Quantum-Inspired Linear Genetic Programming as a Knowledge Management System

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Douglas {Mota Dias} and 
                 Marco Aurelio Cavalcanti Pacheco",
  title =        "Quantum-Inspired Linear Genetic Programming as a
                 Knowledge Management System",
  journal =      "The Computer Journal",
  year =         "2013",
  volume =       "56",
  number =       "9",
  pages =        "1043--1062",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0010-4620",
  bibdate =      "Wed Aug 28 14:23:42 MDT 2013",
  bibdate =      "2013-10-15",
  bibsource =    "DBLP,
  bibsource =    "http://comjnl.oxfordjournals.org/content/56/9.toc;
  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:
  URL =          "http://comjnl.oxfordjournals.org/content/56/9/1043.full.pdf+html",
  journal-URL =  "http://comjnl.oxfordjournals.org/",
  URL =          "http://dx.doi.org/10.1093/comjnl/bxs108",
  DOI =          "doi:10.1093/comjnl/bxs108",
  size =         "20 pages",
  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.",
  notes =        "Also known as \cite{journals/cj/DiasP13}",

Genetic Programming entries for Douglas Mota Dias Marco Aurelio Cavalcanti Pacheco