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Moving Target Prediction Using Evolutionary Algorithms

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Advances in Artificial Intelligence (Canadian AI 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3501))

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

  1. Bethke, A.D.: Genetic algorithms as function optimizers, Ph.D. Thesis, Dept. Computer and Communication Sciences, Univ. of Michigan (1981)

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  2. Brindle, A.: Genetic algorithms for function optimization, Ph.D. Thesis, Computer Science Dept., Univ. of Alberta (1981)

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  3. 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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© 2005 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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