Chapter

Genetic and Evolutionary Computation — GECCO 2003

Volume 2724 of the series Lecture Notes in Computer Science pp 1588-1589

Date:

A Genetic Algorithm as a Learning Method Based on Geometric Representations

  • Gregory A. HolifieldAffiliated withSchool of Electrical Engineering and Computer Science, University of Central Florida
  • , Annie S. WuAffiliated withSchool of Electrical Engineering and Computer Science, University of Central Florida

* Final gross prices may vary according to local VAT.

Get Access

Abstract

A number of different methods combining the use of neural networks and genetic algorithms have been described [1]. This paper discusses an approach for training neural networks based on the geometric representation of the network. In doing so, the genetic algorithm becomes applicable as a common training method for a number of machine learning algorithms that can be similarly represented. The experiments described here were specifically derived to construct claim regions for Fuzzy ARTMAP Neural Networks [2],[3].