Parallel single front genetic algorithm: performance analysis in a cluster system

In this paper a performance analysis in a cluster system of the parallel single front genetic algorithm (PSFGA) is carried out. The PSFGA is a parallel evolutionary optimizer for multiobjective problems that use a structured population in the form of a set of islands. The SFGA, an elitist evolutiona...

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Veröffentlicht in:Proceedings International Parallel and Distributed Processing Symposium S. 8 pp.
Hauptverfasser: De Toro, F., Ortega, J., Paechter, B.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 2003
Schlagworte:
Computer architecture
Computer science
Constraint optimization
Dissolved gas analysis
Distributed processing
Evolutionary computation
Genetic algorithms
Performance analysis
ISBN:0769519261, 9780769519265
ISSN:1530-2075
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Zusammenfassung:In this paper a performance analysis in a cluster system of the parallel single front genetic algorithm (PSFGA) is carried out. The PSFGA is a parallel evolutionary optimizer for multiobjective problems that use a structured population in the form of a set of islands. The SFGA, an elitist evolutionary algorithm with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes, is performed on each subpopulation (island) associated to a different area in the search space. Experimental results show that PSFGA outperforms SFGA and SPEA (strength Pareto evolutionary algorithm) in the cases studied.
ISBN:0769519261
9780769519265
ISSN:1530-2075
DOI:10.1109/IPDPS.2003.1213273