Abstract
This chapter introduces the basic concepts and notation of genetic algorithms, which is a basic search methodology that can be used for modelling and simulation of complex non-linear dynamical systems. Since this technique can be considered as general purpose optimization methodologies, we can use them to find the mathematical model which minimizes the fitting errors for a specific problem. On the other hand, we can also use this technique for simulation if we exploit their efficient search capabilities to find the appropriate parameter values for a specific mathematical model. We can use a genetic algorithm to optimize the number of rules or the membership functions of a fuzzy system for a specific problem. These are two important application of genetic algorithms, which will be used in later chapters to design intelligent systems for controlling real-world dynamical systems.
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Castillo, O., Aguilar, L.T. (2019). Genetic Algorithms. In: Type-2 Fuzzy Logic in Control of Nonsmooth Systems. Studies in Fuzziness and Soft Computing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-030-03134-3_2
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DOI: https://doi.org/10.1007/978-3-030-03134-3_2
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