A genetic algorithm approach to nonlinear least squares estimation
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.
Document Type: Research Article
Affiliations: 1: Department of Mathematics Bryant College Smithfield RI 02917 USA, Email: [email protected] 2: Department of Management Science University of Rhode Island Kingston RI 02881 USA
Publication date: 01 March 2004
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