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Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems

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Applications of Evolutionary Computation (EvoApplications 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9028))

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Abstract

A commonly used approach in Evolutionary Algorithms for Dynamic Constrained Optimization Problems forces re-evaluation of a population of individuals whenever the landscape changes. On the contrary, there are algorithms like IDEA-ARIMA that can effectively anticipate certain types of landscapes rather than react to changes which already happened and thus be one step ahead with the dynamic environment. However, the computational cost of IDEA-ARIMA and its memory consumption are barely acceptable in practical applications. This paper proposes a set of modifications aimed at making this algorithm an efficient and competitive tool by reducing the use of memory and proposing the new anticipation mechanism.

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Author information

Authors and Affiliations

  1. Computational Intelligence Research Group, Institute of Computer Science, University of Wroclaw, Wroclaw, Poland

    Patryk Filipiak & Piotr Lipinski

Authors
  1. Patryk Filipiak
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  2. Piotr Lipinski
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Correspondence to Patryk Filipiak .

Editor information

Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Antonio M. Mora

  2. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

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Filipiak, P., Lipinski, P. (2015). Making IDEA-ARIMA Efficient in Dynamic Constrained Optimization Problems. In: Mora, A., Squillero, G. (eds) Applications of Evolutionary Computation. EvoApplications 2015. Lecture Notes in Computer Science(), vol 9028. Springer, Cham. https://doi.org/10.1007/978-3-319-16549-3_71

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  • DOI: https://doi.org/10.1007/978-3-319-16549-3_71

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16548-6

  • Online ISBN: 978-3-319-16549-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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