Genetic algorithms for discovery of matrix multiplication methods

Abstract

We present a parallel genetic algorithm for nding matrix multiplication algo-rithms. For 3 x 3 matrices our genetic algorithm successfully discovered algo-rithms requiring 23 multiplications, which are equivalent to the currently best known human-developed algorithms. We also studied the cases with less mul-tiplications and evaluated the suitability of the methods discovered. Although our evolutionary method did not reach the theoretical lower bound it led to an approximate solution for 22 multiplications.

Publication DOI: https://doi.org/10.1109/TEVC.2011.2159270
Divisions: ?? 50811700Jl ??
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: parallel genetic algorithm,matrix multiplication algo-rithms,theoretical lower bound,Software,Computational Theory and Mathematics,Theoretical Computer Science
Publication ISSN: 1089-778X
Last Modified: 06 Jun 2024 07:08
Date Deposited: 25 Jun 2013 14:12
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... rnumber=6151102 (Publisher URL)
PURE Output Type: Article
Published Date: 2012-10
Published Online Date: 2012-02-10
Authors: Joó, A.M.
Ekárt, Aniko (ORCID Profile 0000-0001-6967-5397)
Neirotti, Juan P. (ORCID Profile 0000-0002-2409-8917)

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