text: abstract—evolutionary deep intelligence synthesizes highly
  efficient deep neural networks architectures over successive
  generations. inspired by the nature versus nurture debate, we
  propose a study to examine the role of external factors on
  the network synthesis process by varying the availability of
  simulated environmental resources. experimental results were
  obtained for networks synthesized via asexual evolutionary synthesis (1-parent) and sexual evolutionary synthesis (2-parent,
  3-parent, and 5-parent) using a 10% subset of the mnist
  dataset. results show that a lower environmental factor model
  resulted in a more gradual loss in performance accuracy and
  decrease in storage size. this potentially allows significantly
  reduced storage size with minimal to no drop in performance
  accuracy, and the best networks were synthesized using the
  lowest environmental factor models.
  keywords-deep neural networks; deep learning; evolutionary deep intelligence; evolutionary synthesis; environmental
  resource models