A Genetic Algorithm Approach to Nonlinear Least Squares Estimation

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Published by Taylor & Francis Ltd in International Journal of Mathematical Education in Science & Technology; volume 35, issue 2, March/April 2004, pages 207-217.

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A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than global optimum. Genetic algorithms have been applied successfully to function optimization and therefore would be effective for nonlinear least squares estimation. This paper provides an illustration of a genetic algorithm applied to a simple nonlinear least squares example.