CEC Theses and Dissertations

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Date of Award

2010

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Information Systems (DISS)

Department

Graduate School of Computer and Information Sciences

Advisor

Easwar A Nyshadham

Committee Member

Sumitra Mukherjee

Committee Member

Randall S Sexton

Abstract

Parallelizing neural networks is an active area of research. Current approaches surround the parallelization of the widely used back-propagation (BP) algorithm, which has a large amount of communication overhead, making it less than ideal for parallelization. An algorithm that does not depend on the calculation of derivatives, and the backward propagation of errors, better lends itself to a parallel implementation.

One well known training algorithm for neural networks explicitly incorporates network structure in the objective function to be minimized which yields simpler neural networks. Prior work has implemented this using a modified genetic algorithm in a serial fashion that is not scalable, thus limiting its usefulness.

This dissertation created a parallel version of the algorithm. The performance of the proposed algorithm is compared against the existing algorithm using a variety of syn-thetic and real world problems. Computational experiments with benchmark datasets in-dicate that the parallel algorithm proposed in this research outperforms the serial version from prior research in finding better minima in the same time as well as identifying a simpler architecture.

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