An implementation of genetic algorithms for rule based machine learning

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

@Article{Sette:2000:EAAI,

• author = "S. Sette and L. Boullart",
• title = "An implementation of genetic algorithms for rule based machine learning",
• journal = "Engineering Applications of Artificial Intelligence",
• year = "2000",
• volume = "13",
• pages = "381--390",
• number = "4",
• keywords = "genetic algorithms, genetic programming, Genetic based machine learning, Learning classifier systems, Fuzzy efficiency based classifier systems, Textiles, Production process",
• ISSN = "0952-1976",
• owner = "wlangdon",
• URL = "http://www.sciencedirect.com/science/article/B6V2M-40FGBJY-1/2/78ce2dab77a99bf15b8dec2aab6ccd61",
• DOI = "doi:10.1016/S0952-1976(00)00020-8",
• abstract = "Genetic algorithms have given rise to two new fields of research where (global) optimisation is of crucial importance: 'Genetic Programming' and 'Genetic based Machine Learning' (GBML). An overview of one of the first GBML implementations by Holland, also known as the Learning Classifier Systems (LCS) will be given. After describing and solving a well-known basic (educational) problem a more complex application of GBML is presented. The goal of this application is the automatic development of a rule set for an industrial production process. To this end, the case study on generating a rule set for predicting the spinnability in the fibre-to-yarn production process will be presented. A largely modified LCS, called Fuzzy Efficiency based Classifier System (FECS), originally designed by one of the authors, is used to solve this problem successfully.",

}

Genetic Programming entries for Stefan Sette Luc Boullart

Citations