class_docstr: A DE class, inherited from Optimizer.
  
  This is the designed class to define DE-related
  variables and methods.
  
  References:
      R. Storn. On the usage of differential evolution for function optimization.
      Proceedings of North American Fuzzy Information Processing (1996).
class_docstr_tok: ["A","DE","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","DE-related","variables","and","methods",".","References",":","R.","Storn",".","On","the","usage","of","differential","evolution","for","function","optimization",".","Proceedings","of","North","American","Fuzzy","Information","Processing","(","1996",")","."]
docstr: Initialization method.
  
  Args:
      params: Contains key-value parameters to the meta-heuristics.
docstr_tok: ["Initialization","method",".","Args",":","params",":","Contains","key-value","parameters","to","the","meta-heuristics","."]
code: def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
      """Initialization method.
  
      Args:
          params: Contains key-value parameters to the meta-heuristics.
  
      """
  
      super(DE, self).__init__()
  
      self.CR = 0.9
      self.F = 0.7
  
      self.build(params)
  
      logger.info("Class overrided.")
  
imports: import copy
  from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.distribution
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new DE object from inherited base classes: Optimizer with example: obj = DE(params)