A genetic algorithm approach to nonlinear least squares estimation
Date of Original Version
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, e.g., Journal
International Journal of Mathematical Education in Science and Technology
Olinsky, Alan D., John T. Quinn, Paul M. Mangiameli, and Shaw K. Chen. "A genetic algorithm approach to nonlinear least squares estimation." International Journal of Mathematical Education in Science and Technology 35, 2 (2004): 207-217. doi: 10.1080/00207390310001638331.