Gentle, JamesSharp, Andrew Clinton2017-01-292017-01-292016https://hdl.handle.net/1920/10593A common problem in training artificial neural networks is determining the optimal network architecture for the particular problem. An ontology is presented that allows for the generalization of the multi-dimensional differential evolution (MD-DE) algorithm in training an ensemble of neural networks with heterogeneous architectures. This generalized algorithm is referred to as the multi-dimensional evolutionary algorithm (MD-EA) since it provides a framework for performing any of the evolutionary optimization algorithms such as differential evolution or genetic algorithms on neural networks with heterogeneous architectures.151 pagesenCopyright 2016 Andrew Clinton SharpMathematicsAmerican Style OptionsEvolutionary algorithmsHeterogeneous Neural NetworksMachine learningOptimizationDissertation