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.
Recommended CitationOlinsky, Alan; Quinn, John; Mangiameli, Paul; and Chen, Shaw, "A genetic algorithm approach to nonlinear least squares estimation." (2004). Mathematics Department Journal Articles. Paper 6.
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