class_docstr: Wrapper the Covariance Matrix Adaptation Evolutionary strategy (CMA-ES) optimization method. It works generating
  an stochastic search based on multivariate Gaussian samples. Only requires f and the box constraints to work.
class_docstr_tok: ["Wrapper","the","Covariance","Matrix","Adaptation","Evolutionary","strategy","(","CMA-ES",")","optimization","method",".","It","works","generating","an","stochastic","search","based","on","multivariate","Gaussian","samples",".","Only","requires","f","and","the","box","constraints","to","work","."]
code: def __init__(self, bounds, maxiter=1000):
      super(OptCma, self).__init__(bounds)
      self.maxiter = maxiter
  
imports: import numpy
  from ..core.errors import InvalidVariableNameError
label_desc: Use image node_id 1 to create a new OptCma object from inherited base classes: Optimizer with example: obj = OptCma(bounds, maxiter)