The document describes KohonAnts, an algorithm that combines concepts from Kohonen's Self-Organizing Maps and Ant Colony Optimization to perform clustering and pattern classification. It works by associating input samples to "ants" that move on a grid, updating pheromone values to cluster similar samples together. Experiments on iris, glass and diabetes datasets show it outperforms KNN and other methods, with promising results for clustering and classification tasks. Future work includes additional comparisons to other algorithms and testing on more complex problems.

1. KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification Antonio M. Mora , C.M. Fernandes, J.J. Merelo, J.L.J. Laredo Dpto. ATC. UNIVERSITY OF GRANADA (Spain) V. Ramos, A.C. Rosa LASEEB-ISR/IST. UNIVERSITY OF LISBON (Portugal) ALIFE XI

10. KohonAnts Working ALIFE XI Initialization Decide Where to Go For each Ant: Pheromone Update Pheromone Evaporation END Stop criteria is TRUE No more Ants Stop criteria is FALSE

20. Thank You! ALIFE XI The sources, bin and data can be found in: https://forja.rediris.es/websvn/wsvn/geneura/KohonAnts/