Meta-learning genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Meuth:2010:geccocomp,
  author =       "Ryan J. Meuth",
  title =        "Meta-learning genetic programming",
  booktitle =    "GECCO 2010 Late breaking abstracts",
  year =         "2010",
  editor =       "Daniel Tauritz",
  isbn13 =       "978-1-4503-0073-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "2101--2102",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830761.1830882",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In computational intelligence, the term 'memetic
                 algorithm' has come to be associated with the
                 algorithmic pairing of a global search method with a
                 local search method. In a sociological context, a
                 'meme' has been loosely defined as a unit of cultural
                 information, the social analog of genes for
                 individuals. Both of these definitions are inadequate,
                 as 'memetic algorithm' is too specific, and ultimately
                 a misnomer, as much as a 'meme' is defined too
                 generally to be of scientific use. In this paper, we
                 extend the notion of memes from a computational
                 viewpoint and explore the purpose, definitions, design
                 guidelines and architecture for effective memetic
                 computing. Using two genetic programming test-beds (the
                 even-parity problem and the Pac-Man video game), we
                 demonstrate the power of high-order meme-based
                 learning, known as meta-learning. With applications
                 ranging from cognitive science to machine learning,
                 meta-learning has the potential to provide much-needed
                 stimulation to the field of computational intelligence
                 by providing a framework for higher order learning.",
  notes =        "Also known as \cite{1830882} Distributed on CD-ROM at
                 GECCO-2010.

                 ACM Order Number 910102.",
}

Genetic Programming entries for Ryan J Meuth

Citations