Evolutionary Synthesis and Control of Chaotic Systems

This research deals with the synthesis and control of chaos by means of evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of synthesis of new chaotic system and optimization of its control and to show a new approach of solving this problem and constructing new cost functions operating in “blackbox mode” without previous exact mathematical analysis of the system, thus without knowledge of stabilizing of the target state. Three different cost functions are presented and tested. The optimizations were achieved in several ways, each one for another desired periodic orbit. The evolutionary algorithm, Self-Organizing Migrating Algorithm (SOMA) was used in its four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and cost function. Presented results lend weight to the argument, that proposed cost functions give satisfactory results.