%0 Journal Article %1 yeun_2005_SMO %A Yeun, Y. S. %A Yang, Y. S. %A Ruy, W. S. %A Kim, B. J. %D 2005 %J Structural and Multidisciplinary Optimization %K algorithms, data-set derivative-based directional extended genetic high-order interpolation, method, overfitting,response polynomial, programming, smoothing, surface %N 1 %P 19--34 %R doi:10.1007/s00158-004-0460-6 %T Polynomial genetic programming for response surface modeling Part 1: a methodology %U http://www.springerlink.com/app/home/contribution.asp?wasp=pggkrvwxwq5hf4uqva6q&referrer=parent&backto=issue,2,6;journal,2,60;linkingpublicationresults,1:102504,1; %V 29 %X The second-order polynomial is commonly used for fitting a response surface but the low-order polynomial is not sufficient if the response surface is highly nonlinear. Based on genetic programming (GP), this paper presents a method with which high-order smooth polynomials, which can model nonlinear response surfaces, can be built. Since in many cases small samples are used to fit the response surface, it is inevitable that the high-order polynomial shows serious overfitting behaviors. Moreover, the high-order polynomial shows infamous wiggling, unwanted oscillations, and large peaks. To suppress such problematic behaviors, this paper introduces a novel method, called directional derivative-based smoothing (DDBS) that is very effective for smoothing a high-order polynomial. The role of GP is to find appropriate terms of a polynomial through the application of genetic operators to GP trees that represent polynomials. The GP tree is transformed into the standard form of a polynomial using the translation algorithm. To estimate the coefficients of the polynomial quickly the ordinary least-square (OLS) method that incorporates the DDBS and extended data-set method is devised. Also, by using the classical Lagrange multiplier method, the modified OLS method enabling interpolation is presented. Four illustrative numerical examples are given to demonstrate the performance of GP with DDBS.

@article{yeun_2005_SMO, abstract = {The second-order polynomial is commonly used for fitting a response surface but the low-order polynomial is not sufficient if the response surface is highly nonlinear. Based on genetic programming (GP), this paper presents a method with which high-order smooth polynomials, which can model nonlinear response surfaces, can be built. Since in many cases small samples are used to fit the response surface, it is inevitable that the high-order polynomial shows serious overfitting behaviors. Moreover, the high-order polynomial shows infamous wiggling, unwanted oscillations, and large peaks. To suppress such problematic behaviors, this paper introduces a novel method, called directional derivative-based smoothing (DDBS) that is very effective for smoothing a high-order polynomial. The role of GP is to find appropriate terms of a polynomial through the application of genetic operators to GP trees that represent polynomials. The GP tree is transformed into the standard form of a polynomial using the translation algorithm. To estimate the coefficients of the polynomial quickly the ordinary least-square (OLS) method that incorporates the DDBS and extended data-set method is devised. Also, by using the classical Lagrange multiplier method, the modified OLS method enabling interpolation is presented. Four illustrative numerical examples are given to demonstrate the performance of GP with DDBS.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Yeun, Y. S. and Yang, Y. S. and Ruy, W. S. and Kim, B. J.}, biburl = {https://www.bibsonomy.org/bibtex/2b62ae60e3ae12c798301e503fa8579c0/brazovayeye}, doi = {doi:10.1007/s00158-004-0460-6}, interhash = {0697a04ad6b796ab287a518e9224afee}, intrahash = {b62ae60e3ae12c798301e503fa8579c0}, issn = {1615-147X}, journal = {Structural and Multidisciplinary Optimization}, keywords = {algorithms, data-set derivative-based directional extended genetic high-order interpolation, method, overfitting,response polynomial, programming, smoothing, surface}, month = {January}, number = 1, pages = {19--34}, timestamp = {2008-06-19T17:54:54.000+0200}, title = {Polynomial genetic programming for response surface modeling Part 1: a methodology}, url = {http://www.springerlink.com/app/home/contribution.asp?wasp=pggkrvwxwq5hf4uqva6q&referrer=parent&backto=issue,2,6;journal,2,60;linkingpublicationresults,1:102504,1;}, volume = 29, year = 2005 }