Criticality dispersion in swarms to optimize n-tuples

Azhar, M. H. B., Deravi, F. and Dimond, K. (2008) Criticality dispersion in swarms to optimize n-tuples. In: UNSPECIFIED, ed. GECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation. New York: Association for Computing Machinery. pp. 1-8 ISBN 9781605581309

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Abstract

Among numerous pattern recognition methods the neural network approach has been the subject of much research due to its ability to learn from a given collection of representative examples. This paper concerns with the optimization of a weightless neural network, which decomposes a given pattern into several sets of n points, termed n-tuples. A population-based stochastic optimization technique, known as Particle Swarm Optimization (PSO), has been used to select an optimal set of connectivity patterns to improve the recognition performance of such .n-tuple. classifiers. The original PSO was refined by combining it with a bio-inspired technique called the Self-Organized Criticality (SOC) to add diversity in the population for finding better solutions. The hybrid algorithms were adapted for the n-tuple system and the performance was measured in selecting better connectivity patterns. The aim was to improve the discriminating power of the classifier in recognizing handwritten characters by exploiting the criticality dispersion in the swarm population. This paper presents the implementation of the hybrid model in greater detail with the effect of criticality dispersion in finding better solutions.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Divisions: Faculty of Social and Applied Sciences > School of Law, Criminal Justice and Computing
Depositing User: Dr. Hannan Azhar
Date Deposited: 02 Jul 2015 15:31
Last Modified: 02 Jul 2015 15:31
URI: https://create.canterbury.ac.uk/id/eprint/13525

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Last edited: 29/06/2016 12:23:00