30 pages, 23 figures. ; The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a time-changing fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison. ; The work of the first and second authors has been carried out under a "Ramón y Cajal" research fellowship from the Ministerio de Ciencia y Tecnología of Spain, and partially funded by projects DPI2005-04302 and DPI2006-03444. ; Publicado
Tracking Moving Optima Using Kalman-Based Predictions
2008-03-01
doi:10.1162/evco.2008.16.1.1
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Moving Vehicle Tracking Based on Kalman Filter
Trans Tech Publications | 2011
|Optima Loesung - Test Kia Optima 1,7 CRDi
Kraftfahrwesen | 2012
|Using the Unscented Kalman Filter for Pedestrian Tracking from a Moving Host
British Library Conference Proceedings | 2008
|Kraftfahrwesen | 2012
|