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Multiobjective Metaheuristics (MOMH) permit to conceive a complete novel approach to induce classifiers. In the Rule Learning problem, the use of MOMH permit that the properties of the rules can be expressed in different objectives, and then the algorithm finds these rules in an unique run by exploring Pareto dominance concepts. This work describes a Multiobjective Particle Swarm Optimization (MOPSO) algorithm that handles with numerical and discrete attributes. The algorithm is evaluated by using the area under ROC curve and the approximation sets produced by the algorithm are also analyzed following Multiobjective methodology.