Genetic Programming for Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{Petrowski:2017:EAch6,

• author = "Alain Petrowski and Sana Ben-Hamida",
• title = "Genetic Programming for Machine Learning",
• booktitle = "Evolutionary Algorithms",
• year = "2017",
• publisher = "John Wiley \& Sons, Inc.",
• chapter = "6",
• pages = "183--216",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, grammatical evolution, graph-based representation, intrusion detection system, linear genetic programming, linear-based representation, machine learning, tree-based representation",
• isbn13 = "9781119136378",
• URL = "http://onlinelibrary.wiley.com/doi/10.1002/9781119136378.ch6/summary",
• DOI = "doi:10.1002/9781119136378.ch6",
• size = "34 pages",
• abstract = "Genetic programming (GP) is considered as the evolutionary technique having the widest range of application domains. It can be used to solve problems in at least three main fields: optimization, automatic programming and machine learning. This chapter summarizes the different GP implementations based on one of the three representations: tree-based representation, linear-based representation and graph-based representation. It presents three of these implementations that have proven successful in practice: linear GP (LGP), grammatical evolution (GE) for linear-based representation and Cartesian GP (CGP) for graph-based representation. Several research papers explore the feasibility of applying GP to multi-category pattern classification problems. The chapter proposes a CGP-based approach to design classifiers for an Intrusion Detection problem. The major problem faced by an intrusion detection system (IDS) is the large number of false-positive alerts, i.e. normal behaviours mistakenly classified as alerts",
• notes = "Book reviewed by \cite{Downing:GPEM}",

}

Genetic Programming entries for Alain Petrowski Sana Ben Hamida

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