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Genetic Programming Metamodel for Rotating Beams

Anuj Pratap Singh, V. Mani, Ranjan Ganguli1

Department of Aerospace Engineering, Indian Institute ofScience, Bangalore-560012, India.

Computer Modeling in Engineering & Sciences 2007, 21(2), 133-148.


This paper investigates the use of Genetic Programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.


Cite This Article

APA Style
Singh, A.P., Mani, V., Ganguli, R. (2007). Genetic programming metamodel for rotating beams. Computer Modeling in Engineering & Sciences, 21(2), 133-148.
Vancouver Style
Singh AP, Mani V, Ganguli R. Genetic programming metamodel for rotating beams. Comput Model Eng Sci. 2007;21(2):133-148
IEEE Style
A.P. Singh, V. Mani, and R. Ganguli "Genetic Programming Metamodel for Rotating Beams," Comput. Model. Eng. Sci., vol. 21, no. 2, pp. 133-148. 2007.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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