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

Authors:
Hongmei He;Ondrej Sýkora;Ana Salagean;Erkki Mäkinen
Affiliations:
Department of Computer Science, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;Department of Computer Science, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;Department of Computer Science, Loughborough University, Loughborough, Leicestershire LE11 3TU, UK;Department of Computer Sciences, University of Tampere, P.O. Box 607, FIN-33014, Finland
Venue:
Journal of Parallel and Distributed Computing
Year:
2007

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Abstract

Genetic algorithms (GAs) 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 GAs. This paper investigates the complexity of parallel genetic algorithms (PGAs) based on two evaluation measures: computation time to communication time and population size to chromosome size. 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.