Benchmarking the (1+1)-CMA-ES on the BBOB-2009 function testbed

Anne Auger, Nikolaus Hansen
2009 Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference - GECCO '09  
The (1+1)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions defined on a continuous search space in a black-box scenario. In this paper, an independent restart version of the (1+1)-CMA-ES is implemented and benchmarked on the BBOB-2009 noise-free testbed. The maximum number of function evaluations per run is set to 10 4 times the search space dimension. The algorithm solves 23, 13 and 12 of 24 functions in dimension 2, 10 and 40, respectively.
doi:10.1145/1570256.1570344 dblp:conf/gecco/AugerH09 fatcat:rxldbx4asnfonldb36bn4b76eq