Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009

Nikolaus Hansen 1, 2 Anne Auger 1 Raymond Ros 1 Steffen Finck 3 Petr Posik 4
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
Abstract : This paper presents results of the BBOB-2009 benchmark- ing of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a sin- gle convergence graph and the runtime distribution is uncov- ered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in dif- ferent subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations.
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Communication dans un congrès
ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2010, Portland, United States
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Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, Petr Posik. Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009. ACM-GECCO Genetic and Evolutionary Computation Conference, Jul 2010, Portland, United States. 〈hal-00545727〉

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