class_docstr: Object which carries out genetic search and returns top performing model
  upon completion.
code: def _reproduce(self, pop, gen):
      members = []
  
      # 95% of population from crossover
      for _ in range(int(len(pop) * 0.95)):
          members.append(self._crossover(pop.select(), pop.select()))
  
      # best models survive automatically
      members += pop.get_best(len(pop) - int(len(pop) * 0.95))
  
      # randomly mutate
      for imem, mem in enumerate(members):
          members[imem] = self._mutate(mem, gen)
      return members
  
imports: from __future__ import print_function
  import random
  import csv
  import operator
  import gc
  import os
  from datetime import datetime
  from keras.callbacks import EarlyStopping
  from keras.models import load_model
  import keras.backend
  from sklearn.metrics import log_loss
  import numpy
label_desc: Use image node_id 4 for calling the DEvol obj's underlying member method code with example usage: obj._reproduce(pop, gen) and returns: members