Advisor(s)
Abstract(s)
Designing control systems using multiobjective genetic algorithms can lead to a substantial computational load as a result of the repeated evaluation of the multiple objectives and the population-based nature of the search. Here, a neural network approach, based on radial basis functions, is introduced to alleviate this problem by providing computationally inexpensive
estimates of objective values during the search. A straightforward example demonstrates the utility of the approach.
Description
Keywords
Mmultiobjective optimisation Genetic algorithms Computer-aided control system design
Citation
Duarte, N. M.; Ruano, A. E.; Fonseca, C. M.; Fleming, P. J. Accelerating multi-objective control system design using a neuro-genetic approach, Trabalho apresentado em 2000 Congress on Evolutionary Computation, In Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), La Jolla, CA, USA, 2000.