A Parallel Genetic Programming Algorithm for Classification

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

@InProceedings{conf/hais/CanoZV11,

• author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
• title = "A Parallel Genetic Programming Algorithm for Classification",
• booktitle = "Proceedings of the 6th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2011) Part {I}",
• year = "2011",
• editor = "Emilio Corchado and Marek Kurzynski and Michal Wozniak",
• volume = "6678",
• series = "Lecture Notes in Computer Science",
• pages = "172--181",
• address = "Wroclaw, Poland",
• month = may # " 23-25",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, KEEL, JCLEC",
• isbn13 = "978-3-642-21218-5",
• DOI = "doi:10.1007/978-3-642-21219-2_23",
• size = "10 pages",
• abstract = "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 parallelises 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.",
• notes = "UCI",
• affiliation = "Department of Computing and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain",
• bibdate = "2011-06-06",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/hais/hais2011-1.html#CanoZV11",

}

Genetic Programming entries for Alberto Cano Rojas Amelia Zafra Gomez Sebastian Ventura

Citations