Skip to main content

An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models

  • Chapter
Evolutionary Computation in Dynamic and Uncertain Environments

Part of the book series: Studies in Computational Intelligence ((SCI,volume 51))

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Barrico C, Antunes CH (2006) “Robustness Analysis in Multi-Objective Opti- mization”. Research Report N3/2006, INESC Coimbra, Portugal.

    Google Scholar 

  2. Barrico C, Antunes CH (2006) “A New Approach to Robustness Analysis in Multi-Objective Optimization”. Proceedings of the 7th International Confer- ence on Multi-Objective Programming and Goal Programming (MOPGP 2006). Loire Valley (Tours), France.

    Google Scholar 

  3. Barrico C, Antunes CH (2006) “Robustness Analysis in Multi-Objective Op- timization Using a Degree of Robustness Concept”. Proceedings of the 2006 IEEE World Congress on Computational Intelligence (WCCI 2006): 6778-6783.

    Google Scholar 

  4. Branke J. (1998) “Creating Robust Solutions by means of an Evolutionary Algorithm”. Parallel Problem Solving from Nature, Lecture Notes in Computer Science 1498, Springer: 119-128.

    Google Scholar 

  5. Branke J. (2000) “Efficient Evolutionary Algorithms for Searching Robust Solutions”. Adaptive Computing in Design and Manufacture (ACDM 2000), Springer: 275-286.

    Google Scholar 

  6. Branke J, Schmidh C (2005) “Faster Convergence by means of Fitness Estima- tion”. Soft Computing 9(1): 13-20.

    Article  Google Scholar 

  7. Coello C, Veldhuizen D, Lamont G (2002) “Evolutionary Algorithms for Solving Multi-Objective Problems”. Kluwer Academic Publishers.

    Google Scholar 

  8. Deb K (2001) “Multi-Objective Optimization Using Evolutionary Algorithms”. John Wiley and Sons, New York.

    Google Scholar 

  9. Deb K, Gupta H (2004) “Introducing Robustness in Multiple-Objective Op- timization”. KanGAL Report Number 2004016, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology Kanpur, India.

    Google Scholar 

  10. Deb K, Gupta H (2005) “Searching for Robust Pareto-Optimal Solutions in Multi-Objective Optimization”. Proceedings of the Third International Con- ference of Evolutionary Multi-Criteria Optimization (EMO-2005): 150-164.

    Google Scholar 

  11. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) “A fast and elitist multi- objective genetic algorithm: NSGA-II”. IEEE Transactions on Evolutionary Computation 6(2): 181-197.

    Article  Google Scholar 

  12. Fonseca CM, Fleming PJ (1995) “An Overview of Evolutionary Algorithms in Multiobjective Optimization”. Evolutionary Computation 3(1): 1-16.

    Article  Google Scholar 

  13. Gomes A, Antunes CH, Martins A (2004) “A multiple objective evolutionary approach for the design and selection of load control strategies”. IEEE Trans- actions on Power Systems 19(2): 1173-1180.

    Article  Google Scholar 

  14. Hughes EJ (2001) “Evolutionary Multi-Objective Ranking with Uncertainty and Noise”. Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO-2001): 329-343.

    Google Scholar 

  15. Jin Y, Branke J (2005) “Evolutionary Optimization in Uncertain Environments - A Survey”. IEEE Transactions on Evolutionary Computation 9(3): 1-15.

    Article  Google Scholar 

  16. Jin Y, Sendhoff B (2003) “Trade-Off between Performance and Robustness: An Evolutionary Multiobjective Approach”. Proceedings of the Second Interna- tional Conference on Evolutionary Multi-Criterion Optimization (EMO-2003): 237-251.

    Google Scholar 

  17. Li M, Azarm S, Aute V (2005) “A Multi-Objective Genetic Algorithm for Robust Design Optimization”. Proceedings of Genetic and Evolutionary Com- putation Conference (GECCO’05): 771-778.

    Google Scholar 

  18. Lim D, Ong YS, Lee BS (2005) “Inverse Multi-Objective Robust Evolutionary Design Optimization in the Presence of Uncertainty”. Proceedings of Genetic and Evolutionary Computation Conference (GECCO’05): 55-62.

    Google Scholar 

  19. Ong YS, Nair PB, Lum KY (2005) “Max-Min Surrogate-Assisted Evolutionary Algorithm for Robust Aerodynamic Design”. IEEE Transactions on Evolution- ary Computation 10(4): 392-404.

    Google Scholar 

  20. Parmee IC (1996) “The Maintenance of Search Diversity for Effective Design Space Decomposition using Cluster-Oriented Genetic Algorithms (Cogas) and Multi-Agent Strategies (Gaant)”. Proceedings of the Second International Con- ference of Adaptive Computing in Engineering and Control: 128-138.

    Google Scholar 

  21. Teich J (2001) “Pareto-Front Exploration with Uncertain Objectives”. Pro- ceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO-2001): 314-328.

    Google Scholar 

  22. Tsutsui S, Ghosh A (1997) “Genetic Algorithm with a Robust Solution Search- ing Scheme”. IEEE Transactions on Evolutionary Computation 1(3): 201-219.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Barrico, C., Antunes, C.H. (2007). An Evolutionary Approach for Assessing the Degree of Robustness of Solutions to Multi-Objective Models. In: Yang, S., Ong, YS., Jin, Y. (eds) Evolutionary Computation in Dynamic and Uncertain Environments. Studies in Computational Intelligence, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49774-5_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-49774-5_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49772-1

  • Online ISBN: 978-3-540-49774-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics