Meta-heuristic to estimate parameters in Non-Linear Regression Models

K. Antony Arokia Durai Raj, B. Kanagasabapathi and Gopichand Agnihothram

International Journal of Mathematics in Operational Research, 2011, vol. 3, issue 5, 473-489

Abstract: Non-Linear Regression Models (NLRM) are used in analysing scientific applications such as metal treatment, chemical process, pharmacology, and physiology. If the parameters in a regression model are non-linear, then the model is termed as NLRM, even if the explanatory variables of such a model are linear. The computational effort required to solve linear regression models are less compared to NLRMs. In this paper we propose a Genetic Algorithm (GA) to estimate the parameters in NLRMs. The computational results show that the proposed GA performs better than/equivalent to the existing methods in most of the problem instances considered in this study.

Keywords: NLRM parameters; nonlinear regression models; parameter estimation; heuristics; GAs; genetic algorithms; modelling; metaheuristics. (search for similar items in EconPapers)
Date: 2011
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