Multi-Objective ant programming for mining classification rules

Authors:
Juan Luis Olmo;José Raúl Romero;Sebastián Ventura
Affiliations:
Dept. of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain;Dept. of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain;Dept. of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain
Venue:
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Year:
2012

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Abstract

Ant programming (AP) is a kind of automatic programming that generates computer programs by using the ant colony optimization metaheuristic. It has recently demonstrated a good generalization ability when extracting classification rules. We extend the investigation on the application of AP to classification, developing an algorithm that addresses rules' evaluation using a novel multi-objective approach specially devised for the classification task. The algorithm proposed also incorporates an evolutionary computing niching procedure to increment the diversity of the population of programs found so far. Results obtained by this algorithm are compared with other three genetic programming algorithms and other industry standard algorithms from different areas, proving that multi-objective AP is a good technique at tackling classification problems.