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Optimizing the Cost Matrix for Approximate String Matching using Genetic Algorithms

Marc Parizeau, Nadia Ghazzali and Jean-François Hébert

Abstract - This paper describes a method for optimizing the cost matrix of any approximate string matching algorithm based on the Levenshtein distance. The method, which uses genetic algorithms, de nes the problem formally as a discrimination between a set of classes. It is tested and evaluated using both synthetically generated strings of symbols and chain code data extracted from the international Unipen database of online handwritten scripts. Experimental results show that this approach can e ectively discover the hidden costs of elementary operations in a set of string classes.

Bibtex:

@article{Parizeau57,
author    = { Marc Parizeau and Nadia Ghazzali and Jean-François Hébert },
title     = { Optimizing the Cost Matrix for Approximate String Matching using Genetic Algorithms },
volume    = { 31 },
number    = { 4 },
pages     = { 431-440 },
year      = { 1998 },
month     = { April },
journal   = { Pattern Recognition }
}

Dernière modification: 2002/06/14 par parizeau

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