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


    Access

    Download


    Export, share and cite



    Title :

    Tracking Moving Optima Using Kalman-Based Predictions



    Publication date :

    2008-03-01


    Remarks:

    doi:10.1162/evco.2008.16.1.1



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Moving Vehicle Tracking Based on Kalman Filter

    Yan, Yan ;Shi, Yan Cong ;Ma, Zeng Qiang | Trans Tech Publications | 2011


    Optima Loesung - Test Kia Optima 1,7 CRDi

    Thomas,J. / Kia Motors,KR | Automotive engineering | 2012




    Kalman filter-based Detection and Tracking Method for Substation Moving Targets

    Shen, Huaqiang / Cheng, Song / Wang, Yafeng et al. | IEEE | 2022