Abstract
In this paper, we study an evolutionary algorithm employed to design and optimize a local control of chaos. In particular, we use a multi—objective fitness function, which consists of the objective function to be optimized and an auxiliary quantity applied as an additional driving force for the algorithm. Numerical results are presented illustrating the proposed scheme and showing the influence of employing such a multi—objective fitness function on convergence of the algorithm.
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Richter, H. (2002). An Evolutionary Algorithm for Controlling Chaos: The Use of Multi—objective Fitness Functions. In: Guervós, J.J.M., Adamidis, P., Beyer, HG., Schwefel, HP., Fernández-Villacañas, JL. (eds) Parallel Problem Solving from Nature — PPSN VII. PPSN 2002. Lecture Notes in Computer Science, vol 2439. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45712-7_30
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DOI: https://doi.org/10.1007/3-540-45712-7_30
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