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Prediction with Recurrent Neural Networks in Evolutionary Dynamic Optimization

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

Evolutionary algorithms (EAs) are a good choice to solve dynamic optimization problems. Objective functions changing over time are challenging because after a change the EA has to adapt its population to find the new optimum. Prediction techniques that estimate the position of the next optimum can be incorporated into the EA. After a change, the predicted optimum can be employed to move the EA’s population to a promising region of the solution space in order to accelerate convergence and improve accuracy in tracking the optimum. In this paper we introduce a recurrent neural network-based prediction approach. In an experimental study on the Moving Peaks Benchmark and dynamic variants of the Sphere, Rosenbrock, and Rastrigin functions we compare it to an autoregressive prediction approach and an EA without prediction. The results show the competitiveness of our approach and its suitability especially for repeated optima.

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Notes

  1. 1.

    https://keras.io/.

  2. 2.

    http://www.statsmodels.org/0.6.1/vector_ar.html.

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Acknowledgments

This research is funded by the German Research Foundation through the Research Training Group SCARE – System Correctness under Adverse Conditions (DFG-GRK 1765), www.scare.uni-oldenburg.de.

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Authors and Affiliations

  1. Computational Intelligence Group, Department of Computing Science, University of Oldenburg, Oldenburg, Germany

    Almuth Meier & Oliver Kramer

Authors
  1. Almuth Meier
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  2. Oliver Kramer
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Correspondence to Almuth Meier .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Meier, A., Kramer, O. (2018). Prediction with Recurrent Neural Networks in Evolutionary Dynamic Optimization. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_56

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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