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PSFGA: a parallel genetic algorithm for multiobjective optimization

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4 Author(s)
de Toro, F. ; Univ. of Huelva, Spain ; Ortega, J. ; Fernandez, J. ; Diaz, A.

This paper presents the parallel single front genetic algorithm (PSFGA), a parallel Pareto-based algorithm for multiobjective optimization problems based on an evolutionary procedure. In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a sequential multiobjective genetic algorithm that we have devised (called single front genetic algorithm, SFGA) to its subpopulation. Experimental results are provided comparing PSFGA with previously proposed multiobjective evolutionary algorithms

Published in:

Parallel, Distributed and Network-based Processing, 2002. Proceedings. 10th Euromicro Workshop on

Date of Conference:

2002