A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems

Author:

Gandomi Amir Hossein,Alavi Amir Hossein

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference45 articles.

1. Walter E, Pronzato L (1997) Identification of parametric models from experimental data. Springer, London

2. Metenidis MF, Witczak M, Korbicz J (2004) A novel genetic programming approach to nonlinear system modelling: application to the DAMADICS benchmark problem. Eng Appl Art Int 17:363–370

3. Guzelbey IH, Cevik A, Gogus MT (2006) Prediction of rotation capacity of wide flange beams using neural networks. J Constr Steel Res 62(10):950–961

4. Guzelbey IH, Cevik A, Erklig A (2006) Prediction of web crippling strength of cold-formed steel sheeting using neural networks. J Constr Steel Res 62(10):962–973

5. Gandomi AH, Alavi AH (2011) Applications of computational intelligence in behavior simulation of concrete materials. In: Yang XS, Koziel S (eds) Chapter 9 in Computational optimization and applications in engineering and industry. Springer, Berlin, pp 225–249

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