Quantum-Inspired Linear Genetic Programming as a Knowledge Management System
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{Dias:2013:QIL,
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author = "Douglas {Mota Dias} and
Marco Aurelio Cavalcanti Pacheco",
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title = "Quantum-Inspired Linear Genetic Programming as a
Knowledge Management System",
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journal = "The Computer Journal",
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year = "2013",
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volume = "56",
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number = "9",
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pages = "1043--1062",
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month = sep,
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keywords = "genetic algorithms, genetic programming",
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ISSN = "0010-4620",
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bibdate = "Wed Aug 28 14:23:42 MDT 2013",
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bibdate = "2013-10-15",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/cj/cj56.html#DiasP13",
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bibsource = "http://comjnl.oxfordjournals.org/content/56/9.toc;
http://www.math.utah.edu/pub/tex/bib/compj2010.bib",
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acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
801 581 4148, e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|, \path|beebe@computer.org|
(Internet), URL:
\path|http://www.math.utah.edu/~beebe/|",
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URL = "http://comjnl.oxfordjournals.org/content/56/9/1043.full.pdf+html",
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journal-URL = "http://comjnl.oxfordjournals.org/",
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URL = "http://dx.doi.org/10.1093/comjnl/bxs108",
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DOI = "doi:10.1093/comjnl/bxs108",
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size = "20 pages",
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abstract = "The superior performance of quantum computers in some
problems lies in the direct use of quantum mechanics
phenomena. This ability has originated the
quantum-inspired evolutionary algorithms (QIEAs), which
are classical algorithms (for classical computers) that
exploit quantum mechanics principles to improve their
performance. Several proposed QIEAs are able to
outperform their traditional counterparts when applied
to different kinds of problems. Aiming to exploit this
new paradigm on genetic programming (GP), this paper
introduces a novel QIEA model (quantum-inspired linear
GP QuaLiGP), which evolves machine code programs.
QuaLiGP is inspired on multi-level quantum systems, and
its operation is based on quantum individuals, which
represent a superposition of all programs (solutions)
of the search space. The tests use symbolic regression
and binary classification as knowledge management
problems to assess the QuaLiGP performance and compare
it with Automatic Induction of Machine Code by Genetic
Programming model, which is currently the most
efficient GP model to evolve machine code. Results show
that QuaLiGP outperforms the reference GP system for
all these problems, by achieving better solutions from
a smaller number of evaluations and by using fewer
parameters and operators. This paper concludes that the
quantum-inspired paradigm can be a competitive approach
to evolve programs efficiently, encouraging
improvements and extensions of QuaLiGP.",
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notes = "Also known as \cite{journals/cj/DiasP13}",
- }
Genetic Programming entries for
Douglas Mota Dias
Marco Aurelio Cavalcanti Pacheco
Citations