Criticality dispersion in swarms to optimize n-tuples

Book chapter


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

Page range1-8
Year2008
Book titleGECCO '08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation
PublisherAssociation for Computing Machinery
Output statusPublished
Place of publicationNew York
ISBN9781605581309
Publication dates
Print2008
Publication process dates
Deposited02 Jul 2015
Digital Object Identifier (DOI)https://doi.org/10.1145/1389095.1389097
EventGECCO '08 Proceedings of the 10th annual conference on Genetic and evolutionary computation
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