Global optimization with multivariate adaptive regression splines.

This paper presents a novel procedure for approximating the global optimum in structural design by combining multivariate adaptive regression splines (MARS) with a response surface methodology (RSM). MARS is a flexible regression technique that uses a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accurate models. Combining MARS and RSM improves the conventional RSM by addressing highly nonlinear high-dimensional problems that can be simplified into lower dimensions, yet maintains a low computational cost and better interpretability when compared to neural networks and generalized additive models. MARS/RSM is also compared to simulated annealing and genetic algorithms in terms of computational efficiency and accuracy. The MARS/RSM procedure is applied to a set of low-dimensional test functions to demonstrate its convergence and limiting properties.