Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence

TL;DR

This paper investigates how external environmental resources influence the evolution of neural network architectures, demonstrating that limited resources can lead to more efficient models with minimal performance loss.

Contribution

It introduces a study on environmental resource variation in evolutionary deep intelligence, revealing that lower resource availability yields more efficient networks with preserved accuracy.

Findings

Lower environmental factors lead to gradual performance decline.

Reduced storage size with minimal accuracy loss.

Best networks synthesized under minimal environmental resources.

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.