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Using gradient-based information to deal with scalability in multi-objective evolutionary algorithms

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3 Author(s)
Adriana Lara ; Departamento de Computación, CINVESTAVIPN, Av. IPN No. 2508, Col. San Pedro Zacatenco, México, D.F. 07360, MEXICO ; Carlos A. Coello Coello ; Oliver Schutze

This work introduces a hybrid between an elitist multi-objective evolutionary algorithm and a gradient-based descent method, which is applied only to certain (selected) solutions. Our proposed approach requires a low number of objective function evaluations to converge to a few points in the Pareto front. Then, the rest of the Pareto front is reconstructed using a method based on rough sets theory, which also requires a low number of objective function evaluations. Emphasis is placed on the effectiveness of our proposed hybrid approach when increasing the number of decision variables, and a study of the scalability of our approach is also presented.

Published in:

2009 IEEE Congress on Evolutionary Computation

Date of Conference:

18-21 May 2009