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Ranking empirical cumulative distribution functions using stochastic and pareto dominance

Published: 06 July 2018 Publication History

Abstract

In this paper, we propose two new approaches to rank the frequently used empirical cumulative distribution functions (ECDFs) for performance assessment of stochastic optimization algorithms. In the first approach, the different orders of stochastic dominance among running lengths are adopted in a hierarchical manner: the first order stochastic dominance is tested and the second order is used when the first order leads to incomparable results. In the second approach, ECDFs are considered as local Pareto front of the bi-criteria decision-making problem, in which one objective is to achieve a high success rate and the other is to use as few function evaluations as possible. In this case, it is proposed to adopt the multi-objective performance indicator to handle incomparable ECDFs.

References

[1]
Dimo Brockhoff, Tobias Wagner, and Heike Trautmann. 2012. On the Properties of the R2 Indicator. In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation (GECCO '12). ACM, New York, NY, USA, 465--472.
[2]
Carlos A Coello Coello and Margarita Reyes Sierra. 2004. A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In Mexican International Conference on Artificial Intelligence. Springer, 688--697.
[3]
Josef Hadar and William R Russell. 1969. Rules for ordering uncertain prospects. The American economic review 59, 1 (1969), 25--34.
[4]
Giora Hanoch and Haim Levy. 1975. The efficiency analysis of choices involving risk. In Stochastic Optimization Models in Finance. Elsevier, 89--100.
[5]
George A Whitmore. 1970. Third-degree stochastic dominance. The American Economic Review 60, 3 (1970), 457--459.
[6]
Elmar Wolfstetter, U Dulleck, R Inderst, P Kuhbier, and M Lands-Berger. 1993. Stochastic dominance: theory and applications. Humboldt-Univ., Wirtschaftswiss. Fak.
[7]
Eckart Zitzler and Lothar Thiele. 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation 3, 4 (1999), 257--271.

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cover image ACM Conferences
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2018
1968 pages
ISBN:9781450357647
DOI:10.1145/3205651
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 06 July 2018

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  1. empirical cumulative distribution functions
  2. pareto dominance
  3. stochastic dominance

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