Genetic Programming Meets Linear Algebra: How Genetic Programming Can Be Used to Find Improved Iterative Numerical Methods

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Mahmoodabadi:2017:GECCO,
  author =       "Reza Gholami Mahmoodabadi and Harald Koestler",
  title =        "Genetic Programming Meets Linear Algebra: How Genetic
                 Programming Can Be Used to Find Improved Iterative
                 Numerical Methods",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1403--1406",
  size =         "4 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082502",
  DOI =          "doi:10.1145/3067695.3082502",
  acmid =        "3082502",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, iterative
                 solvers, sparse linear algebra",
  month =        "15-19 " # jul,
  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.",
  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