initialize one or several options. Arguments --------- `dict_or_str` a dictionary if ``val is None``

Argument `y` is a phenotypic vector, return `y` put into boundaries, as a copy iff ``y != into_bounds(y)``. Note: this code is dup

compute the Mahalanobis norm that is induced by the adapted covariance matrix C times sigma**2. Argument ------

make sure that solutions fit to sample distribution, this interface will probably change. In particular the frequency of x - self.mean bein

get new candidate solutions, sampled from a multi-variate normal distribution and transformed to f-representation (phenotype) to be

abstract method, display some iteration infos if ``self.iteration_counter % modulo == 0``

Evaluates all option values in environment `loc`. :See: `eval()`

functional interface to the stochastic optimizer CMA-ES for non-convex function minimization. Calling Sequences =================

compute the hash key of ``x``

return all options that match, in the name or the description, with string `s`, case is disregarded. Example: ``cma.Options().matc

register a `CMAEvolutionStrategy` instance for logging, ``append=True`` appends to previous data logged under the same name, by defa