A Genetic Programming Approach for Evolving Highly-Competitive General Algorithms for Envelope Reduction in Sparse Matrices
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{conf/ppsn/KoohestaniP12,
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author = "Behrooz Koohestani and Riccardo Poli",
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title = "A Genetic Programming Approach for Evolving
Highly-Competitive General Algorithms for Envelope
Reduction in Sparse Matrices",
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booktitle = "Parallel Problem Solving from Nature, PPSN XII (part
2)",
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year = "2012",
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editor = "Carlos A. {Coello Coello} and Vincenzo Cutello and
Kalyanmoy Deb and Stephanie Forrest and
Giuseppe Nicosia and Mario Pavone",
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volume = "7492",
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series = "Lecture Notes in Computer Science",
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pages = "287--296",
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address = "Taormina, Italy",
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month = sep # " 1-5",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming,
Hyper-Heuristic, Envelope Reduction Problem, Graph
Labelling, Sparse Matrices",
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isbn13 = "978-3-642-32936-4",
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DOI = "doi:10.1007/978-3-642-32964-7_29",
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size = "10 pages",
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abstract = "Sparse matrices emerge in a number of problems in
science and engineering. Typically the efficiency of
solvers for such problems depends crucially on the
distances between the first non-zero element in each
row and the main diagonal of the problem's matrix, a
property assessed by a quantity called the size of the
envelope of the matrix. This depends on the ordering of
the variables (i.e., the order of the rows and columns
in the matrix). So, some permutations of the variables
may reduce the envelope size which in turn makes a
problem easier to solve. However, finding the
permutation that minimises the envelope size is an
NP-complete problem. In this paper, we introduce a
hyper-heuristic approach based on genetic programming
for evolving envelope reduction algorithms. We evaluate
the best of such evolved algorithms on a large set of
standard benchmarks against two state-of-the-art
algorithms from the literature and the best algorithm
produced by a modified version of a previous
hyper-heuristic introduced for a related problem. The
new algorithm outperforms these methods by a wide
margin, and it is also extremely efficient.",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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affiliation = "School of Computer Science and Electronic Engineering,
University of Essex, CO4 3SQ UK",
- }
Genetic Programming entries for
Behrooz Koohestani
Riccardo Poli
Citations