Forest-Genetic method to optimize parameter design of multiresponse experiment

• Adriana Villa-Murillo ^[1] ; Andrés Carrión ^[2] ; Antonio Sozzi ^[3]
  1. [1] Universidad Viña del Mar

     Universidad Viña del Mar

     Viña del Mar, Chile

     
  2. [2] Universidad Politécnica de Valencia

     Universidad Politécnica de Valencia

     Valencia, España

     
  3. [3] Universidad de la República

     Universidad de la República

     Uruguay

     

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• Localización: Inteligencia artificial: Revista Iberoamericana de Inteligencia Artificial, ISSN-e 1988-3064, ISSN 1137-3601, Vol. 23, Nº. 66, 2020, págs. 9-25
• Idioma: inglés
• Enlaces
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• Resumen

  • We propose a methodology for the improvement of the parameter design that consists of the combination of Random Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization.

    The rst phase corresponds to the previous preparation of the data set by using normalization functions. In the second phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called it Multivariate Random Forest (MRF) for the determination of the objective function. Finally, in the third phase, we obtained the optimal combination of parameter levels with the integration of properties of our modelling scheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us to compare and validate the virtues of our methodology versus other proposals involving Articial Neural Networks (ANN) and Simulated Annealing (SA).

• Referencias bibliográficas
  • H. Allende, D. Bravo, and E. Canessa. Robust design in multi multivariate systems using genetic algorithms. Qual Quant , 44:315–332, 2010.
  • J Antony. Simultaneous optimization of multiple quality characteristics in manufacturing processes using taguchi’s quality loss function....
  • L. Breiman. Random forest. Machine Learning , 45(1):5–32, 2001.
  • Hsu C-M, Su C*T, and D. Liao. Simultaneous optimisation of the broadband tap coupler opti- cal performance based on neural...
  • E. Canessa, G. Bielenberg, and E. Allende. Robust design in multi objetive systems using taguchi’s prameter design approach and pareto genetic...
  • Su Chao-Ton and Chang Hsu-Hwa. Optimization of parameter design: An intelligent approach using neural network and simulated annealing. International...
  • Su Chao-Ton, Chen Mu-Chen, and Chan Hsiao-Ling. Applying neural network and scatter search to optimize parameter design with dynamic...
  • Glenn De’ath. Multivariate regression tree: a new technique for modelingspecies-environment rela- tionships. Ecology, 83(4):1105–1117,...
  • E Del Castillo, D Montgomery, and D McCarville. Modified desirability functions for multiple response optimization. Journal...
  • A. Dine, D. Larocque, and F Bellavance. Multivariate trees for mixed outcomes. Computational statistics and data analysis, 53(11):3795–3804,...
  • E. Harrington. The desirability function. Industrial Quality Control , 21(10):494–498, 1965.
  • Shang Hsu, Feng-Liang Lee, and Muh-Cherng Wu. An integrated approach to achieving optimal design of computer games. Expert Syst. Appl....
  • Shang Hsu, Feng-Liang Lee, and Muh-Cherng Wu. An integrated approach to achieving optimal design of computer games. Expert Syst. Appl....
  • Chang Hsu-Hwa. Dynamic multi-response experiments by backpropagation networks and desirability functions. Journal of the Chinese Institute...
  • Chang Hsu-Hwa. A data mining approach to dynamic multiple responses in taguchi experimental design. Expert System with Applications, 35(3):1095–1103,...
  • Chang Hsu-Hwa and Chen Yan-Kwang. Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments. Applied Soft...
  • Chang Hsu-Hwa and Chen Yan-Kwang. Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments. Applied Soft...
  • K. Kim, K. Dennis, and Lin J. Simultaneous optimization of mechanical properties of steel by maximizing exponential desirability...
  • Hsieh Kun-Lin, Tong Lee-Ing, Chiu Hung-Pin, and Hsin-Ya Yeh. Optimization of a multi-response problem in taguchi’s dynamic system. Computers...
  • Huang M-L and Hung Y. H. Combining radial basis function neural network and genetic algorithm to improve hdd driver ic chip scale package...
  • S Maghsoodloo, G Ozdemir, V Jordan, and Ch Huang. Strengths and limitations of taguchi’s contributions to quality, manufacturing...
  • Koksoy Onur. A nonlinear programing solution to robust multi-response quality problem. Applied Mathematics and Computation, 196(2):603–612,...
  • Surajit Pal and Susanta Kumar Gauri. Multi-response optimization using multiple regressionˆabased weighted signal-to-noise ratio (mrwsn). Quality...
  • Yang-Gil Park and Bong-Jin Yum. Development of performance measures of dynamic parameter design problems. IJMTM, 5:91–104, 2003.
  • Roberta Siciliano and Francesco Mola. Multivariate data analysis and modeling through classification and regression trees. Computational Statistics...
  • Chao-Ton Su and Lee-Ing Tong. Multi-response robust design by principal component analysis. 1997.
  • Pal Sujarit and Kumar G. Susanta. A desirability functions-based approach for misultaneos op-timization of quantitative and...
  • Joseph V. Roshan and Wu C-F Jeff. Robust parameter design of multiple-target systems. Techno-metric, 44(4):338–346, 2002.
  • Adriana Villa-Murillo, Andrés Carrión, and Antonio Sozzi. Optimización del diseño de parámetros: Métodos Forest-Genetic univariante. Publicaciones...
  • Adriana Villa-Murillo, Susana. San Matías, and Andrés Carrión. Optimización del diseño de parámetros: Métodos Forest-Genetic....
  • Ful-Chiang Wu and Chi-Hao Yeh. Robust design of multiple dynamic quality characteristics. The International Journal of Advanced Manufacturing...
  • C. Zang, M. Friswell, and JE. Monttershead. A review of robust ptimal design and its applications in dynamic. Computer and Structures , 83(4):315–326,...