Skip to main content
SpringerLink
Log in

Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration

  • Chapter
  • First Online:
High-Performance Simulation-Based Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 833))

Abstract

Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it automatic. These research efforts go way beyond tuning only numerical parameters of already fully defined algorithms, but exploit automatic configuration as a means for automatic algorithm design. In this chapter, we review two main aspects where the research on automatic configuration and multi-objective optimization intersect. The first is the automatic configuration of multi-objective optimizers, where we discuss means and specific approaches. In addition, we detail a case study that shows how these approaches can be used to design new, high-performing multi-objective evolutionary algorithms. The second aspect is the research on multi-objective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few examples in this direction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In a different stream of research, Wessing et al. had also applied tuning methods to tune the variation operator of a multi-objective evolutionary algorithm applied to a single problem instance [71].

References

  1. Abbass, H.A.: The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation (CEC 2002), pp. 831–836. IEEE Press, NJ (2002)

    Google Scholar 

  2. Abbass, H.A., Sarker, R., Newton, C.: PDE: A Pareto-frontier differential evolution approach for multi-objective optimization problems. In: Proceedings of the 2001 Congress on Evolutionary Computation (CEC 2001), pp. 971–978. IEEE Press, NJ (2001)

    Google Scholar 

  3. Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) Principles and Practice of Constraint Programming, CP 2009. Lecture Notes in Computer Science, vol. 5732, pp. 142–157. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Balaprakash, P., Birattari, M., Stützle, T.: Improvement strategies for the F-race algorithm: Sampling design and iterative refinement. In: Bartz-Beielstein, T., Blesa, M.J., Blum, C., Naujoks, B., Roli, A., Rudolph, G., Sampels, M. (eds.) Hybrid Metaheuristics. Lecture Notes in Computer Science, vol. 4771, pp. 108–122. Springer, Heidelberg (2007)

    Google Scholar 

  5. Bartz-Beielstein, T., Lasarczyk, C., Preuss, M.: The sequential parameter optimization toolbox. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, pp. 337–360. Springer, Berlin (2010)

    Chapter  Google Scholar 

  6. Bezerra, L.C.T.: A component-wise approach to multi-objective evolutionary algorithms: from flexible frameworks to automatic design. Ph.D. thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium (2016)

    Google Scholar 

  7. Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: Automatic generation of multi-objective ACO algorithms for the biobjective knapsack. In: Dorigo, M., et al. (eds.) Swarm Intelligence, 8th International Conference, ANTS 2012. Lecture Notes in Computer Science, vol. 7461, pp. 37–48. Springer, Heidelberg (2012)

    Google Scholar 

  8. Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: Automatic component-wise design of multi-objective evolutionary algorithms (2015). http://iridia.ulb.ac.be/supp/IridiaSupp2014-010/

  9. Bezerra, L.C.T., López-Ibáñez, M., Stützle, T.: Automatic component-wise design of multi-objective evolutionary algorithms. IEEE Trans. Evol. Comput. 20(3), 403–417 (2016)

    Article  Google Scholar 

  10. Birattari, M.: The problem of tuning metaheuristics as seen from a machine learning perspective. Ph.D. thesis, IRIDIA, École polytechnique, Université Libre de Bruxelles, Belgium (2004)

    Google Scholar 

  11. Birattari, M., Yuan, Z., Balaprakash, P., Stützle, T.: F-race and iterated F-race: An overview. In: Bartz-Beielstein, T., Chiarandini, M., Paquete, L., Preuss, M. (eds.) Experimental Methods for the Analysis of Optimization Algorithms, pp. 311–336. Springer, Berlin (2010)

    Chapter  Google Scholar 

  12. Blot, A., Hoos, H.H., Jourdan, L., Kessaci-Marmion, M.E., Trautmann, H.: MO-ParamILS: A multi-objective automatic algorithm configuration framework. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) Learning and Intelligent Optimization, 10th International Conference, LION 10. Lecture Notes in Computer Science, vol. 10079, pp. 32–47. Springer, Cham (2016)

    Google Scholar 

  13. Blot, A., Pernet, A., Jourdan, L., Kessaci-Marmion, M.E., Hoos, H.H.: Automatically configuring multi-objective local search using multi-objective optimisation. In: Trautmann, H., Rudolph, G., Klamroth, K., Schütze, O., Wiecek, M.M., Jin, Y., Grimme, C. (eds.) Evolutionary Multi-criterion Optimization, EMO 2017. Lecture Notes in Computer Science, pp. 61–76. Springer International Publishing, Cham (2017)

    Google Scholar 

  14. Box, G.E.P., Draper, N.R.: Response Surfaces, Mixtures, and Ridge Analyses. Wiley, New York (2007)

    Google Scholar 

  15. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  16. Burke, E.K., Hyde, M.R., Kendall, G.: Grammatical evolution of local search heuristics. IEEE Trans. Evol. Comput. 16(7), 406–417 (2012)

    Article  Google Scholar 

  17. Dang Thi Thanh, N., De Causmaecker, P.: Motivations for the development of a multi-objective algorithm configurator. In: Vitoriano, B., Pinson, E., Valente, F. (eds.) ICORES 2014 - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems, pp. 328–333. SciTePress (2014)

