DOI: https://doi.org/10.15368/theses.2013.154
Available at: https://digitalcommons.calpoly.edu/theses/1024
Date of Award
8-2013
Degree Name
MS in Computer Science
Department/Program
Computer Science
Advisor
Franz J. Kurfess
Abstract
This paper discusses a modification to improve usability and functionality of a ge- netic neural net algorithm called NEAT (NeuroEvolution of Augmenting Topolo- gies). The modification aims to accomplish its goal by automatically changing parameters used by the algorithm with little input from a user. The advan- tage of the modification is to reduce the guesswork needed to setup a successful experiment with NEAT that produces a usable Artificial Intelligence (AI). The modified algorithm is tested against the unmodified NEAT with several different setups and the results are discussed. The algorithm shows strengths in some areas but can increase the runtime of NEAT due to the addition of parameters into the solution search space.