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Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains

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Parallel Problem Solving from Nature – PPSN X (PPSN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

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Abstract

In this work we investigate the use of prediction mechanisms in Evolutionary Algorithms for dynamic environments. These mechanisms, linear regression and Markov chains, are used to estimate the generation when a change in the environment will occur, and also to predict to which state (or states) the environment may change, respectively. Different types of environmental changes were studied. A memory-based evolutionary algorithm empowered by these two techniques was successfully applied to several instances of the dynamic bit matching problem.

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Author information

Authors and Affiliations

  1. Department of Informatics and Systems Engineering, Coimbra Polytechnic, Portugal

    Anabela Simões

  2. Centre of Informatics and Systems of the University of Coimbra, Portugal

    Anabela Simões & Ernesto Costa

Authors
  1. Anabela Simões
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  2. Ernesto Costa
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Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

  2. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen  & Nicola Beume  & 

  3. Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, Colchester, Essex, UK

    Simon Lucas

  4. Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, 34127, Trieste, Italy

    Carlo Poloni

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© 2008 Springer-Verlag Berlin Heidelberg

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Simões, A., Costa, E. (2008). Evolutionary Algorithms for Dynamic Environments: Prediction Using Linear Regression and Markov Chains. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_31

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  • DOI: https://doi.org/10.1007/978-3-540-87700-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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