Optimal control of evolutionary synthesized chaotic system

Abstract

This work deals with the synthesis and subsequent control of discrete chaotic system by means of evolutionary algorithms. The main aim of this work is to show that evolutionary algorithms are capable of synthesis of new discrete 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. Three different cost functions are presented and tested here. 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 result and described method can be considered as a, simple tool as to how to check the controllability of new synthesized chaotic system without any numerical analysis.