On the performance assessment and comparison of stochastic multiobjective optimizers

  • Carlos M. Fonseca
  • Peter J. Fleming
Comparison of Methods
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1141)


This work proposes a quantitative, non-parametric interpretation of statistical performance of stochastic multiobjective optimizers, including, but not limited to, genetic algorithms. It is shown that, according to this interpretation, typical performance can be defined in terms analogous to the notion of median for ordinal data, as can other measures analogous to other quantiles.

Non-parametric statistical test procedures are then shown to be useful in deciding the relative performance of different multiobjective optimizers on a given problem. Illustrative experimental results are provided to support the discussion.


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    C. M. Fonseca and P. J. Fleming, “An overview of evolutionary algorithms in multiobjective optimization,” Evolutionary Computation, vol. 3, pp. 1–16, Spring 1995.Google Scholar
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  5. 5.
    C. M. Fonseca and P. J. Fleming, “Multiobjective genetic algorithms made easy: Selection, sharing and mating restriction,” in First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, (Sheffield, UK), pp. 45–52, The Institution of Electrical Engineers, 1995.Google Scholar

Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  • Carlos M. Fonseca
    • 1
  • Peter J. Fleming
    • 1
  1. 1.Department of Automatic Control and Systems EngineeringThe University of SheffieldSheffieldUK

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