Title

A genetic algorithm approach to nonlinear least squares estimation

Document Type

Article

Date of Original Version

1-1-2004

Abstract

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 bea effective for nonlinear least squares estimation. This paper provides an illustration of a genetic algorithm applied to a simple nonlinear least squares example. © 2004 Taylor & Francis Group, LLC.

Publication Title

International Journal of Mathematical Education in Science and Technology

Volume

35

Issue

2

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