Multiple response optimization: Analysis of genetic programming for symbolic regression and assessment of desirability functions

作者：

Highlights：

• Assessment between Genetic Programming and Ordinary Least Squares to mathematical models building.

• Assessment between desirability and modified desirability agglutinating functions.

• Use of Akaike Information Criterion (AIC) to select the best configuration for multiple response optimization.

• Two Design of Experiment cases were evaluated.

摘要

•Assessment between Genetic Programming and Ordinary Least Squares to mathematical models building.•Assessment between desirability and modified desirability agglutinating functions.•Use of Akaike Information Criterion (AIC) to select the best configuration for multiple response optimization.•Two Design of Experiment cases were evaluated.

论文关键词：00-01,99-00,Optimization,Genetic programming,Desirability function,Modeling

论文评审过程：Received 6 July 2018, Revised 7 March 2019, Accepted 2 May 2019, Available online 15 May 2019, Version of Record 12 June 2019.

论文官网地址：https://doi.org/10.1016/j.knosys.2019.05.002