A genetic algorithms based technique for computing the nonlinear least squares estimates of the parameters of sum of exponentials model

Estimation of the parameters of a nonlinear sum of exponentials model is an important and well studied problem in time series analysis. The sum of exponentials model finds application in modeling various physical phenomena in a wide variety of real life applications. The problem of finding the nonlinear least squares estimates in well known to be numerically difficult. In this paper, we propose an elitist generational genetic algorithm based iterative procedure for computing the nonlinear least squares estimates. Simulation studies and real life data fitting examples indicate satisfactory performance of the proposed technique.

► We consider least squares estimation of the parameters of sum of exponential model.
► A genetic algorithm based method for finding the nonlinear least squares estimates is proposed.
► The proposed method uses an elitist generational genetic algorithm.
► Simulation studies and real life data analysis show the usefulness and efficiency of the proposed technique.