Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the (1+1)-ES with one-fifth success rule on the BBOB-2009 noisy testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
An ACO algorithm benchmarked on the BBOB noiseless function testbed
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
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.