Analytic programming - Symbolic Regression by means of arbitrary Evolutionary Algorithms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Zelinka:2005:IJSSST,
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author = "Ivan Zelinka and Zuzana Oplatkova and Lars Nolle",
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title = "Analytic programming - Symbolic Regression by means of
arbitrary Evolutionary Algorithms",
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journal = "International Journal of Simulation Systems, Science
\& Technology",
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year = "2005",
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volume = "6",
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number = "9",
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pages = "44--56",
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month = aug,
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note = "Special Issue on: Intelligent Systems",
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keywords = "genetic algorithms, genetic programming, symbolic
regression, grammar evolution, differential evolution,
analytic programming, SOMA",
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ISSN = "1473-8031",
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URL = "http://ducati.doc.ntu.ac.uk/uksim/journal/Vol-6/No.9/Paper5.pdf",
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size = "13 pages",
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abstract = "This contribution introduces analytical programming, a
novel method that allows solving various problems from
the symbolic regression domain. Symbolic regression was
first proposed by J. R. Koza in his genetic programming
and by C. Ryan in grammatical evolution. This
contribution explains the main principles of analytic
programming, and demonstrates its ability to synthesise
suitable solutions, called programs. It is then
compared in its structure with genetic programming and
grammatical evolution. After theoretical part, a
comparative study concerned with Boolean k-symmetry and
k-even problems from Koza's genetic programming domain
is done with analytical programming. Here, two
evolutionary algorithms are used with analytical
programming: differential evolution and self-organising
migrating algorithm. Boolean k-symmetry and k-even
problems comparative study here are continuation of
previous comparative studies done by analytic
programming in the past.",
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notes = "A publication of the United Kingdom Simulation Society
http://ducati.doc.ntu.ac.uk/uksim/journal/Vol-6/No.9/cover.htm",
- }
Genetic Programming entries for
Ivan Zelinka
Zuzana Oplatkova
Lars Nolle
Citations