Skip to main content

The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators

  • Conference paper
Artificial Evolution (EA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7401))

Abstract

This paper investigates the relationship between the covered fraction, completeness, and (weighted) hypervolume indicators for assessing the quality of the Pareto-front approximations produced by multiobjective optimizers. It is shown that these unary quality indicators are all, by definition, weighted Hausdorff measures of the intersection of the region attained by such an optimizer outcome in objective space with some reference set. Moreover, when the optimizer is stochastic, the indicators considered lead to real-valued random variables following particular probability distributions. Expressions for the expected value of these distributions are derived, and shown to be directly related to the first-order attainment function.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alberti, G.: Geometric measure theory. In: Françoise, J.P., et al. (eds.) Encyclopedia of Mathematical Physics, vol. 2, pp. 520–527. Elsevier, Oxford (2006)

    Chapter  Google Scholar 

  2. Bader, J.M.: Hypervolume-Based Search for Multiobjective Optimization: Theory and Methods. Ph.D. thesis, Swiss Federal Institute of Technology, Zurich (2009)

    Google Scholar 

  3. Barenblatt, G.I.: Scaling, Self-similarity, and Intermediate Asymptotics. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  4. DiBenedetto, E.: Real Analysis. Birkhäuser, Boston (2002)

    Book  MATH  Google Scholar 

  5. Fonseca, C.M., Grunert da Fonseca, V., Paquete, L.: Exploring the Performance of Stochastic Multiobjective Optimisers with the Second-Order Attainment Function. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 250–264. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Fonseca, C.M., Guerreiro, A.P., López-Ibáñez, M., Paquete, L.: On the Computation of the Empirical Attainment Function. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 106–120. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Grunert da Fonseca, V., Fonseca, C.M.: The attainment-function approach to stochastic multiobjective optimizer assessment and comparison. In: Bartz-Beielstein, T., et al. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, ch. 5, pp. 103–130. Springer, Berlin (2010)

    Chapter  Google Scholar 

  8. Grunert da Fonseca, V., Fonseca, C.M., Hall, A.O.: Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D. (eds.) EMO 2001. LNCS, vol. 1993, pp. 213–225. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Ito, K. (ed.): Encyclopedic Dictionary of Mathematics 2. The Mathematical Society of Japan. The MIT Press (1987)

    Google Scholar 

  10. Lotov, A.V., Bushenkov, V.A., Kamenev, G.K.: Interactive Decision Maps: Approximation and Visualization of Pareto Frontier. Kluwer Academic Publishers, Dordrecht (2004)

    MATH  Google Scholar 

  11. Molchanov, I.: Theory of Random Sets. Springer, London (2005)

    MATH  Google Scholar 

  12. Ott, E.: Chaos in Dynamical Systems. Cambridge University Press, Cambridge (2002)

    Book  MATH  Google Scholar 

  13. Ulungu, E.L., Teghem, J., Fortemps, P.H., Tuyttens, D.: MOSA method: A tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis 8(4), 221–236 (1999)

    Article  MATH  Google Scholar 

  14. Zitzler, E., Brockhoff, D., Thiele, L.: The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  15. Zitzler, E., Knowles, J., Thiele, L.: Quality Assessment of Pareto Set Approximations. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 373–404. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  17. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Grunert da Fonseca, V., Fonseca, C.M. (2012). The Relationship between the Covered Fraction, Completeness and Hypervolume Indicators. In: Hao, JK., Legrand, P., Collet, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2011. Lecture Notes in Computer Science, vol 7401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35533-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35533-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35532-5

  • Online ISBN: 978-3-642-35533-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics