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Parallelisation of genetic algorithms for the 2-page crossing number problem
journal contribution
posted on 2006-09-25, 11:27 authored by Hongmei He, Ondrej Sykora, A.M. Salagean, Erkki MakinenGenetic 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 bytesCitation
HE et al, 2007. Parallelisation of genetic algorithms for the 2-page crossing number problem. Journal of parallel and distributed computing, 66(2), pp. 229-241Publisher
© ElsevierPublication date
2007Notes
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/07437315ISSN
0743-7315Language
- en