Multiple Response Optimization: Comparative Analysis Between Models Obtained by Ordinary Least Method and Genetic Programming

Abstract

Purpose: This work aims to analyze and compare the performance between the Ordinary Least Squares (OLS) method executed in Minitab (v. 17) and the genetic programming performed in Eureqa Formulize (v. 1.24.0). Theoretical reference: Obtaining a model that mathematically describes the relationship between the independent variable and the response variable is essential to optimizing the process. The model can be described as an approximate representation of the real system or process, while the modeling process is a balance between simplicity and accuracy (X. Chen et al., 2018; Gomes et al., 2019; Sampaio et al., 2022; A. R. S. Silva et al., 2021). Method: An Evaluation of the best method for constructing mathematical models was performed using the Adjusted Coefficient of Determination (Radj2) and Akaike's Information Criterion Results and conclusion: The comparison between the use of the methods showed the superiority of genetic programming over OLS in the construction of mathematical models. Originality/Value: Genetic Programming produces mathematical models that are sometimes differentiated when several replicates are performed, but always with similar explanatory power and with biased characteristic that does not affect in any way the quality of prediction of the dependent variable being studied.