Comparison Of Crossover In Genetic Algorithm For Discrete-Time System Identification

Zainuddin, Farah Ayiesya and Abd Samad, Md Fahmi (2021) Comparison Of Crossover In Genetic Algorithm For Discrete-Time System Identification. International Review of Mechanical Engineering, 15 (2). pp. 59-66. ISSN 1970-8734

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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.

Item Type: Article
Uncontrolled Keywords: Genetic Algorithm, System Identification, Model Structure Selection, Crossover, Discrete-Time Model, ARX, NARX
Divisions: Faculty of Mechanical Engineering
Depositing User: Norfaradilla Idayu Ab. Ghafar
Date Deposited: 09 Mar 2022 16:41
Last Modified: 09 Mar 2022 16:41
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