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Performance assessment of multi-objective evolutionary algorithms with the R package ecr

Published:06 July 2018Publication History

ABSTRACT

Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.

References

  1. Thomas Bartz-Beielstein, Christian Lasarczyk, and Mike Preuss. 2005. Sequential Parameter Optimization. In Proceedings Congress on Evolutionary Computation 2005 (CEC'05). Edinburgh, Scotland, 1553. http://www.spotseven.de/wp-content/papercite-data/pdf/blp05.pdfGoogle ScholarGoogle ScholarCross RefCross Ref
  2. Nicola Beume, Boris Naujoks, and Michael Emmerich. 2007. SMS-EMOA: Multi-objective selection based on dominated hypervolume. European Journal of Operational Research 181, 3 (2007), 1653--1669.Google ScholarGoogle ScholarCross RefCross Ref
  3. Mickael Binois and Victor Picheny. 2018. GPareto: Gaussian Processes for Pareto Front Estimation and Optimization. https://CRAN.R-project.org/package=GPareto R package version 1.1.1.Google ScholarGoogle Scholar
  4. Bernd Bischl, Jakob Richter, Jakob Bossek, Daniel Horn, Janek Thomas, and Michel Lang. 2017. mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions. http://arxiv.org/abs/1703.03373Google ScholarGoogle Scholar
  5. S Bleuler, M Laumanns, L Thiele, and E Zitzler. 2003. PISA - a platform and programming language independent interface for search algorithms. In Proceedings of International Conference on Evolutionary Multi-Criterion Optimization (EMO), C M Fonseca, P J Fleming, E Zitzler, K Deb, and L Thiele (Eds.). Springer, Berlin, Germany, 494--508. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jakob Bossek. 2017. ecr 2.0: A Modular Framework for Evolutionary Computation in R. In Genetic and Evolutionary Computation Conference. Berlin, Germany. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jakob Bossek and Christian Grimme. 2017. A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Honolulu, HI, USA, 3280--3287.Google ScholarGoogle ScholarCross RefCross Ref
  8. Jakob Bossek and Christian Grimme. 2017. An Extended Mutation-Based Priority-Rule Integration Concept for Multi-Objective Machine Scheduling. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Honolulu, HI, USA, 3288--3295.Google ScholarGoogle Scholar
  9. Jakob Bossek and Christian Grimme. 2018. Solving Scalarized Subproblems within Evolutionary Algorithms for Multi-Criteria Shortest Path Problems. In Proceedings of the 12th International Conference on Learning and Intelligent Optimization (LION 2018). Springer International Publishing, Kalamata, Greece, accepted.Google ScholarGoogle Scholar
  10. Jakob Bossek and Heike Trautmann. 2016. Understanding characteristics of evolved instances for state-of-the-art inexact TSP solvers with maximum performance difference. In AI*IA 2016 Advances in Artificial Intelligence, G. Adorni, S. Cagnoni, M. Gori, and M. Maratea (Eds.), Vol. 10037 LNAI. Springer International Publishing, Genova, Italy, 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Carlos A Coello Coello, Gary B Lamont, and David A Van Veldhuizen. 2006. Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer-Verlag New York, Inc., Secaucus, NJ, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, and T Meyarivan. 2000. A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II. In Parallel Problem Solving from Nature PPSN VI, Marc Schoenauer, Kalyanmoy Deb, Günther Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 849--858. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Matt Dowle and Arun Srinivasan. 2017. data.table: Extension of 'data.frame'. https://CRAN.R-project.org/package=data.table R package version 1.10.4-3.Google ScholarGoogle Scholar
  14. N. Hansen and A. Jaszkiewicz. 2006. Evaluating the quality of approximations to the non-dominated set. Technical Report. Technical University of Denmark.Google ScholarGoogle Scholar
  15. Joshua Knowles and David Corne. 2002. On Metrics for Comparing Non-Dominated Sets. In Proceedings of the 2002 Congress on Evolutionary Computation Conference (CEC02). Institute of Electrical and Electronics Engineers, Honolulu, HI, USA, 711--716.Google ScholarGoogle Scholar
  16. J Knowles, L Thiele, and E Zitzler. 2006. A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. Technical Report. Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland, https://sop.tik.ee.ethz.ch/KTZ2005a.pdfGoogle ScholarGoogle Scholar
  17. Asep Maulana, Marios Kefalas, and Michael T. M. Emmerich. 2017. Immunization of Networks Using Genetic Algorithms and Multiobjective Metaheuristics. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Honolulu, HI, USA, 2953--2960.Google ScholarGoogle Scholar
  18. Olaf Mersmann. 2012. emoa: Evolutionary Multiobjective Optimization Algorithms. https://CRAN.R-project.org/package=emoa R package version 0.5-0.Google ScholarGoogle Scholar
  19. Olaf Mersmann. 2014. mco: Multiple Criteria Optimization Algorithms and Related Functions. https://CRAN.R-project.org/package=mco R package version 1.0-15.1.Google ScholarGoogle Scholar
  20. Adriano Passos. 2017. moko: Multi-Objective Kriging Optimization. https://CRAN.R-project.org/package=moko R package version 1.0.1.Google ScholarGoogle Scholar
  21. Serpil Saym. 2000. Measuring the quality of discrete representations of efficient sets in multiple objective mathematical programming. Mathematical Programming 87, 3 (01 May 2000), 543--560.Google ScholarGoogle Scholar
  22. V. T'kindt and J.-C. Billaut. 2006. Multicriteria Scheduling: Theory, Models and Algorithms (2nd ed.). Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. David Allen Van Veldhuizen. 1999. Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. Ph.D. Dissertation. Wright Patterson AFB, OH, USA. Advisor(s) Lamont, Gary B. AAI9928483.Google ScholarGoogle Scholar
  24. Hadley Wickham. 2009. ggplot2: Elegant Graphics for Data Analysis. Springer New York, http://ggplot2.org Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hadley Wickham, Romain Francois, Lionel Henry, and Kirill MÃijller. 2017. dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr R package version 0.7.4.Google ScholarGoogle Scholar
  26. E. Zitzler and L. Thiele. 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation 3, 4 (1999), 257--271. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca. 2003. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions of Evolutionary Computation 7, 2 (2003), 117--132. Google ScholarGoogle ScholarDigital LibraryDigital Library

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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

        Copyright © 2018 ACM

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        • Published: 6 July 2018

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