title: Tracking moving optima using Kalman-based predictions.
abstract: 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.
authors.affiliation: Departamento de Automatica, Ingeniería Electronica e Informatica Industrial, Universidad Politécnica de Madrid, Madrid, 28006, Spain. Claudio.Rossi@upm.es
authors.forename: Claudio
authors.initials: C
authors.lastname: Rossi
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abderrahim
authors.forename: Julio César
authors.initials: JC
authors.lastname: Díaz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D009038:Motion; D009154:Mutation; D012371:Robotics; D014785:Vision, Ocular