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Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time

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Learning and Intelligent Optimization (LION 12 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

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Abstract

A multiobjective perspective onto common performance measures such as the PAR10 score or the expected runtime of single-objective stochastic solvers is presented by directly investigating the tradeoff between the fraction of failed runs and the average runtime. Multi-objective indicators operating in the bi-objective space allow for an overall performance comparison on a set of instances paving the way for instance-based automated algorithm selection techniques.

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References

  1. Bischl, B. et al.: ASlib: a benchmark library for algorithm selection. Artif. Intell. J. 237, 41–58 (2016). https://doi.org/10.1016/j.artint.2016.04.003

  2. Blot, A., Hoos, H., Jourdan, L., Marmion, M., Trautmann, H.: In: Joaquin, V. et al. (ed.) MO-ParamILS: A multi-objective automatic algorithm configuration framework, pp. 32–47. Springer International Publishing, Ischia (2016)

    Google Scholar 

  3. Coello Coello, C., Lamont, G.B., van Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-objective Problems. Springer, Berlin (2007)

    MATH  Google Scholar 

  4. Hansen, N., Auger, A., Finck, S., Ros, R.: Real-Parameter Black-Box Optimization Benchmarking 2009: Experimental Setup. Technical Report RR-6828, INRIA (2009). https://hal.inria.fr/inria-00362649v3/document

  5. Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H.H., Trautmann, H.: Leveraging TSP solver complementarity through machine learning. Evol. Comput. 0(0), 1–24 (2017). https://doi.org/10.1162/evco_a_00215, pMID: 28836836

  6. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge support from the European Research Center for Information Systems (ERCIS) and the DAAD PPP project No. 57314626.

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Authors and Affiliations

  1. Information Systems and Statistics, University of Münster, Münster, Germany

    Jakob Bossek & Heike Trautmann

Authors
  1. Jakob Bossek
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  2. Heike Trautmann
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Corresponding author

Correspondence to Jakob Bossek .

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Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Bossek, J., Trautmann, H. (2019). Multi-objective Performance Measurement: Alternatives to PAR10 and Expected Running Time. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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