Adaptable Representation in GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{janikow:gecco05ws,
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author = "Cezary Z. Janikow",
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title = "Adaptable Representation in {GP}",
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booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2005)} workshop program",
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year = "2005",
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month = "25-29 " # jun,
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editor = "Franz Rothlauf and Misty Blowers and
J{\"u}rgen Branke and Stefano Cagnoni and Ivan I. Garibay and
Ozlem Garibay and J{\"o}rn Grahl and Gregory Hornby and
Edwin D. {de Jong} and Tim Kovacs and Sanjeev Kumar and
Claudio F. Lima and Xavier Llor{\`a} and
Fernando Lobo and Laurence D. Merkle and Julian Miller and
Jason H. Moore and Michael O'Neill and Martin Pelikan and
Terry P. Riopka and Marylyn D. Ritchie and Kumara Sastry and
Stephen L. Smith and Hal Stringer and
Keiki Takadama and Marc Toussaint and Stephen C. Upton and
Alden H. Wright",
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publisher = "ACM Press",
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address = "Washington, D.C., USA",
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pages = "327--331",
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keywords = "genetic algorithms, genetic programming, ACGP,
Heuristics, Representation",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2005wks/papers/0327.pdf",
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DOI = "doi:10.1145/1102256.1102329",
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size = "5 pages",
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abstract = "Genetic Programming uses trees to represent
chromosomes. The user defines the representation space
by defining the set of functions and terminals to label
the nodes in the trees. The sufficiency principle
requires that the set be sufficient to label the
desired solution trees, often forcing the user to
enlarge the set, thus also enlarging the search space.
Structure-preserving crossover, STGP, CGP, and
CFG-based GP give the user the power to reduce the
space by specifying rules for valid tree construction,
based on types, syntax, and heuristics. These rules in
effect change the representation. However, in general
the user may not be aware of the best representation,
including heuristics, to solve a particular problem.
Last year, ACGP methodology was introduced for
extracting local problem-specific heuristics, that is
for learning a local model of the problem domain. ACGP
discovers representation, in the space of probabilistic
representations, one that improves the search itself
and that provides the user with heuristics about the
domain. We discuss and illustrate the probabilistic
representation.",
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notes = "Distributed on CD-ROM at GECCO-2005. ACM
1-59593-097-3/05/0006",
- }
Genetic Programming entries for
Cezary Z Janikow
Citations