Describing Quantum-Inspired Linear Genetic Programming from Symbolic Regression Problems

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@InProceedings{Dias:2012:CEC,
  title =        "Describing Quantum-Inspired Linear Genetic Programming
                 from Symbolic Regression Problems",
  author =       "Douglas Dias and Marco Aurelio Pacheco",
  pages =        "907--914",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
                 Computation",
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256634",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Quantum
                 Computing and Evolutionary Computation, Estimation of
                 distribution algorithms",
  abstract =     "Quantum-inspired evolutionary algorithms
                 (QIEAs)exploit principles of quantum mechanics to
                 improve the performance of classical evolutionary
                 algorithms. This paper describes the latest version of
                 a QIEA model (Quantum-Inspired Linear Genetic
                 Programming, QILGP) to evolve machine code programs.
                 QILGP is inspired on multilevel quantum systems and its
                 operation is based on quantum individuals, which
                 represent a superposition of all programs of search
                 space (solutions). Symbolic regression problems and the
                 current more efficient model to evolve machine code
                 (AIMGP) are used in comparative tests, which aim to
                 evaluate the performance impact of introducing demes
                 (subpopulations) and a limited migration strategy in
                 this version of QILGP. It outperforms AIMGP by
                 obtaining better solutions with fewer parameters and
                 operators. The performance improvement achieved by this
                 latest version of QILGP encourages its ongoing and
                 future enhancements. Thus, this paper concludes that
                 the quantum inspiration paradigm can be a competitive
                 approach to evolve programs more efficiently.",
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",
}

Genetic Programming entries for Douglas Mota Dias Marco Aurelio Cavalcanti Pacheco

Citations