A genetic algorithm approach to nonlinear least squares estimation |
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| Authors: | Alan D Olinsky John T Quinn Paul M Mangiameli Shaw K Chen |
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| Institution: | 1. Department of Mathematics , Bryant College , Smithfield, RI 02917, USA E-mail: aolinsky@bryant.edu;2. Department of Management Science , University of Rhode Island , Kingston, RI 02881, USA |
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| 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 be effective for nonlinear least squares estimation. This paper provides an illustration of a genetic algorithm applied to a simple nonlinear least squares example. |
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