class_docstr: Evolutionary optimization of Neural Networks.
  
  Parameters:
  -----------
  n_individuals: int
      The number of neural networks that are allowed in the population at a time.
  mutation_rate: float
      The probability that a weight will be mutated.
  model_builder: method
      A method which returns a user specified NeuralNetwork instance. 
class_docstr_tok: ["Evolutionary","optimization","of","Neural","Networks",".","Parameters",":","--","--","--","--","--","-","n_individuals",":","int","The","number","of","neural","networks","that","are","allowed","in","the","population","at","a","time",".","mutation_rate",":","float","The","probability","that","a","weight","will","be","mutated",".","model_builder",":","method","A","method","which","returns","a","user","specified","NeuralNetwork","instance","."]
docstr: Initialization of the neural networks forming the population
docstr_tok: ["Initialization","of","the","neural","networks","forming","the","population"]
code: def _initialize_population(self):
      """Initialization of the neural networks forming the population"""
      self.population = []
      for _ in range(self.population_size):
          model = self._build_model(id=np.random.randint(1000))
          self.population.append(model)
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/neuroevolution.py
imports: from __future__ import print_function, division
  import numpy
  import copy
label_desc: Use image node_id 3 for calling the Neuroevolution obj's underlying member method code with example usage: obj._initialize_population() without return types