Evolving an Improved Algorithm for Envelope Reduction Using a Hyper-Heuristic Approach

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@Article{Koohestani:2014:ieeeTEC,
  author =       "Behrooz Koohestani and Riccardo Poli",
  title =        "Evolving an Improved Algorithm for Envelope Reduction
                 Using a Hyper-Heuristic Approach",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  volume =       "18",
  number =       "4",
  pages =        "543--558",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, graph theory,
                 optimisation, search methods, sparse matrices",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2281512",
  size =         "16 pages",
  abstract =     "Large sparse symmetric matrices are typical
                 characteristics of the linear systems found in various
                 scientific and engineering disciplines such as fluid
                 mechanics, structural engineering, finite element
                 analysis and network analysis. In all such systems, the
                 performance of solvers crucially depends on the sum of
                 the distance of each matrix element from the matrix's
                 main diagonal. This quantity is known as the envelope.
                 In order to reduce the envelope of a matrix, its rows
                 and columns should be reordered properly. The problem
                 of minimising the envelope size is exceptionally hard
                 and known to be NP-complete. A considerable number of
                 methods have been developed for reducing the envelope
                 among which the Sloan algorithm offered a substantial
                 improvement over previous algorithms. In this paper, a
                 hyper-heuristic approach based on genetic programming,
                 which we term Genetic Hyper-Heuristic, is introduced
                 for evolving an enhanced version of the Sloan
                 algorithm. We also present a local search algorithm and
                 integrate it with the new algorithm produced by our
                 hyper-heuristic in order to further improve the quality
                 of the solutions. The new algorithms are evaluated on a
                 large set of standard benchmarks against six
                 state-of-the-art algorithms from the literature. Our
                 algorithms outperform all the methods under test by a
                 wide margin.",
  notes =        "Also known as \cite{6600959}",
}

Genetic Programming entries for Behrooz Koohestani Riccardo Poli

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