Forest-Genetic method to optimize parameter design of multiresponse experiment

Abstract

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 Arti cial Neural Networks (ANN) and Simulated Annealing (SA).

Keywords

• Genetic Algorithm
• Random Forest
• Neural Networks
• Multivariate Analysis
• Artificial Intelligence