A parallel Genetic Programming algorithm for classification

Alberto Cano, Amelia Zafra, Sebastian Ventura
Department of Computing and Numerical Analysis, University of Cordoba, Cordoba, Spain
6th international conference on Hybrid artificial intelligent systems (HAIS’11), 2011


   title={A parallel genetic programming algorithm for classification},

   author={Cano, A. and Zafra, A. and Ventura, S.},

   journal={Hybrid Artificial Intelligent Systems},





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In this paper a Grammar Guided Genetic Programming based method for the learning of rule-based classification systems is proposed. The method learns disjunctive normal form rules generated by means of a context-free grammar. The individual constitutes a rule based decision list that represents the full classifier. To overcome the problem of computational time of this system, it parallelizes the evaluation phase reducing significantly the computation time. Moreover, different operator genetics are designed to maintain the diversity of the population and get a compact set of rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.
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