This solver is implemented in
optunity.solvers.CMA_ES. It as available in
optunity.make_solver() as ‘cma-es’.
CMA-ES stands for Covariance Matrix Adaptation Evolutionary Strategy. This is an evolutionary strategy for continuous function optimization. It can dynamically adapt its search resolution per hyperparameter, allowing for efficient searches on different scales. More information is available in [HANSEN2001].
Optunity’s implementation of this solver is done using the DEAP toolbox [DEAP2012]. This, in turn, requires NumPy. Both dependencies must be met to use this solver.
|[HANSEN2001]||Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2):159-195, 2001.|
|[DEAP2012]||Felix-Antoine Fortin, Francois-Michel De Rainville, Marc-Andre Gardner, Marc Parizeau and Christian Gagne, DEAP: Evolutionary Algorithms Made Easy, Journal of Machine Learning Research, pp. 2171-2175, no 13, jul 2012.|