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

Author

Listed:
  • K. Antony Arokia Durai Raj
  • B. Kanagasabapathi
  • Gopichand Agnihothram

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.

Suggested Citation

  • K. Antony Arokia Durai Raj & B. Kanagasabapathi & Gopichand Agnihothram, 2011. "Meta-heuristic to estimate parameters in Non-Linear Regression Models," International Journal of Mathematics in Operational Research, Inderscience Enterprises Ltd, vol. 3(5), pages 473-489.
    • Handle: RePEc:ids:ijmore:v:3:y:2011:i:5:p:473-489
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