    Google Scholar 

  18. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multiobjective optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization, Advanced Information and Knowledge Processing, pp. 105–145. Springer, London, UK (2005)

    Google Scholar 

  19. Dorigo, M., Gambardella, L.M.: Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  20. Dréo, J.: Using performance fronts for parameter setting of stochastic metaheuristics. In: Rothlauf, F. (ed.) GECCO (Companion), pp. 2197–2200. ACM Press, New York (2009)

    Google Scholar 

  21. Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Automatic configuration of state-of-the-art multi-objective optimizers using the TP\(+\)PLS framework. In: Krasnogor, N., Lanzi, P.L. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 2019–2026. ACM Press, New York (2011)

    Google Scholar 

  22. Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Combining two search paradigms for multi-objective optimization: Two-Phase and Pareto local search. In: Talbi, E.G. (ed.) Hybrid Metaheuristics. Studies in Computational Intelligence, vol. 434, pp. 97–117. Springer, Berlin (2013)

    Chapter  Google Scholar 

  23. Eiben, A.E., Smit, S.K.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)

    Article  Google Scholar 

  24. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems (NIPS 28), pp. 2962–2970 (2015)

    Google Scholar 

  25. Fukunaga, A.S.: Evolving local search heuristics for SAT using genetic programming. In: Deb, K. et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2004, Part II. Lecture Notes in Computer Science, vol. 3103, pp. 483–494. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  26. Fukunaga, A.S.: Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008)

    Article  Google Scholar 

  27. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, USA (1989)

    Google Scholar 

  28. Hoos, H.H.: Automated algorithm configuration and parameter tuning. In: Hamadi, Y., Monfroy, E., Saubion, F. (eds.) Autonomous Search, pp. 37–71. Springer, Berlin (2012)

    Chapter  Google Scholar 

  29. Hoos, H.H.: Programming by optimization. Commun. ACM 55(2), 70–80 (2012)

    Article  Google Scholar 

  30. Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Trans. Evol. Comput. 10(5), 477–506 (2006)

    Article  Google Scholar 

  31. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Automated configuration of mixed integer programming solvers. In: Lodi, A., Milano, M., Toth, P. (eds.) 7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2010. Lecture Notes in Computer Science, vol. 6140, pp. 186–202. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  32. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello Coello, C.A. (ed.) 5th International Conference on Learning and Intelligent Optimization, LION 5. Lecture Notes in Computer Science, vol. 6683, pp. 507–523. Springer, Heidelberg (2011)

    Google Scholar 

  33. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: An automatic algorithm configuration framework. J. Artif. Intell. Res. 36, 267–306 (2009)

    Article  Google Scholar 

  34. Hutter, F., Xu, L., Hoos, H.H., Leyton-Brown, K.: Algorithm runtime prediction: Methods and evaluation. Artif. Intell. 206, 79–111 (2014)

    Article  MathSciNet  Google Scholar 

  35. IBM: ILOG CPLEX optimizer (2017). http://www.ibm.com/software/integration/optimization/cplex-optimizer/

  36. Ishibuchi, H., Masuda, H., Tanigaki, Y., Nojima, Y.: Modified distance calculation in generational distance and inverted generational distance. In: Gaspar-Cunha, A., Antunes, C.H., Coello Coello, C.A. (eds.) Evolutionary Multi-criterion Optimization, EMO 2015 Part I. Lecture Notes in Computer Science, vol. 9018, pp. 110–125. Springer, Heidelberg (2015)

    Google Scholar 

  37. Jiang, S., Ong, Y.S., Zhang, J., Feng, L.: Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Trans. Cybern. 44(12), 2391–2404 (2014)

    Article  Google Scholar 

  38. KhudaBukhsh, A.R., Xu, L., Hoos, H.H., Leyton-Brown, K.: SATenstein: Automatically building local search SAT solvers from components. In: Boutilier, C. (ed.) Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 517–524. AAAI Press, CA (2009)

    Google Scholar 

  39. Knowles, J.D., Thiele, L., Zitzler, E.: A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report 214, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zürich, Switzerland (2006). Revised version

    Google Scholar 

  40. Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 17, 1–5 (2016)

    Google Scholar 

  41. Kukkonen, S., Lampinen, J.: GDE3: the third evolution step of generalized differential evolution. In: Proceedings of the 2005 Congress on Evolutionary Computation (CEC 2005), pp. 443–450. IEEE Press, NJ (2005)

    Google Scholar 

  42. Liao, T., Stützle, T., Montes de Oca, M.A., Dorigo, M.: A unified ant colony optimization algorithm for continuous optimization. Eur. J. Oper. Res. 234(3), 597–609 (2014)

    Article  MathSciNet  Google Scholar 

  43. Liefooghe, A., Jourdan, L., Talbi, E.G.: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. Eur. J. Oper. Res. 209(2), 104–112 (2011)

