Accelerating multi-objective control system design using a neuro-genetic approach

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. A neural network approach, based on radial basis functions, is introduced to...

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Bibliographic Details
Published in2000 Congress on Evolutionary Computation Vol. 1; pp. 392 - 397 vol.1
Main Authors Duarte, N.M., Ruano, A.E., Fonseca, C.M., Fleming, P.J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2000
Subjects
Acceleration
Automatic control
Control systems
Degradation
Design engineering
Design optimization
Genetic algorithms
Genetic engineering
Mmultiobjective optimisation
Neural networks
Systems engineering and theory
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Summary: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. 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.
ISBN:9780780363755
0780363752
DOI:10.1109/CEC.2000.870322