A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming

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

@InProceedings{Koohestani:2011:SGAI,
  author =       "Behrooz Koohestani and Riccardo Poli",
  title =        "A Hyper-Heuristic Approach to Evolving Algorithms for
                 Bandwidth Reduction Based on Genetic Programming",
  booktitle =    "Proceedings of the 31st SGAI International Conference
                 on Innovative Techniques and Applications of Artificial
                 Intelligence, AI-2011",
  year =         "2011",
  editor =       "Max Bramer and Miltos Petridis and Lars Nolle",
  pages =        "93--106",
  address =      "Cambridge, England",
  publisher_address = "London",
  month =        dec,
  organisation = "BCS special interest group on Artificial
                 Intelligence",
  publisher =    "Springer",
  note =         "Research and Development in Intelligent Systems
                 XXVIII, Incorporating Applications and Innovations in
                 Intelligent Systems XIX",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4471-2318-7",
  URL =          "http://cswww.essex.ac.uk/staff/poli/papers/KoohestaniPoli2011_SGAI_conference.pdf",
  DOI =          "doi:10.1007/978-1-4471-2318-7_7",
  size =         "14 pages",
  abstract =     "The bandwidth reduction problem is a well-known
                 NP-complete graph layout problem that consists of
                 labelling the vertices of a graph with integer labels
                 in such a way as to minimise the maximum absolute
                 difference between the labels of adjacent vertices. The
                 problem is isomorphic to the important problem of
                 reordering the rows and columns of a symmetric matrix
                 so that its non-zero entries are maximally close to the
                 main diagonal, a problem which presents itself in a
                 large number of domains in science and engineering. A
                 considerable number of methods have been developed to
                 reduce the bandwidth, among which graph-theoretic
                 approaches are typically faster and more effective. a
                 hyper-heuristic approach based on genetic programming
                 is presented for evolving graph-theoretic bandwidth
                 reduction algorithms. The algorithms generated from our
                 hyper-heuristic are extremely effective. We test the
                 best of such evolved algorithms on a large set of
                 standard benchmarks from the Harwell-Boeing sparse
                 matrix collection against two state-of-the-art
                 algorithms from the literature. Our algorithm
                 outperforms both algorithms by a significant margin,
                 clearly indicating the promise of the approach.",
  affiliation =  "School of Computer Science and Electronic Engineering,
                 University of Essex, Colchester, CO4 3SQ, UK",
}

Genetic Programming entries for Behrooz Koohestani Riccardo Poli

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