Cost function design for evolutionary optimization of deterministic 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 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 and behavior of system. 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 cost function are compared.