Nature vs. Nurture: The Role of Environmental Resources in Evolutionary Deep Intelligence
This work addresses the efficiency of neural network synthesis for resource-constrained applications, though it appears incremental in exploring environmental factors within an existing evolutionary framework.
The study investigated how varying simulated environmental resources affects evolutionary synthesis of deep neural networks, finding that lower resource models produced networks with more gradual performance loss and reduced storage size, with the best networks synthesized under the lowest resource conditions.
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.