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Evolutionary Computation

Spring 2008, Vol. 16, No. 1, Pages 1-30
(doi: 10.1162/evco.2008.16.1.1)
© 2008 by the Massachusetts Institute of Technology
Tracking Moving Optima Using Kalman-Based Predictions
Article PDF (505.46 KB)
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.