Date of Award


Document Type

Honors Thesis (Open Access)


Colby College. Computer Science Dept.


Stephanie Taylor


This paper demonstrates that neuroevolution is an effective method to determine an optimal neural network topology. I provide an overview of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, and describe how unique characteristics of this algorithm solve various problem inherent to neuroevolution (namely the competing conventions problem and the challenges associated with protecting topological innovation). Parallelization is shown to greatly speed up efficiency, further reinforcing neuroevolution as a potential alternative to traditional backpropagation. I also demonstrate that appropriate parameter selection is critical in order to efficiently converge to an optimal topology. Lastly, I produce an example solution to a medical classification machine learning problem that further demonstrates some unique advantages of the NEAT algorithm.


NEAT, Artificial Neural Networks, Genetic Algorithm, Neuroevolution