Forecasting nonlinear time series with genetic algorithms genetic algorithms and symbolic form

A genetic algorithm (GA) programmed to approximate the functional relation, in symbolic form, that describes the behavior of a time series. The GA formalism proposed here utilizes the “postfix” representation with a view to reduce the procedural complexities and the “elitist mating” scheme to produce fitter offspring strings. An initial population of potential solutions is subjected to an evolutionary process described by selection, reproduction and mutation processes which are repeated over generations until an optimum individual is finally found. The GA was proved useful in obtaining functional forms describing accurately the evolution of the nonlinear time series.