Abstract
This paper investigates a new model that takes advantage of the cooperative self-organization of Ant Algorithms to evolve a naturally inspired pattern recognition (and also clustering) method. The approach considers each data item as an ant that changes the environment as it moves through it. The algorithm is successfully applied to well-known classification problems and yields better results than some other classification approaches, like K-Nearest Neighbours and Neural Networks.
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Fernandes, C., Mora, A.M., Merelo, J.J., Ramos, V., Laredo, J.L., Rosa, A. (2008). KANTS: Artifical Ant System for Classification. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_35
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DOI: https://doi.org/10.1007/978-3-540-87527-7_35
Publisher Name: Springer, Berlin, Heidelberg
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