Results from BBOB-2009: all functions COCO (COmparing Continuous Optimizers) is a platform for systematic and sound comparisons of real-parameter global optimizers. COCO provides benchmark function testbeds, experimentation templates which are easy to parallelize, and tools for processing and visualizing data generated by one or several optimizers. The COCO platform has been used for the Black-Box-Optimization-Benchmarking (BBOB) workshops since 2009.

The COCO experiment source code has been rewritten in the years 2014-2015 and the current production code is available on our COCO github page. The old code is still available at the downloads page and shall be used for experiments on the noisy test suite until this test suite will be available in the new code as well.

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Internal wiki:

Selected Results

The figures show selected results from BBOB 2009. The empirical runtime distribution is shown for six subgroups of the BBOB functions. Click on the respective figures for more details. (At the top of this page the aggregated results over all functions are shown). More plots (for all supported COCO test suites) can be found in the

 Results from BBOB-2009: separable functions   Results from BBOB-2009: moderate functions  Results from BBOB-2009: ill-conditioned functions  Results from BBOB-2009: multimodal structured functions  Results from BBOB-2009: multimodal weakly structured functions  Results from BBOB-2009: non-smooth functions

Current BBOB Functions

The current 24 noiseless test functions are

Only f1 and f5 are purely quadratic or linear respectively.

See also N. Hansen et al (2010): Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009. Workshop Proceedings of the GECCO Genetic and Evolutionary Computation Conference 2010, ACM. 1)

The current 30 noisy test functions are


1) This work was partially funded by FUI of System@tic Paris-Region ICT cluster through contract DGT 117 407 Complex Systems Design Lab (CSDL), and by the ANR-2010-COSI-002 grant (SIMINOLE) of the French National Research Agency.
2) Work on Coco has been funded to a great extend via the ANR project NumBBO and in the context of the PGMO project AESOP. We also acknowledge support via Inria's ADT funding.
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