Title

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

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