Toward a Quantum-Inspired Linear Genetic Programming Model

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{MotaDias:2009:cec,
  author =       "Douglas {Mota Dias} and Marco Aurelio C. Pacheco",
  title =        "Toward a Quantum-Inspired Linear Genetic Programming
                 Model",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "1691--1698",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P354.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983145",
  abstract =     "The huge performance superiority of quantum computers
                 for some specific problems lies in their direct use of
                 quantum mechanical phenomena (e.g. superposition of
                 states) to perform computations. This has motivated the
                 creation of quantum-inspired evolutionary algorithms
                 (QIEAs), which successfully use some quantum physics
                 principles to improve the performance of evolutionary
                 algorithms (EAs) for classical computers. This paper
                 proposes a novel QIEA (Quantum- Inspired Linear Genetic
                 Programming - QILGP) for automatic synthesis of machine
                 code (MC) programs and aims to present a preliminary
                 evaluation of applying the quantum-inspiration paradigm
                 to evolve programs by using two symbolic regression
                 problems. QILGP performance is compared to AIMGP model,
                 since it is the most successful genetic programming
                 technique to evolve MC. In the first problem, the hit
                 ratio of QILGP (100percent) is greater than the one of
                 AIMGP (77percent). In the second problem, QILGP seems
                 to carry on a less greedy search than AIMGP. Since
                 QILGP presents some satisfactory results, this paper
                 shows that the quantum-inspiration paradigm can be a
                 competitive approach to evolve programs more
                 efficiently, which encourages further developments of
                 that first and simplest QILGP model with multiple
                 individuals.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR",
}

Genetic Programming entries for Douglas Mota Dias Marco Aurelio Cavalcanti Pacheco

Citations