Conference Paper

Using Gradient-Based Information to Deal with Scalability in Multi-Objective Evolutionary Algorithms

Dept. de Comput., CINVESTAVIPN, Mexico City
DOI: 10.1109/CEC.2009.4982925 Conference: Evolutionary Computation, 2009. CEC '09. IEEE Congress on
Source: IEEE Xplore


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.

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