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Parallelisation of genetic algorithms for the 2-page crossing number problem

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journal contribution
posted on 25.09.2006, 11:27 by Hongmei He, Ondrej Sykora, A.M. Salagean, Erkki Makinen
Genetic algorithms have been applied to solve the 2-page crossing number problem successfully, but since they work with one global population, the search time and space are limited. Parallelisation provides an attractive prospect to improve the efficiency and solution quality of genetic algorithms. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: Computation-time to Communication-time and Population-size to Chromosomesize. Moreover, the paper unifies the framework of PGA models with the function PGA (subpopulation size; cluster size, migration period; topology), and explores the performance of PGAs for the 2-page crossing number problem.

History

School

  • Science

Department

  • Computer Science

Pages

867796 bytes

Citation

HE et al, 2007. Parallelisation of genetic algorithms for the 2-page crossing number problem. Journal of parallel and distributed computing, 66(2), pp. 229-241

Publisher

© Elsevier

Publication date

2007

Notes

This article is to be published in the journal, Journal of parallel and distributed computing [© Elsevier] and will also be available at: http://www.sciencedirect.com/science/journal/07437315

ISSN

0743-7315

Language

en