Abstract
Comparing the performance of different evolutive multiobjective algorithms is an open problem. With time, many performance measures have been proposed. Unfortunately, the evaluations of many of these performance measures disagree with the common sense of when a multiobjective algorithm is performing better than another. In this work we present a benchmark that is helpful to check if a performance measure actually has a good behavior. Some of the most popular performance measures in literature are tested. The results are valuable for a better understanding of what performance measures are better.
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References
Beume, N., Rudolph, G.: Faster s-metric calculation by considering dominated hypervolume as klee’s measure problem. In: Kovalerchuk, B. (ed.) Computational Intelligence, pp. 233–238. IASTED/ACTA Press (2006)
Brockhoff, D., Zitzler, E.: Improving Hypervolume-based Multiobjective Evolutionary Algorithms by Using Objective Reduction Methods. In: Congress on Evolutionary Computation (CEC 2007) (2007)
Carlos, D.V., Coello, A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic/Plenum Publishers, New York (2002)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. John Wiley and Sons, Chichester (2001)
Fonseca, C.M., Paquete, L., López-Ibáñez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006), pp. 1157–1163 (2006)
Hansen, M.P., Jaszkiewicz, A.: Evaluating the quality of approximations to the non-dominated set. Technical Report IMM-REP-1998-7 (1998)
Knowles, J., Corne, D.: On Metrics for Comparing Nondominated Sets. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), vol. 1, pp. 711–716 (2002)
Schott, J.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Ph.D thesis, Massachusetts Institute of Technology (May 1995)
Zitzler, E.: Evolutionary Algorithms Multiobjective Optimization: Methods and Applications. Ph.D thesis, Swiss Federal Institute of Technology (ETH) (November 1999)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on Evolutionary Computation 7(2), 529–533 (2003)
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Lizárraga, G., Hernández, A., Botello, S. (2008). A Set of Test Cases for Performance Measures in Multiobjective Optimization. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_41
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DOI: https://doi.org/10.1007/978-3-540-88636-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
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