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Optimal Polynomial Regression Models by using a Genetic AlgorithmPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2011 (English)In: Proceedings of the Second International Conference on Soft ComputingTechnology in Civil, Structural and Environmental Engineering Conference, (Crete,Greece), 2011009, 2011Conference paper (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

2011.
##### Keyword [en]

Polynomial regression model, Metamodeling, Design of experiments (DoE)
##### National Category

Mechanical Engineering
##### Identifiers

URN: urn:nbn:se:liu:diva-72350OAI: oai:DiVA.org:liu-72350DiVA: diva2:459271
##### Conference

The Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering Conference, 6-9 September, Chania, Crete, Greece
#####

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##### Projects

MERA
Available from: 2011-11-25 Created: 2011-11-25 Last updated: 2011-11-25Bibliographically approved
##### In thesis

Different regression models are commonly used to approximate the behavior of an unknown response in a given design domain. The regression models are usually obtained from a design of experiments, the corresponding responses and the constitution of the regression model. In this work a new approach is proposed, where the constituents of a polynomial regression model are of arbitrary order. A genetic algorithm is used to find the optimal terms to be included in the so-called optimal polynomial regression model. The objective for the genetic algorithm is to minimize the sum of squared errors of the predicted responses. In practice the genetic algorithm generates an optimal set of exponents of the design variables for the specified number of terms in the regression model, where each term is a product of a regression coefficient and the design variables. Several example problems are presented to show the performance and accuracy of the optimal polynomial regression model. Results show an improved performance for optimal polynomial regression models compared to traditional regression models.

1. Robustness Analysis of Residual Stresses in Castings$(function(){PrimeFaces.cw("OverlayPanel","overlay459301",{id:"formSmash:j_idt647:0:j_idt651",widgetVar:"overlay459301",target:"formSmash:j_idt647:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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