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Learning a Hidden Markov Model-Based Hyper-heuristic

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Learning and Intelligent Optimization (LION 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8994))

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Abstract

A simple model shows how a reasonable update scheme for the probability vector by which a hyper-heuristic chooses the next heuristic leads to neglecting useful mutation heuristics. Empirical evidence supports this on the MaxSat, TravelingSalesman, PermutationFlowshop and VehicleRoutingProblem problems. A new approach to hyper-heuristics is proposed that addresses this problem by modeling and learning hyper-heuristics by means of a hidden Markov Model. Experiments show that this is a feasible and promising approach.

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Notes

  1. 1.

    See goo.gl/vVTZNE for details.

  2. 2.

    If the heuristics are applied with uniform probability, around \(5\,\%\) to \(20\,\%\) of the time, although it strongly depends on the problem. See goo.gl/vVTZNE for empirical evidence.

  3. 3.

    See the right axis for the appropriate unit.

  4. 4.

    Experiments show that such local optimum is nearly always near the global optimum, although sequences can be derived that are hard to learn.

  5. 5.

    This in contrast with the Markov assumption that states that a Markov process has no memory.

  6. 6.

    See Sect. 5.3.

  7. 7.

    The decision component will however have an impact on the generated evidence.

  8. 8.

    Since the Baum-Welch algorithm is a heuristic learning algorithm, this is not guaranteed, but we never encountered such an example.

  9. 9.

    For instance, how a hyper-heuristic can model a local search heuristic.

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Acknowledgements

This research is funded by the Institute for Innovation through Science and Technology (IWT) under grant \(131'751\).

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Authors and Affiliations

  1. Department of Computer Science, KU Leuven, Celestijnenlaan 200A, 3001, Heverlee, Belgium

    Willem Van Onsem, Bart Demoen & Patrick De Causmaecker

Authors
  1. Willem Van Onsem
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  2. Bart Demoen
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  3. Patrick De Causmaecker
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Corresponding author

Correspondence to Willem Van Onsem .

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Editors and Affiliations

  1. Lille University, Villeneuve d'Ascq, France

    Clarisse Dhaenens

  2. Lille University, Villeneuve d'Ascq, France

    Laetitia Jourdan

  3. Lille University, Villeneuve d'Ascq, France

    Marie-Eléonore Marmion

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Van Onsem, W., Demoen, B., De Causmaecker, P. (2015). Learning a Hidden Markov Model-Based Hyper-heuristic. In: Dhaenens, C., Jourdan, L., Marmion, ME. (eds) Learning and Intelligent Optimization. LION 2015. Lecture Notes in Computer Science(), vol 8994. Springer, Cham. https://doi.org/10.1007/978-3-319-19084-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-19084-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19083-9

  • Online ISBN: 978-3-319-19084-6

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