Design of Targeting Cost Function for Evolutionary Optimization of Chaos Control

This contribution deals with optimization of the control of chaos by means of evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control and to show several methods of constructing the complex targeting cost function leading to satisfactory results. As a model of deterministic chaotic system the two dimensional Henon map was used. The optimizations were realized in several ways, each one for another cost function or another desired periodic orbit. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, simulations were repeated several times to show and check robustness of used method and cost function. At the end of this work the results of optimized chaos control for each designed targeting cost function are compared.