Abstract
The article proposes a multi-objective optimization method called Generalized Differential Evolution (GDE3) for a Variable Number of Dimensions (VND). The well-known generalized differential evolution is adapted to handle problems where the number of decision space variables is not a priori known. The performance of the method is assessed on a set of benchmark problems based on standard multi-objective test cases modified to depend on the number of decision space variables. Experimental results include a study of the setting of controlling parameters and a comparison with the state-of-the-art Variable-Length GDE3 (VLGDE3) algorithm. Finally, the novel approach is compared to the standard approach in a linear antenna array design problem.
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