Abstract
The objective of the third part of this book is to present a path for transferring computational intelligence from the realm of exciting research ideas into a highly competitive generator of valuable solutions to real-world problems. The proposed application strategy begins in this chapter by emphasizing the importance of integrating various approaches for resolving real-world problems. Various ways to introduce, apply, and leverage computational intelligence in a business are discussed in Chap. 12. Chapter 13 presents different techniques for addressing one of the key issues in the application strategy of computational intelligence – marketing the technology to technical and nontechnical audiences. The last chapter in the third part, Chap. 14, includes examples of successful and failed industrial applications.
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Notes
- 1.
A. Kordon, F. Castillo, G. Smits, and M. Kotanchek, Application issues of genetic programming in industry, Genetic Programming Theory and Practice III, T. Yu, R. Riolo, and B. Worzel (eds), Springer, pp. 241–258, 2006.
- 2.
P. Bonissone, Y. Chen, K. Goebel, and P. Khedkar, Hybrid soft computing systems: industrial and commercial applications, Proceedings of the IEEE, 87, no. 9, pp. 1641–1667, 1999.
- 3.
O. Gusikhin, N. Rychtyckyj, and D. Filev, Intelligent systems in the automotive industry: Applications and trends, Knowl. Inf. Syst., 12, 2, pp. 147–168, 2007.
- 4.
A. Kordon, Hybrid intelligent systems for industrial data analysis, International Journal of Intelligent Systems, 19, pp. 367–383, 2004.
- 5.
L. Medsker, Hybrid Intelligent Systems, Kluwer, 1995.
- 6.
http://www.nerogame.org
- 7.
F. Castillo, A. Kordon, J. Sweeney, and W. Zirk, Using genetic programming in industrial statistical model building, Genetic Programming Theory and Practice II., U.-M. O'Reilly, T. Yu, R. Riolo, and B. Worzel (eds), Springer, pp. 31–48, 2004.
- 8.
A statistical measure that indicates that the model does not fit the data properly.
- 9.
The initial version of the methodology is published in: A. Kordon, G. Smits, E. Jordaan and E. Rightor, Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines, Proceedings of WCCI 2002, Honolulu, pp. 896–901, 2002.
- 10.
L. Eriksson, E. Johansson, N. Wold, and S. Wold, Multi and Megavariate Data Analysis: Principles and Applications, Umeå, Sweden, Umetrics Academy, 2003.
- 11.
The number is suggested from practical experience.
- 12.
The method is described in: G. Smits, A. Kordon, E. Jordaan, C. Vladislavleva, and M. Kotanchek, Variable selection in industrial data sets using Pareto genetic programming, Genetic Programming Theory and Practice III, T. Yu, R. Riolo, and B. Worzel (eds), Springer, pp. 79–92, 2006.
- 13.
The idea was proposed by Flor Castillo initially for designed data and is fully described in F. Castillo, K. Marshall, J. Green, and A. Kordon, Symbolic regression in design of experiments: A case study with linearizing transformations, Proceedings of GECCO 2002, New York, pp. 1043–1048, 2002.
- 14.
Variance Inflation Factor (VIF) is a statistical measure of collinearity between input variables.
- 15.
F. Castillo and C. Villa, Symbolic regression in multicollinearity problems, Proceedings of GECCO 2005, Washington, D.C., pp. 2207–2208, 2005.
- 16.
The material in this section was originally published in F. Castillo, A. Kordon, J. Sweeney, and W. Zirk, Using genetic programming in industrial statistical model building, Genetic Programming Theory and Practice II, U.-M. O'Reilly, T. Yu, R. Riolo, and B. Worzel (eds), Springer, pp. 31–48, 2004. With kind permission of Springer-Verlag.
- 17.
G. Box and N. Draper, Empirical Model Building and Response Surfaces, Wiley, 1987.
- 18.
F. Castillo, A. Kordon, J. Sweeney, and W. Zirk, Using genetic programming in industrial statistical model building, Genetic Programming Theory and Practice II, U.-M. O'Reilly, T. Yu, R. Riolo, and B. Worzel (eds), Springer, pp. 31–48, 2004.
- 19.
The parameters are selected according to the values recommended in: F. Castillo, A. Kordon, and G. Smits, Robust Pareto front genetic programming parameter selection based on design of experiments and industrial data, In: R. Riolo and B. Worzel (eds): Genetic Programming Theory and Practice IV, Springer, pp. 149–166, 2007.
- 20.
JMP is a registered trademark of SAS Institute Inc., Cary, NC, USA.
Suggested Reading
The following books give detailed technical descriptions of the key integration methods of computational intelligence techniques:
L. Jain and N. Martin, Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithm : Industrial Applications, CRC Press, 1999.
R. Khosla and T. Dillon, Engineering Intelligent Hybrid Multi-Agent System, Kluwer, 1995.
L. Medsker, Hybrid Intelligent Systems, Kluwer, 1995.
Z. Michalewicz, M. Schmidt, M. Michalewicz, and C. Chiriac, Adaptive Business Intelligence, Springer, 2007.
D. Ruan (Editor), Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithm , Kluwer, 1997.
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Kordon, A.K. (2010). Integrate and Conquer. In: Applying Computational Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69913-2_11
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DOI: https://doi.org/10.1007/978-3-540-69913-2_11
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