Genetic algorithms for discovery of matrix multiplication methods

Research output: Contribution to journalArticle

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.
Original languageEnglish
Article number6151102
Pages (from-to)749-751
Number of pages3
JournalIEEE Transactions on Evolutionary Computation
Volume16
Issue number5
Early online date10 Feb 2012
DOIs
Publication statusPublished - Oct 2012

Bibliographical note

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Keywords

  • parallel genetic algorithm
  • matrix multiplication algo-rithms
  • theoretical lower bound

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