Evolving problem heuristics with on-line ACGP
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{1274017,
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author = "Cezary Z. Janikow",
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title = "Evolving problem heuristics with on-line {ACGP}",
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booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
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year = "2007",
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month = "7-11 " # jul,
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editor = "Peter A. N. Bosman",
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isbn13 = "978-1-59593-698-1",
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pages = "2503--2508",
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address = "London, United Kingdom",
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keywords = "genetic algorithms, genetic programming, heuristics,
machine learning, STGP, artificial ant",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2503.pdf",
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DOI = "doi:10.1145/1274000.1274017",
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publisher = "ACM Press",
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publisher_address = "New York, NY, USA",
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size = "6 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:
types, syntax, and heuristics. However, in general the
user may not be aware of the best representation space,
including heuristics, to solve a particular problem.
Recently, the ACGP methodology for extracting
problem-specific heuristics, and thus for learning
model of the problem domain, was introduced with
preliminary off-line results. This paper overviews
ACGP, pointing out its strength and limitations in the
off-line mode. It then introduces a new on-line model,
for learning while solving a problem, illustrated with
experiments involving the multiplexer and the Santa Fe
trail.",
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notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
- }
Genetic Programming entries for
Cezary Z Janikow
Citations