class_docstr: A GSGP class, inherited from GP.
  
  This is the designed class to define GSGP-related
  variables and methods.
  
  References:
      A. Moraglio, K. Krawiec, and C. G. Johnson.
      Geometric semantic genetic programming.
      Lecture Notes in Computer Science (2012).
  
      G. H. de Rosa, J. P. Papa, and L. P. Papa.
      Feature selection using geometric semantic genetic programming.
      Proceedings of the Genetic and Evolutionary Computation Conference Companion (2017).
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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.
  
      """
  
      logger.info("Overriding class: GP -> GSGP.")
  
      super(GSGP, self).__init__(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/evolutionary/gsgp.py
imports: import copy
  from hashlib import sha1
  from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.general
  import opytimizer.math.random
  from opytimizer.core.node import Node
  from opytimizer.optimizers.evolutionary.gp import GP
  from opytimizer.spaces.tree import TreeSpace
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new GSGP object from inherited base classes: GP with example: obj = GSGP(params)