Evolutionary Computation: from Genetic Algorithms to Genetic Programming

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

@InCollection{intro:2006:GSP,
  author =       "Ajith Abraham and Nadia Nedjah and 
                 Luiza {de Macedo Mourelle}",
  title =        "Evolutionary Computation: from Genetic Algorithms to
                 Genetic Programming",
  year =         "2006",
  booktitle =    "Genetic Systems Programming: Theory and Experiences",
  pages =        "1--20",
  volume =       "13",
  series =       "Studies in Computational Intelligence",
  editor =       "Nadia Nedjah and Ajith Abraham and 
                 Luiza {de Macedo Mourelle}",
  publisher =    "Springer",
  address =      "Germany",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  ISBN =         "3-540-29849-5",
  URL =          "http://www.softcomputing.net/gpsystems.pdf",
  DOI =          "doi:10.1007/3-540-32498-4_1",
  abstract =     "Evolutionary computation, offers practical advantages
                 to the researcher facing difficult optimisation
                 problems. These advantages are multi-fold, including
                 the simplicity of the approach, its robust response to
                 changing circumstance, its flexibility, and many other
                 facets. The evolutionary approach can be applied to
                 problems where heuristic solutions are not available or
                 generally lead to unsatisfactory results. As a result,
                 evolutionary computation have received increased
                 interest, particularly with regards to the manner in
                 which they may be applied for practical problem
                 solving.

                 we review the development of the field of evolutionary
                 computations from standard genetic algorithms to
                 genetic programming, passing by evolution strategies
                 and evolutionary programming. For each of these
                 orientations, we identify the main differences from the
                 others. We also, describe the most popular variants of
                 genetic programming. These include linear genetic
                 programming (LGP), gene expression programming (GEP),
                 multi-expression programming (MEP), Cartesian genetic
                 programming (CGP), traceless genetic programming (TGP),
                 gramatical evolution (GE) and genetic algorithm for
                 deriving software (GADS).",
  notes =        "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
  size =         "21 pages",
}

Genetic Programming entries for Ajith Abraham Nadia Nedjah Luiza de Macedo Mourelle

Citations