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

Authors:
Adriana Lara;Carlos A. Coello Coello;Oliver Schütze
Affiliations:
Departamento de Computación CINVESTAV-IPN, México, D.F., Mexico;Departamento de Computación CINVESTAV-IPN, México, D.F., Mexico and UMI-LAFMIA, CNRS;Departamento de Computación CINVESTAV-IPN, México, D.F., Mexico
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

Citing 12
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Abstract

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.