USING THE NSGA2 ALGORITHM TO HYBRIDIZE FUZZY REGRESSION WITH MULTI-OBJECTIVE PROGRAMMING APPROACHES WITH APPLICATION - Fatima Othman Eatiah Al-Abadi; Dr. Sahera Hussein Zain Al-Thalabi (Scholars Digest Publishing, 2024)
Fuzzy regression analysis was used to model the relationship between the response variable and explanatory variables in an ambiguous environment. A multi-objective optimization approach was adopted using NSGA-II algorithms with the aim of minimizing two objectives: the prediction error is the mean squared error between the predicted and actual concentrations, which indicates the accuracy of the model and the other objective is fuzziness. In the model that represents the uncertainty in the model predictions. The lower these values are, the better the model’s performance in terms of accuracy and reliability.
Keywords: Fuzzy regression, multi-objective programming, Tanaka model, fuzzy least squares method, fuzzy moments method, fuzzy Bayesian method, NSGA2 hybridization algorithm.