Abstract
This paper presents an approach for target movement prediction by using Genetic Algorithms to generate the population of movement generation operators. In this approach, we use objective functions, not derivatives or other auxiliary knowledge, and apply probabilistic transition rules, not deterministic rules, for target movement prediction. Its performance has been experimentally evaluated through several experiments.
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References
Bethke, A.D.: Genetic algorithms as function optimizers, Ph.D. Thesis, Dept. Computer and Communication Sciences, Univ. of Michigan (1981)
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Baik, S.W., Bala, J., Hadjarian, A., Pachowicz, P.: Genetic Evolution Approach for Target Movement Prediction. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3037, pp. 678–681. Springer, Heidelberg (2004)
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Baik, S., Bala, J., Hadjarian, A., Pachowicz, P., Baik, R. (2005). Moving Target Prediction Using Evolutionary Algorithms. In: Kégl, B., Lapalme, G. (eds) Advances in Artificial Intelligence. Canadian AI 2005. Lecture Notes in Computer Science(), vol 3501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11424918_22
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DOI: https://doi.org/10.1007/11424918_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25864-3
Online ISBN: 978-3-540-31952-8
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