Master of Science (MS)
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
In the field of Computer Science, neural networks and genetic algorithms have become very popular tools in solving complex problems. Because of this growing popularity, there has been several attempts to combine the two concepts. Some of these attempts focused on using genetic algorithms to determine the best architecture, starting weights, or feature selection, to name of few of the applications. While a lot of the research that is available focuses on solving more than one element of the neural network design or is looking to use genetic algorithms to replace a part of the traditional neural network, such as back propagation, in this paper we focus on solving one key element of the network. We will show that it is possible to use a genetic algorithm to determine the best learning rate to be used when training a network, as opposed to the simple manual trial-and-error method that is used by most in the field today.
Neural networks (Computer science); Genetic algorithms; Learning
University of Nevada, Las Vegas
Miller, Eric, "Calculating the Learning Rate of a Neural Network using a Genetic Algorithm" (2021). UNLV Theses, Dissertations, Professional Papers, and Capstones. 4304.
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/