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

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Research and Development in Intelligent Systems XXVIII (SGAI 2011)

Abstract

The bandwidth reduction problem is a well-known NP-complete graphlayout problem that consists of labeling the vertices of a graph with integer labels in such a way as to minimize 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. In this paper, 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.

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Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, CO4 3SQ, UK

    Behrooz Koohestani & Riccardo Poli

Authors
  1. Behrooz Koohestani
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  2. Riccardo Poli
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Correspondence to Behrooz Koohestani .

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Editors and Affiliations

  1. University of Portsmouth, Lion Terrace, Portsmouth, PO1 3HE, United Kingdom

    Max Bramer

  2. School of Computing &, Mathematical Sciences, University of Greenwich, Park Row 30, London, SE10 9LS, United Kingdom

    Miltos Petridis

  3. , School of Computing and Informatics, Nottingham Trent University, Burton Street, Nottingham, NG1 4BU, United Kingdom

    Lars Nolle

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Koohestani, B., Poli, R. (2011). A Hyper-Heuristic Approach to Evolving Algorithms for Bandwidth Reduction Based on Genetic Programming. In: Bramer, M., Petridis, M., Nolle, L. (eds) Research and Development in Intelligent Systems XXVIII. SGAI 2011. Springer, London. https://doi.org/10.1007/978-1-4471-2318-7_7

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  • DOI: https://doi.org/10.1007/978-1-4471-2318-7_7

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