Harnessing Symbolic Regression and Nonlinear Programming for Computationally Expensive Multi-Objective Optimization
A surrogate-assisted multi-objective optimization approach that leverages symbolic regression (SR) to construct interpretable analytical approximations of expensive black-box functions and uses iterative construction of surrogates and Tchebycheff scalarization along a set of parallel reference vectors to search for wel...