class_docstr: Differential Evolution optimization.
      
class_docstr_tok: ["Differential","Evolution","optimization","."]
docstr: Minimize a function using differential evolution.
  
  Uses a differential evolution algorithm to find the minimum of a function of
  one or more variables.
  
  Args:
      cost (function, default=None): function to be minimized: ``y = cost(x)``.
      termination (termination, default=None): termination conditions.
      ExtraArgs (tuple, default=None): extra arguments for cost.
      strategy (strategy, default=Best1Bin): the mutation strategy for generating        new trial solutions.
      CrossProbability (float, default=0.9): the probability of cross-parameter
          mutations.
      ScalingFactor (float, default=0.8): multiplier for mutations on the trial
          solution.
      sigint_callback (function, default=None): signal handler callback function.
      callback (function, default=None): function called after each iteration. The
          interface is ``callback(xk)``, with ``xk`` the current parameter vector.
      disp (bool, default=False): if True, print convergence messages.
  
  Returns:
      None
          
docstr_tok: ["Minimize","a","function","using","differential","evolution",".","Uses","a","differential","evolution","algorithm","to","find","the","minimum","of","a","function","of","one","or","more","variables",".","Args",":","cost","(","function",",","default=None",")",":","function","to","be","minimized",":","``","y","=","cost","(","x",")","``",".","termination","(","termination",",","default=None",")",":","termination","conditions",".","ExtraArgs","(","tuple",",","default=None",")",":","extra","arguments","for","cost",".","strategy","(","strategy",",","default=Best1Bin",")",":","the","mutation","strategy","for","generating","new","trial","solutions",".","CrossProbability","(","float",",","default=0.9",")",":","the","probability","of","cross-parameter","mutations",".","ScalingFactor","(","float",",","default=0.8",")",":","multiplier","for","mutations","on","the","trial","solution",".","sigint_callback","(","function",",","default=None",")",":","signal","handler","callback","function",".","callback","(","function",",","default=None",")",":","function","called","after","each","iteration",".","The","interface","is","``","callback","(","xk",")","``",",","with","``","xk","``","the","current","parameter","vector",".","disp","(","bool",",","default=False",")",":","if","True",",","print","convergence","messages",".","Returns",":","None"]
code: def Solve(self, cost=None, termination=None, ExtraArgs=None, **kwds):
      """Minimize a function using differential evolution.
  
      Uses a differential evolution algorithm to find the minimum of a function of
      one or more variables.
  
      Args:
          cost (function, default=None): function to be minimized: ``y = cost(x)``.
          termination (termination, default=None): termination conditions.
          ExtraArgs (tuple, default=None): extra arguments for cost.
          strategy (strategy, default=Best1Bin): the mutation strategy for generating        new trial solutions.
          CrossProbability (float, default=0.9): the probability of cross-parameter
              mutations.
          ScalingFactor (float, default=0.8): multiplier for mutations on the trial
              solution.
          sigint_callback (function, default=None): signal handler callback function.
          callback (function, default=None): function called after each iteration. The
              interface is ``callback(xk)``, with ``xk`` the current parameter vector.
          disp (bool, default=False): if True, print convergence messages.
  
      Returns:
          None
      """
      super(DifferentialEvolutionSolver, self).Solve(
          cost, termination, ExtraArgs, **kwds
      )
      return
  
imports: from mystic.tools import wrap_function, unpair, isiterable
  from mystic.tools import wrap_bounds, wrap_penalty, reduced
  from mystic.abstract_solver import AbstractSolver
  from mystic.abstract_map_solver import AbstractMapSolver
  from numpy import asfarray, ravel
  from collections.abc import Callable
label_desc: Use image node_id 7 for calling the DifferentialEvolutionSolver obj's underlying member method code with example usage: obj.Solve(cost, termination, ExtraArgs) without return types