Publication: Tracking Moving Optima Using Kalman-Based Predictions
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2008-03
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MIT Press
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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.
Description
30 pages, 23 figures.
Keywords
Evolutionary algorithms, Vision-based tracking, Dynamic optimization problem, Time-varying fitness function
Bibliographic citation
Evolutionary Computation, 2008, v. 16, n. 1, p. 1-30