Title

Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing

Abstract

The escalation of Neural Network research in Business has been brought about by the ability of neural networks, as a tool, to closely approximate unknown functions to any degree of desired accuracy. Although, gradient based search techniques such as back-propagation are currently the most widely used optimization techniques for training neural networks, it has been shown that these gradient techniques are severely limited in their ability to find global solutions. Global search techniques have been identified as a potential solution to this problem. In this paper we examine two well known global search techniques, Simulated Annealing and the Genetic Algorithm, and compare their performance. A Monte Carlo study was conducted in order to test the appropriateness of these global search techniques for optimizing neural networks.

Document Type

Article

DOI

https://doi.org/10.1016/S0377-2217(98)00114-3

Keywords

Extrapolation, Genetic algorithm, Global solutions, Interpolation, Neural networks, Optimization, Simulated annealing

Publication Date

5-1-1999

Journal Title

European Journal of Operational Research

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