Multi-Dimensional Evolutionary Algorithms for Training Neural Networks with Heterogeneous Architectures in Pricing of American Style Options
Date
2016
Authors
Sharp, Andrew Clinton
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Abstract
A 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.
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Keywords
Mathematics, American Style Options, Evolutionary algorithms, Heterogeneous Neural Networks, Machine learning, Optimization