Masters Theses
Date of Award
5-2025
Degree Type
Thesis
Degree Name
Master of Science
Major
Computer Science
Major Professor
Catherine D. Schuman
Committee Members
James Plank, Amir Sadovnik
Abstract
The optimization of spiking neural networks is a difficult task and has been handled previously by evolutionary algorithms. The usage of evolutionary algorithms allows for the optimization of more complex problem types, including multiobjective optimization. In this work, we present a method for multiobjective neuroevolution of spiking neural networks. The underlying multiobjective approach is shown to be highly effective on a suite of test problems, and the complete neuroevolution algorithm is applied to the optimization of an expanded classical control problem. The algorithm is found to be capable of evolving a diverse set of highly capable solutions, even on configurations with a fairly high number of objectives.
Recommended Citation
McCombs, Luke Philip, "Evolutionary Multiobjective Optimization of Spiking Neural Networks. " Master's Thesis, University of Tennessee, 2025.
https://trace.tennessee.edu/utk_gradthes/13891
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Controls and Control Theory Commons, Multivariate Analysis Commons