Convolutional neural network optimization using genetic algorithms
Date of Award
2017
Degree Name
M.S. in Computer Engineering
Department
Department of Electrical and Computer Engineering
Advisor/Chair
Advisor: Eric John Balster
Abstract
This thesis proposes the use of a genetic algorithm (GA) to optimize the accuracy of a convolutional neural network (CNN). The GA modifies the structure of the CNN such as the number of convolutional filters, strides, kernel size, nodes, learning parameters, etc. Each modification of the network is trained and evaluated. Mutation of evolved networks create more successful networks over multiple generations. The final evolved network is 4.77% more accurate than a network proposed in the previous literature. Additionally, the evolved network is 13.4% less computationally complex.
Keywords
Genetic algorithms Data processing, Neural networks (Computer science), System design Data processing, Artificial Intelligence, Computer Engineering, Computer Science, deep learning hyper parameter genetic algorithm evolutionary computation convolutional neural network optimization CNN DL GA CIFAR10
Rights Statement
Copyright © 2017, author
Recommended Citation
Reiling, Anthony Joseph, "Convolutional neural network optimization using genetic algorithms" (2017). Graduate Theses and Dissertations. 1337.
https://ecommons.udayton.edu/graduate_theses/1337
