Failure-Free Genetic Algorithm Optimization of a System Controller Using SAFE/LEARNING Controllers in Tandem

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dc.creatorSazonov, E. S.
dc.creatorDel Gobbo, D.
dc.creatorKlinkhachorn, P.
dc.creatorKlein, R. L.
dc.date2003-12-03
dc.date.accessioned2026-07-07T03:20:41Z
dc.date.available2026-07-07T03:20:41Z
dc.descriptionThe paper presents a method for failure free genetic algorithm optimization of a system controller. Genetic algorithms present a powerful tool that facilitates producing near-optimal system controllers. Applied to such methods of computational intelligence as neural networks or fuzzy logic, these methods are capable of combining the non-linear mapping capabilities of the latter with learning the system behavior directly, that is, without a prior model. At the same time, genetic algorithms routinely produce solutions that lead to the failure of the controlled system. Such solutions are generally unacceptable for applications where safe operation must be guaranteed. We present here a method of design, which allows failure-free application of genetic algorithms through utilization of SAFE and LEARNING controllers in tandem, where the SAFE controller recovers the system from dangerous states while the LEARNING controller learns its behavior. The method has been validated by applying it to an inherently unstable system of inverted pendulum.
dc.identifierhttps://arxiv.org/abs/cs/0312009
dc.identifierhttp://arxiv.org/abs/cs/0312009
dc.identifierProceedings of 34th Southeastern Symposium on System Theory (SSST), Huntsville, AL, March 2002
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31911
dc.subjectNeural and Evolutionary Computing
dc.subjectMachine Learning
dc.subjectI.2.6;C.1.3;I.5.1
dc.titleFailure-Free Genetic Algorithm Optimization of a System Controller Using SAFE/LEARNING Controllers in Tandem
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