursday, July 28, 2016 - 6:41:18 PM
Last modification on : Tuesday, April 21, 2020 - 1:07:51 AM


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  • HAL Id : hal-01294124, version 3
  • ARXIV : 1603.08785


Nikolaus Hansen, Anne Auger, Olaf Mersmann, Tea Tušar, Dimo Brockhoff. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. 2016. ⟨hal-01294124v3⟩



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