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Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?

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Book cover Applications of Evolutionary Computation (EvoApplications 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9597))

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Abstract

Finding the best possible sequence of control actions for a hybrid car in order to minimize fuel consumption is a well-studied problem. A standard method is Dynamic Programming (DP) that is generally considered to provide solutions close to the global optimum in relatively short time. To our knowledge Evolutionary Algorithms (EAs) have so far not been used for this setting, due to the success of DP. In this work we compare DP and EA for a well-studied example and find that for the basic scenario EA is indeed clearly outperformed by DP in terms of calculation time and quality of solutions. But, we also find that when going beyond the standard scenario towards more realistic (and complex) scenarios, EAs can actually deliver a performance en par or in some cases even exceeding DP, making them useful in a number of relevant application scenarios.

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Notes

  1. 1.

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Acknowledgments

The authors want to the thank the reviewers for valuable feedback. Ken Nishikawa acknowledges the financial support from Honda Research Institute Europe.

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Authors and Affiliations

  1. Honda Research Institute Europe, Carl-Legien-Strasse 30, 63073, Offenbach/Main, Germany

    Tobias Rodemann

  2. Graduate School of Information Science and Engineering, Tokyo Institute of Technology, O-okayama 2-12-1, Meguro, Tokyo, 152-8552, Japan

    Ken Nishikawa

Authors
  1. Tobias Rodemann
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  2. Ken Nishikawa
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Corresponding author

Correspondence to Tobias Rodemann .

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Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Aalborg University, Copenhagen, Denmark

    Paolo Burelli

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Rodemann, T., Nishikawa, K. (2016). Can Evolutionary Algorithms Beat Dynamic Programming for Hybrid Car Control?. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_50

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  • DOI: https://doi.org/10.1007/978-3-319-31204-0_50

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31203-3

  • Online ISBN: 978-3-319-31204-0

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