Paper: The parallel genetic algorithm as function optimizer

Authors:
H. Mühlenbein;M. Schomisch;J. Born
Affiliations:
Gesellschaft für Mathematik und Datenverarbeitung mbH, Postfach 1240, D-5205 Sankt Augustin 1, Germany;Gesellschaft für Mathematik und Datenverarbeitung mbH, Postfach 1240, D-5205 Sankt Augustin 1, Germany;Institut für Informatik und Rechentechnik, Rudower Chaussee 5, D-1199 Berlin-Adlershof, Germany
Venue:
Parallel Computing
Year:
1991

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Abstract

In this paper, the parallel genetic algorithm PGA is applied to the optimization of continuous functions. The PGA uses a mixed strategy. Subpopulations try to locate good local minima. If a subpopulation does not progress after a number of generations, hillclimbing is done. Good local minima of a subpopulation are diffused to neighboring subpopulations. Many simulation results are given with popular test functions. The PGA is at least as good as other genetic algorithms on simple problems. A comparison with mathematical optimization methods is done for very large problems. Here a breakthrough can be reported. The PGA is able to find the global minimum of Rastrigin's function of dimension 400 on a 64 processor system! Furthermore, we give an example of a superlinear speedup.