Genetic Programming for Machine Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Petrowski:2017:EAch6,
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author = "Alain Petrowski and Sana Ben-Hamida",
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title = "Genetic Programming for Machine Learning",
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booktitle = "Evolutionary Algorithms",
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year = "2017",
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publisher = "John Wiley \& Sons, Inc.",
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chapter = "6",
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pages = "183--216",
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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",
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isbn13 = "9781119136378",
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URL = "http://onlinelibrary.wiley.com/doi/10.1002/9781119136378.ch6/summary",
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DOI = "doi:10.1002/9781119136378.ch6",
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size = "34 pages",
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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",
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notes = "Book reviewed by \cite{Downing:GPEM}",
- }
Genetic Programming entries for
Alain Petrowski
Sana Ben Hamida
Citations