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

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 104 times the search space dimension. The algorithm solves 23, 13 and 12 of 24 functions in dimension 2, 10 and 40, respectively. Categories and Subject Descriptors

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Anne Auger, Nikolaus Hansen
• Keywords: Adaptive Stochastic Algorithm, Continuous Search Space, GECCO 2009, Optimization, Search Space