Genetic algorithms for discovery of matrix multiplication methods

Joó, A.M., Ekárt, Aniko and Neirotti, Juan P. (2012). Genetic algorithms for discovery of matrix multiplication methods. IEEE Transactions on Evolutionary Computation, 16 (5), pp. 749-751.

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: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Mathematics
Engineering & Applied 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
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://ieeexplo ... rnumber=6151102 (Publisher URL)
Published Date: 2012-10
Authors: Joó, A.M.
Ekárt, Aniko ( 0000-0001-6967-5397)
Neirotti, Juan P. ( 0000-0002-2409-8917)

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