Convolutional neural network optimization using genetic algorithms
Anthony Reiling · OhioLink ETD Center (Ohio Library and Information Network) · 2017
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.iii For my family and friends, they have supported me and encouraged me to achieve a higher education I would like to thank the Air Force Research Labs (AFRL) and the University of Dayton Research Institute (UDRI) for supporting my research.I would also like to thank Dr.