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Polynomial response surface model has been widely used as approximation model which replace physical or numerical experiments in various engineering fields. Generally, low-order model is used to reduce experimental points required to construct the response surfaces, but this approach has limit to represent the highly non-linear phenomena. In this paper, we developed the method to expand modeling capabilities of polynomial response surfaces by increasing order of polynomial and selecting optimum polynomial basis functions. Genetic algorithm is used to choose optimal polynomial basis functions. Developed method was applied to analytic functions with 1 or 2 variables and wind tunnel test data modeling. The results show that this method is applicable to building response surface models for highly non-linear phenomena.

Optimal Basis Function Selection for Polynomial Response Surface Model Using Genetic Algorithm