Date of Award
2016
Document Type
Honors Thesis (Open Access)
Department
Colby College. Computer Science Dept.
Advisor(s)
Stephanie Taylor
Abstract
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.
Keywords
NEAT, Artificial Neural Networks, Genetic Algorithm, Neuroevolution
Recommended Citation
Kearney, William T., "Using Genetic Algorithms to Evolve Artificial Neural Networks" (2016). Honors Theses. Paper 818.https://digitalcommons.colby.edu/honorstheses/818
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons, Theory and Algorithms Commons