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Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments

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Foundations of Computational Intelligence Volume 4

Part of the book series: Studies in Computational Intelligence ((SCI,volume 204))

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Abstract

Evolutionary Computation techniques have proven their applicability for time series forecasting in a number of studies. However these studies, like those applying other techniques, have assumed a static environment, making them unsuitable for many real-world forecasting concerns which are characterized by uncertain environments and constantly-shifting conditions. This chapter summarizes the results of recent studies that investigate adaptive evolutionary techniques for time series forecasting in non-static environments and proposes a new, self-adaptive technique that addresses shortcomings seen from these studies. A theoretical analysis of the proposed technique’s efficacy in the presence of shifting conditions and noise is given.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Augusta State University, Augusta, GA 30904, USA

    Neal Wagner

  2. School of Computer Science, University of Adelaide, Adelaide, SA 5005, Australia

    Zbigniew Michalewicz

  3. Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237, Warsaw, Poland

    Zbigniew Michalewicz

  4. Polish-Japanese Institute of Information Technology, ul. Koszykowa 86, 02-008, Warsaw, Poland

    Zbigniew Michalewicz

Authors
  1. Neal Wagner
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  2. Zbigniew Michalewicz
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Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

  2. College of Business Administration, Quantitative and Information System Department, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

  3. Department of Computer Science, University of São Paulo,SCE - ICMSC - USP, Caixa Postal 668, 13560-970, Sao Carlos, SP, Brazil

    André Ponce de Leon F. de Carvalho

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Wagner, N., Michalewicz, Z. (2009). Adaptive and Self-adaptive Techniques for Evolutionary Forecasting Applications Set in Dynamic and Uncertain Environments. In: Abraham, A., Hassanien, AE., de Carvalho, A.P.d.L.F. (eds) Foundations of Computational Intelligence Volume 4. Studies in Computational Intelligence, vol 204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01088-0_1

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  • DOI: https://doi.org/10.1007/978-3-642-01088-0_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01087-3

  • Online ISBN: 978-3-642-01088-0

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