class_docstr: Object which carries out genetic search and returns top performing model
  upon completion.
code: def _evaluate_population(self, members, epochs, fitness, igen, ngen):
      fit = []
      for imem, mem in enumerate(members):
          self._print_evaluation(imem, len(members), igen, ngen)
          res = self._evaluate(mem, epochs)
          v = res[self._metric_index]
          del res
          fit.append(v)
  
      fit = np.array(fit)
      self._print_result(fit, igen)
      return _Population(members, fit, fitness, obj=self._objective)
  
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 8 for calling the DEvol obj's underlying member method code with example usage: obj._evaluate_population(members, epochs, fitness, igen, ngen) and returns: _Population