Discovery of the Boolean Functions to the Best Density-Classification Rules Using Gene Expression Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{ferreira:2002:EuroGP,
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title = "Discovery of the {Boolean} Functions to the Best
Density-Classification Rules Using Gene Expression
Programming",
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author = "C\^andida Ferreira",
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editor = "James A. Foster and Evelyne Lutton and
Julian Miller and Conor Ryan and Andrea G. B. Tettamanzi",
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booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
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volume = "2278",
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series = "LNCS",
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pages = "50--59",
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publisher = "Springer-Verlag",
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address = "Kinsale, Ireland",
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publisher_address = "Berlin",
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month = "3-5 " # apr,
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year = "2002",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-43378-3",
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DOI = "doi:10.1007/3-540-45984-7_5",
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abstract = "Cellular automata are idealized versions of massively
parallel, decentralized computing systems capable of
emergent behaviours. These complex behaviors result
from the simultaneous execution of simple rules at
multiple local sites. A widely studied behavior
consists of correctly determining the density of an
initial configuration, and both human and
computer-written rules have been found that perform
with high efficiency at this task. However, the two
best rules for the density-classification task,
Coevolution1 and Coevolution2, were discovered using a
coevolutionary algorithm in which a genetic algorithm
evolved the rules and, therefore, only the output bits
of the rules are known. However, to understand why
these and other rules perform so well and how the
information is transmitted throughout the cellular
automata, the Boolean expressions that orchestrate this
behaviour must be known. The results presented in this
work are a contribution in that direction.",
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notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
- }
Genetic Programming entries for
Candida Ferreira
Citations