Use of Nature-inspired Meta-heuristics for Handwritten Digits Recognition
Salima Nebti, Abdellah Boukerram
International Journal of Computational Linguistics Research, Vol. 1, No. 1, 2010.
DOI: 
Link: http://www.dline.info/jcl/fulltext/v1n1/4.pdf
Abstract:
Character recognition is an important task in pattern analysis that aims to give significance to handwritten data without usersâ€™ intervention. Although, an intensive research has been devoted to this problem, it remains a challenging task as humans need to interact with computer in the easiest way. This work attempts to incorporate some meta-heuristics as guidelines searching for the best solution of handwritten digits recognition problem namely the particle swarm optimizer and variations of the beesâ€™ algorithm. The beesâ€™ algorithm is a variant of evolutionary optimization that take inspiration from the foraging behavior of honey bees where individuals called bees are used to perform a neighborhood search in joint with a random search as an attempt to achieve a good balance between exploration and exploitation abilities. We show that this method can be adapted to handwritten characters recognition and can be effectively combined with a neural network classifier which results in a good quality on a wide range of real data against that of the k-nearest neighbour classifier and the back-propagation training algorithm.