Learning fuzzy rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Berlanga:2005:GFS,

• title = "Learning fuzzy rules using Genetic Programming: Context-free grammar definition for high-dimensionality problems",
• author = "Francisco Jose Berlanga and Maria Jose {Del Jesus} and Francisco Herrera",
• booktitle = "International workshop on Genetic Fuzzy System, GFS 2005",
• year = "2005",
• editor = "Oscar Cordon and Francisco Herrera",
• keywords = "genetic algorithms, genetic programming",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.415.2932",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.415.2932",
• URL = "http://sci2s.ugr.es/keel/pdf/specific/congreso/gfs2005.pdf",
• broken = "http://sci2s.ugr.es/publications/ficheros/berlanga_deljesus_herrera_GFS05.pdf",
• size = "5 pages",
• abstract = "The inductive learning of a fuzzy rule-based classification system (FRBCS) with high interpretability is made difficult by the presence of a large number of features that increases the dimensionality of the problem being solved. The difficult comes from the exponential growth of the fuzzy rule search space with the increase in the number of features considered. In this work we tackle this problem, the FRBCS learning with high interpretability for high-dimensionality problems. We propose a genetic-programming-based method, where the evolved disjunctive normal form fuzzy rules compete in order to obtain an FRBCS with high interpretability (few rules and few antecedent conditions per rule) while maintaining a good performance.",

}

Genetic Programming entries for Francisco Jose Berlanga Maria Jose del Jesus Francisco Herrera

Citations