Quantifying and comparing performance of optimization algorithms is one important aspect of research in search and optimization. However, this task turns out to be tedious and difficult to realize even in the single-objective case -- at least if one is willing to accomplish it in a scientifically decent and rigorous way. The COCO platform furnishes most of this tedious task for the experimenter: (1) choice and implementation of a well-motivated single-objective benchmark function testbed, (2) design of an experimental set-up, (3) generation of data output for (4) post-processing and presentation of the results in graphs and tables. In this report, the experimental procedure for the BBOB-2010 benchmarking workshop and data formats are thorou...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
ArXiv e-prints, arXiv:1604.00359International audienceSeveral test function suites are being used fo...
International audienceDirect Multisearch (DMS) and MultiGLODS are two derivative-free solvers for ap...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
ArXiv e-prints, arXiv:1603.08776We present a budget-free experimental setup and procedure for benchm...
ArXiv e-prints, arXiv:1603.08785International audienceWe introduce COCO, an open source platform for...
International audienceExisting studies in black-box optimization for machine learning suffer from lo...
International audienceThe Comparing Continuous Optimizers platform COCO has become a standard for be...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Un problème d'optimisation continue peut se définir ainsi : étant donné une fonction objectif de R à...
pp. 1689-1696This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on ...
International audienceOne of the main goals of the COCO platform is to produce, collect , and make a...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
ArXiv e-prints, arXiv:1604.00359International audienceSeveral test function suites are being used fo...
International audienceDirect Multisearch (DMS) and MultiGLODS are two derivative-free solvers for ap...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
ArXiv e-prints, arXiv:1603.08776We present a budget-free experimental setup and procedure for benchm...
ArXiv e-prints, arXiv:1603.08785International audienceWe introduce COCO, an open source platform for...
International audienceExisting studies in black-box optimization for machine learning suffer from lo...
International audienceThe Comparing Continuous Optimizers platform COCO has become a standard for be...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Un problème d'optimisation continue peut se définir ainsi : étant donné une fonction objectif de R à...
pp. 1689-1696This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on ...
International audienceOne of the main goals of the COCO platform is to produce, collect , and make a...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
ArXiv e-prints, arXiv:1604.00359International audienceSeveral test function suites are being used fo...
International audienceDirect Multisearch (DMS) and MultiGLODS are two derivative-free solvers for ap...