Forest-Genetic method to optimize parameter design of multiresponse experiment
We propose a methodology for the improvement of the parameter design that consists of the combination ofRandom Forest (RF) with Genetic Algorithms (GA) in 3 phases: normalization, modelling and optimization.The first phase corresponds to the previous preparation of the data set by using normalization functions. In thesecond phase, we designed a modelling scheme adjusted to multiple quality characteristics and we have called itMultivariate 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 modellingscheme and desirability functions in the establishment of the corresponding GA. Two illustrative cases allow us tocompare and validate the virtues of our methodology versus other proposals involving Artificial Neural Networks(ANN) and Simulated Annealing (SA).
Artificial Intelligence, Genetic algorithm, Random forest, Artificial Neural networks, Multivariate analysis