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PSFGA: a parallel genetic algorithm for multiobjective optimization

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

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

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

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