class_docstr: An UMDA class, inherited from Optimizer.
  
  This is the designed class to define UMDA-related variables and methods.
  
  References:
      H. Mühlenbein. The equation for response to selection and its use for prediction.
      Evolutionary Computation (1997).
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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(UMDA, self).__init__()
  
      self.p_selection = 0.75
      self.lower_bound = 0.05
      self.upper_bound = 0.95
  
      self.build(params)
  
      logger.info("Class overrided.")
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new UMDA object from inherited base classes: Optimizer with example: obj = UMDA(params)