Full Elite Sets for Multi-objective Optimisation

  • Richard M. Everson
  • Jonathan E. Fieldsend
  • Sameer Singh


Multi-objective evolutionary algorithms frequently use an archive of non-dominated solutions to approximate the Pareto front. We show that the truncation of this archive to a limited number of solutions can lead to oscillating and shrinking estimates of the Pareto front. New data structures to permit efficient query and update of the full archive are proposed, and the superior quality of frontal estimates found using the full archive is illustrated on test problems.


Pareto Front Multiobjective Optimization Multiobjective Evolutionary Algorithm Strength Pareto Evolutionary Algorithm True Pareto Front 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    CM. Fonseca and P.J. Fleming. Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423, San Mateo, California, 1993. Morgan Kaufmann. URL citeseer.nj.nee.com/fonseca93genetic.html.Google Scholar
  2. [2]
    P. Hajela and C-Y. Lin. Genetic search strategies in multicriterion optimal design. Structural Optimization, 4:99–107,1992.MATHCrossRefGoogle Scholar
  3. [3]
    J.E. Fieldsend and S. Singh. Pareto Multi-Objective Non-Linear Regression Modelling to Aid CAPM Analogous Forecasting. In Proc. IEEE Intl. Conf on Computational Intelligence for Financial Engineering, 2002.Google Scholar
  4. [4]
    J. Horn, N. Nafpliotis, and D.E. Goldberg. A Niched Pareto Genetic Algorithm for Multiobjective Optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, volume 1, pages 82–87, Piscataway, New Jersey, 1994. IEEE Service Center. URL citeseer.nj.nec.com/horn94niched.html.CrossRefGoogle Scholar
  5. [5]
    J.D. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In Proc. of the First Int. Conf on Genetic Algorithms, pages 99–100, 1985.Google Scholar
  6. [6]
    N. Srinivas and K. Deb. Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation, 2(3):221–248, 1995. URL citeseer.nj.nee.com/srinivas94multiobjective.html.CrossRefGoogle Scholar
  7. [7]
    E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2): 173–195,2000. URL citeseer.?j.nec.com/zitzler99multiobjective.html <http://nec.com/zitzler99multiobjective.html>.CrossRefGoogle Scholar
  8. [8]
    M. Laumanns, E. Zitzler, and L. Thiele. A Unified Model for Multi-Objective Evolutionary Algorithms with Elitism. In Proc. of the 2000 Congress on Evol. Comp., pages 46–53. IEEE, 2000.Google Scholar
  9. [9]
    D. Van Veldhuizen and G. Lamont. Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation, 8(2):125–147, 2000. URL citeseer.nj.nee.com/vanveldhuizenOOmultiobjective.html.CrossRefGoogle Scholar
  10. [10]
    E. Zitzler. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. PhD thesis, Swiss Federal Institute of Technology Zurich (???), 1999. Diss ??? No. 13398.Google Scholar
  11. [11]
    J.E. Fieldsend, R.M. Everson, and S. Singh. Extensions to the Strength Pareto Evolutionary Algorithm. IEEE Trans. Evol. Comp., 2001. URL www. dcs. ex. ac. uk/people/reverson. (submitted).Google Scholar
  12. [12]
    M. Sun and R.E. Steuer. InterQuad: An interactive quad tree based procedure for solving the discrete multiple criteria problem. European Journal of Operational Research, 89:462–472,1996.MATHCrossRefGoogle Scholar
  13. [13]
    Joshua D. Knowles and David Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation, 8(2):149–172, 2000. URL citeseer.nj.nec.com/ http://citeseer.nj.nec.com/knowles 00approximating.html.CrossRefGoogle Scholar
  14. [14]
    D. W. Corne, J. D. Knowles, and M. J. Oates. The pareto envelope-based selection algorithm for multiobjective optimization. In Hans-Paul Schwefel Marc Schoenauer, Kalyanmoy Deb, Günter Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, editor, Parallel Problem Solving from Nature — PPSN VI 6th International Conference, Paris, France, 16–20 2000. Springer Verlag. URL citeseer.nj.nee.com/corne00pareto.html.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • Richard M. Everson
    • 1
  • Jonathan E. Fieldsend
    • 1
  • Sameer Singh
    • 1
  1. 1.Department of Computer ScienceUniversity of ExeterExeterUK

Personalised recommendations