code: def __init__(self, members, fitnesses, score, obj="max"):
      self.members = members
      scores = fitnesses - fitnesses.min()
      if scores.max() > 0:
          scores /= scores.max()
      if obj == "min":
          scores = 1 - scores
      if score:
          self.scores = score(scores)
      else:
          self.scores = scores
      self.s_fit = sum(self.scores)
  
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 2 to create a new _Population object from inherited base classes: object with example: obj = _Population(members, fitnesses, score, obj)