Optimizing parameter design: the univariate forest-genetic method
Keywords:
Taguchi, classification and regression trees, random forest, genetic algorithm, artificial Neural NetworksAbstract
In the 80’s, Dr Genichi Taguchi developed a methodology for processes and product parameters design improvement known as the Taguchi methodology. Different proposals have emerged involving artificial intelligence techniques. Our proposal consists of a hybrid methodology that combines Random Forest (RF) and Genetic Algorithms (GA) in three phases: normalization, modeling and optimization. The first phase corresponds to the previous preparation of the data set by using normalization functions. In the modeling, the objective function is determined using strategies based on RF to predict the value of the response in a given set of parameters. Finally, in the optimization phase, the optimal combination of the parameter levels is obtained by integrating properties given by our modeling scheme into the corresponding GA. The results are compared numerically with the contributions recently found in the literature. Our methodological proposal focuses on the most important variables resulting from the RF modeling process, which allows to develop and direct more efficiently the new generations in the optimization phase, and consequently, achieve significant improvements in the quality objective considered.
Downloads
References
[2] T. P. Ryan. Statistical methods from quality improvement. Jhon Wiley Sons, 1989.
[3] S. Maghsoodloo, G. Ozdemir, V. Jordan y C.-H. Huang. Strengths and limitations of Taguchi’s contributions to quality, manufacturing and process engineering. Journal of Manufacturing Systems, 2(23):73-126, 2004.
[4] K. Tsui. A critical look at Taguchi’s modeling approach for robust design. Journal of Applied Statistics, 1(26):81-98, 1996.
[5] G. E. P. Box y R. D. Meyer. Introduction to statistical quality control. Wiley Sons, 2001.
[6] A. Miller y C.F.Wu. Manufacturing Quality Control By Means Of A Fuzzy ART Network Trained On Natural Process Data, Statistical Science, 11(2):122-136, 1996.
[7] C.-T. Su y H.-H. Chang. Optimization of parameter design: an intelligent approach using neural network and simulated annealing. International Journal of Systems Science, 31(12):1543-1549, 2000.
[8] C. Zang,M.I. Friswell y J.E.Mottershead. A review of robust optimal design and its application in dynamics. Computer and Structures, 8(3):315-326, 2005.
[9] C.-C. Chiu, C.-T. Su, G-H Yang, J.-S. Huang , S.-C. Chen y N.-T. Cheng. Selection of optimal parameter in gas-assisted injection moulding using a neural network model and the Taguchi method. International Journal of Quality Science, 2:106-120, 1997.
[10] K.M. Tay y C. Butler. Modeling and optimizing of a mig welding process-A case study using experimental designs and neural networks. Quality and Reliability Engineering International, 13:61-70, 1997
[11] H.-H. Chang. Applications of neural networks and genetic algorithms to Taguchi’s. International journal of electronic business management, 3(2):90-96, 2005.
[12] M. R Segal. Machine Learning Benchmarks and Random Forest Regression. Center for Bioinformatics and Molecular Biostatistics. University of California, San Francisco, 2003.
[13] H. Trevor, R. Tibshirani y J. Friedman. The elements of statistical learning. Data mining, inference and prediction. Springer series in statistic, 2009.
[14] D. S. Siroky. Navigation random Forest and related advances in algorithmic modeling. Statistic Surveys, 3:147-163, 2005.
[15] L. Breiman. Random Forest. Machine Learning, 45:5-32, 2001.
[16] A. Villa-Murillo, A. Carrión y S. San Matías. Modeling response variables in Taguchi design parameter using CART and Random Forest based system. Communications in dependability and quality management. An international journal, 15(4):5-15, 2012.
[17] F. Hillier y G. Lieberman. Introducción a la investigación de operaciones. McGraw Hill, 2006.
Published
How to Cite
Issue
Section
Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
The opinions expressed by the authors do not necessarily reflect the position of the publisher of the publication or of UCLA. The total or partial reproduction of the texts published here is authorized, as long as the complete source and the electronic address of this journal are cited.
The authors fully retain the rights to their works, giving the journal the right to be the first publication where the article is presented. The authors have the right to use their articles for any purpose as long as it is done for non-profit. Authors are recommended to disseminate their articles in the final version, after publication in this journal, in the electronic media of the institutions to which they are affiliated or personal digital media.
