@article {BERLANGA20101183, title = {GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems}, journal = {Information Sciences}, volume = {180}, number = {8}, year = {2010}, pages = {1183 - 1200}, abstract = {In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy 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.}, keywords = {classification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off}, issn = {0020-0255}, doi = {https://doi.org/10.1016/j.ins.2009.12.020}, url = {http://www.sciencedirect.com/science/article/pii/S0020025509005635}, author = {F.J. Berlanga and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera} }