Obtaining functional form for chaotic time series evolution using genetic algorithm

Abstract

A genetic algorithm (GA) based strategy is presented for deducing an exact or near-exact functional form from a time series. The GA formalism proposed here utilizes (i) the "postfix"representation with a view to reduce the procedural complexities and (ii) the "elitist mating"scheme to produce fitter offspring strings. The GA procedure is exemplified by considering chaotic time series of the well-known logistic, Henon and universal maps. The GA correctly recovers the underlying functional forms for the respective time series. Measurements from a number of finite-dimensional physical, biological, and other systems often give rise to complex time series and the presented methodology should prove useful in obtaining functional forms describing accurately the evolution of the time series.