Genetics-based machine learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Kovacs:2012:hbnc,
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author = "Tim Kovacs",
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title = "Genetics-based machine learning",
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booktitle = "Handbook of Natural Computing",
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publisher = "Springer",
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year = "2012",
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editor = "Grzegorz Rozenberg and Thomas Baeck and Joost N. Kok",
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volume = "2",
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pages = "937--986",
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month = "19 " # aug,
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keywords = "genetic algorithms, genetic programming, Artificial
Intelligence, Machine Learning",
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isbn13 = "978-3-540-92909-3",
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broken_abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2001175",
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URL = "http://www.cs.bris.ac.uk/Publications/Papers/2001175.pdf",
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URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-540-92911-6",
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DOI = "doi:10.1007/978-3-540-92910-9_30",
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abstract = "This is a survey of the field of Genetics-based
Machine Learning (GBML): the application of
evolutionary algorithms to machine learning. We assume
readers are familiar with evolutionary algorithms and
their application to optimisation problems, but not
necessarily with machine learning. We briefly outline
the scope of machine learning, introduce the more
specific area of supervised learning, contrast it with
optimisation and present arguments for and against
GBML. Next we introduce a framework for GBML which
includes ways of classifying GBML algorithms and a
discussion of the interaction between learning and
evolution. We then review the following areas with
emphasis on their evolutionary aspects: GBML for
sub-problems of learning, genetic programming, evolving
ensembles, evolving neural networks, learning
classifier systems, and genetic fuzzy systems.",
- }
Genetic Programming entries for
Tim Kovacs
Citations