Narrow Results

This paper introduces the notion of chaos synthesis by means of evolutionary algorithms and develops a new method for chaotic systems synthesis. This method is similar to genetic programming and grammatical evolution and is being applied along with three evolutionary algorithms: differential evolution, self-organizing migration and genetic algorithm. The aim of this investigation is to synthesize new and "simple" chaotic systems based on some elements contained in a prechosen existing chaotic system and a properly defined cost function. The investigation consists of eleven case studies: the aforementioned three evolutionary algorithms in eleven versions. For all algorithms, 100 simulations of chaos synthesis were repeated and then averaged to guarantee the reliability and robustness of the proposed method. The most significant results were carefully selected, visualized and commented in this report.

This paper compares the performance of Differential Evolution (DE) with Self-Organizing Migrating Algorithm (SOMA) in the task of optimization of the control of chaos. The main aim of this paper is to show that evolutionary algorithms like DE are capable of optimizing chaos control, leading to satisfactory results, and to show that extreme sensitivity of the chaotic environment influences the quality of results on the selected EA, construction of cost function (CF) and any small change in the CF design. As a model of deterministic chaotic system, the two-dimensional Henon map is used and two complex targeting cost functions are tested. The evolutionary algorithms, DE and SOMA were applied with different strategies. For each strategy, repeated simulations demonstrate the robustness of the used method and constructed CF. Finally, the obtained results are compared with previous research.