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Global Optimization With Multivariate Adaptive Regression Splines

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2 Author(s)
Crino, S. ; US Mil. Acad., West Point, NY ; Brown, D.E.

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

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Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:37 ,  Issue: 2 )