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