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
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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).