Digital Control of Dynamic Systems
Digital Control of Dynamic Systems
Structure optimization of neural networks for evolutionary design optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Three dimensional evolutionary aerodynamic design optimization with CMA-ES
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The complete-basis-functions parameterization in ES and its application to laser pulse shaping
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computing Nash equilibria through computational intelligence methods
Journal of Computational and Applied Mathematics - Special issue: Selected papers of the international conference on computational methods in sciences and engineering (ICCMSE-2003)
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In this paper, the advantages of introducing an additional amount of tests when evolving parameters for specific purposes is discussed. A set of optimal PID-controller parameters are sought for an exemplary system, which simulates a human-like robotic arm. When evolving the controller parameters, the number of different movements included in the optimization process is varied. By including extra movements to the optimization process, the time it takes to evolve the parameters does increase, but the uncertainty due to noise is correspondingly lowered. Additionally, it is shown that the added movements, which improve robustness of the system, do not significantly lower the overall performance of the resulting system, when utilizing the evolved parameters