Date of Award
2012
First Advisor
Paul Shields
Second Advisor
Brian Wynne
Abstract
NEAT (NeuroEvolution of Augmenting Topologies) is a genetic algorithm (GA) for artificial neural network (ANN) training. Evolutionary algorithms like NEAT can be used to study evolutionary behaviors. This thesis examines the effects of applying NEAT to the simulation of birds, controlled by ANNs, in a predator-prey environment. The birds are given only a motivation to survive, with the intention of observing whether or not more complex behaviors can emerge. The results positively indicated the emergence of complex behaviors as a symptom of survival.
Recommended Citation
Goldshtrom, Eitan, "A NEAT Training Algorithm for Artificial Neural Networks" (2012). Senior Theses. 665.
https://digitalcommons.bard.edu/sr-theses/665
Simon's Rock students and employees can log in from off-campus by clicking on the Off-campus Download button and entering their Simon's Rock username and password.