Numerical black-box optimization benchmarking framework. New Features in v2.3: support of discrete variables new bbob-largescale test suite with 24 functions in dimensions 20 to 640 new bbob-mixint test suite, a discretized version of the bbob test suite new bbob-biobj-mixint test suite, a discretized version of the bbob-biobj-ext test suite the order of input arguments to cocopp.main is preserved as the order in which algorithms are displayed and in particular the order in which the colors are chosen. improved COCODataArchive revised example_experiment2.py Solved Bugs bug in SMS-EMOA example (issue #1853) Others adjustments to support matplotlib 3.0.
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, exten...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Release for the BBOB-2023 workshop. Important changes Improved styles in the ECDF graphs. Existing ...
Release for final papers of BBOB-2019 workshop. New Features: Data sets from both the bbob-biobj...
International audienceBenchmarking of optimization solvers is an important and compulsory task for p...
submitted to GECCO 2019International audienceWe introduce two suites of mixed-integer benchmark prob...
COmparing Continuous Optimisers (COCO) is a tool for benchmarking algorithms for black-box optimisat...
International audienceThe Comparing Continuous Optimizers platform COCO has become a standard for be...
International audienceIn this paper, we benchmark a variant of the well-known NSGA-II algorithm of D...
International audienceWe introduce COCO, an open source platform for Comparing Continuous Optimizers...
Release 2.6 for BBOB-2022 workshop experiments New Features: bbob-constrained test suite with 54 pr...
Release 2.5.1 for BBOB-2022 workshop experiments New Features: bbob-constrained test suite with 54 ...
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, exten...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Release for the BBOB-2023 workshop. Important changes Improved styles in the ECDF graphs. Existing ...
Release for final papers of BBOB-2019 workshop. New Features: Data sets from both the bbob-biobj...
International audienceBenchmarking of optimization solvers is an important and compulsory task for p...
submitted to GECCO 2019International audienceWe introduce two suites of mixed-integer benchmark prob...
COmparing Continuous Optimisers (COCO) is a tool for benchmarking algorithms for black-box optimisat...
International audienceThe Comparing Continuous Optimizers platform COCO has become a standard for be...
International audienceIn this paper, we benchmark a variant of the well-known NSGA-II algorithm of D...
International audienceWe introduce COCO, an open source platform for Comparing Continuous Optimizers...
Release 2.6 for BBOB-2022 workshop experiments New Features: bbob-constrained test suite with 54 pr...
Release 2.5.1 for BBOB-2022 workshop experiments New Features: bbob-constrained test suite with 54 ...
The bbob-largescale test suite, containing 24 single-objective functions in continuous domain, exten...
Quantifying and comparing performance of optimization algorithms is one important aspect of research...
Release for the BBOB-2023 workshop. Important changes Improved styles in the ECDF graphs. Existing ...