Genetic Programming Meets Linear Algebra: How Genetic Programming Can Be Used to Find Improved Iterative Numerical Methods
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- @InProceedings{Mahmoodabadi:2017:GECCO,
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author = "Reza Gholami Mahmoodabadi and Harald Koestler",
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title = "Genetic Programming Meets Linear Algebra: How Genetic
Programming Can Be Used to Find Improved Iterative
Numerical Methods",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference Companion",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4939-0",
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address = "Berlin, Germany",
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pages = "1403--1406",
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size = "4 pages",
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URL = "http://doi.acm.org/10.1145/3067695.3082502",
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DOI = "doi:10.1145/3067695.3082502",
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acmid = "3082502",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, iterative
solvers, sparse linear algebra",
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month = "15-19 " # jul,
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abstract = "Iterative schemes play central role in solving large
scale simulations in science and engineering.
Development of such methods over the past few hundreds
of years faces inevitable difficulty of manual design.
Herein, we report, for the first time, iterative
schemes that are automatically evolved by genetic
programming (GP) and outperform the well-known
iterative methods. To cope with the diversity of the
systems of linear equations, the proposed technique is
applied on a sparse system in 1D and 2D domains and on
a non-sparse asymmetric system. Our proof-of-principle
experiments demonstrate GP evolved schemes that
converge up to 4 times faster than the conventional
Gauss-Seidel scheme. Our work paves the way towards
automatic design of efficient iterative solvers for
large scale systems of linear equations.",
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notes = "Also known as
\cite{Mahmoodabadi:2017:GPM:3067695.3082502} GECCO-2017
A Recombination of the 26th International Conference on
Genetic Algorithms (ICGA-2017) and the 22nd Annual
Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Reza Gholami Mahmoodabadi
Harald Koestler
Citations