An introduction to differential evolution
New ideas in optimization
An Evolutionary Algorithm for Controlling Chaos: The Use of Multi-objective Fitness Functions
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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This work deals with the comparison of performance of two selected evolutionary algorithms (EA) in the task of optimization of the control of chaos. The main aim of this work is to show that evolutionary algorithms are capable of optimization of chaos control, leading to satisfactory results and to show extreme sensitivity of quality of results on the selection of EA, setting-up of 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 was used. Two complex targeting cost functions were tested in this work. The optimization was realized in several ways, each one for another evolutionary algorithm or another desired periodic orbit and behavior of system. The evolutionary algorithms, SOMA (Self-Organizing Migrating Algorithm) and DE (Differential Evolution) were used in several versions. For each version, repeated simulations demonstrated the robustness of the used method and constructed CF. Finally, the obtained results are compared.