Abstract
System identification is a process where a mathematical model is derived in order to explain dynamical behaviour of a system. One of its step is model structure selection and it is crucial that, in this step, an adequate model i.e. a model with a good balance between parsimony and accuracy of the model is selected in approximating the system. Genetic algorithm (GA), a method known for optimisation, is used for selecting a model structure. GA is known to be able to reduce much computational burden. This paper investigates the effect of different types of crossover, namely, single-point, double-point, multiple-point and uniform crossover, within GA in producing an optimum model structure for system identification. This was carried out using a computational software on a number of simulated data. As a conclusion, using Akaike Information Criterion as objective function, single point crossover produces the model with the best balance in most of the tests.
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