    Article  MathSciNet  Google Scholar 

  44. Lindauer, M.T., Hoos, H.H., Hutter, F., Schaub, T.: AutoFolio: An automatically configured algorithm selector. J. Artif. Intell. Res. 53, 745–778 (2015)

    Article  Google Scholar 

  45. López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Stützle, T., Birattari, M.: The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)

    Article  MathSciNet  Google Scholar 

  46. López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The irace package, iterated race for automatic algorithm configuration. Technical report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles, Belgium (2011)

    Google Scholar 

  47. López-Ibáñez, M., Stützle, T.: An analysis of algorithmic components for multiobjective ant colony optimization: a case study on the biobjective TSP. In: Collet, P., Monmarché, N., Legrand, P., Schoenauer, M., Lutton, E. (eds.) Artificial Evolution: 9th International Conference, Evolution Artificielle, EA, 2009. Lecture Notes in Computer Science, vol. 5975, pp. 134–145. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  48. López-Ibáñez, M., Stützle, T.: Automatic configuration of multi-objective ACO algorithms. In: Dorigo, M., et al. (eds.) 7th International Conference on Swarm Intelligence, ANTS 2010. Lecture Notes in Computer Science, vol. 6234, pp. 95–106. Springer, Heidelberg (2010)

    Google Scholar 

  49. López-Ibáñez, M., Stützle, T.: The impact of design choices of multi-objective ant colony optimization algorithms on performance: An experimental study on the biobjective TSP. In: Pelikan, M., Branke, J. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp. 71–78. ACM Press, New York (2010)

    Google Scholar 

  50. López-Ibáñez, M., Stützle, T.: The automatic design of multi-objective ant colony optimization algorithms. IEEE Trans. Evol. Comput. 16(6), 861–875 (2012)

    Article  Google Scholar 

  51. Lourenço, H.R., Martin, O., Stützle, T.: Iterated local search. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 321–353. Kluwer Academic Publishers, Norwell, MA (2002)

    Google Scholar 

  52. Madavan, N.K.: Multiobjective optimization using a Pareto differential evolution approach. In: Proceedings of the 2002 World Congress on Computational Intelligence (WCCI 2002), pp. 1145–1150. IEEE Press, NJ (2002)

    Google Scholar 

  53. Marmion, M.E., Mascia, F., López-Ibáñez, M., Stützle, T.: Automatic design of hybrid stochastic local search algorithms. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds.) Hybrid Metaheuristics. Lecture Notes in Computer Science, vol. 7919, pp. 144–158. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  54. Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., Marmion, M.E., Stützle, T.: Algorithm comparison by automatically configurable stochastic local search frameworks: A case study using flow-shop scheduling problems. In: Blesa, M.J., Blum, C., Voß, S. (eds.) Hybrid Metaheuristics. Lecture Notes in Computer Science, vol. 8457, pp. 30–44. Springer, Heidelberg (2014)

    Google Scholar 

  55. Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T.: From grammars to parameters: Automatic iterated greedy design for the permutation flow-shop problem with weighted tardiness. In: Pardalos, P.M., Nicosia, G. (eds.) 7th International Conference on Learning and Intelligent Optimization, LION 7. Lecture Notes in Computer Science, vol. 7997, pp. 321–334. Springer, Heidelberg (2013)

    Google Scholar 

  56. Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T.: Grammar-based generation of stochastic local search heuristics through automatic algorithm configuration tools. Comput. Oper. Res. 51, 190–199 (2014)

    Article  MathSciNet  Google Scholar 

  57. Mendoza, H., Klein, A., Feurer, M., Springenberg, J.T., Hutter, F.: Towards automatically-tuned neural networks. In: Workshop on Automatic Machine Learning, pp. 58–65 (2016)

    Google Scholar 

  58. Minella, G., Ruiz, R., Ciavotta, M.: A review and evaluation of multiobjective algorithms for the flowshop scheduling problem. INFORMS J. Comput. 20(3), 451–471 (2008)

    Article  MathSciNet  Google Scholar 

  59. Miranda, P., Silva, R.M., Prudêncio, R.B.: I/S-Race: An iterative multi-objective racing algorithm for the SVM parameter selection problem. In: 22st European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning, Bruges, April 23-24-25, 2014, pp. 573–578. ESANN (2015)

    Google Scholar 

  60. Paquete, L., Chiarandini, M., Stützle, T.: Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study. In: Gandibleux, X., Sevaux, M., Sörensen, K., T’Kindt, V. (eds.) Metaheuristics for Multiobjective Optimisation. Lecture Notes in Economics and Mathematical Systems, vol. 535, pp. 177–200. Springer, Berlin (2004)

    MATH  Google Scholar 

  61. Paquete, L., Stützle, T.: A two-phase local search for the biobjective traveling salesman problem. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) Evolutionary Multi-criterion Optimization, EMO 2003. Lecture Notes in Computer Science, vol. 2632, pp. 479–493. Springer, Heidelberg (2003)

    Google Scholar 

  62. Pérez Cáceres, L., López-Ibáñez, M., Hoos, H.H., Stützle, T.: An experimental study of adaptive capping in irace. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) 11th International Conference on Learning and Intelligent Optimization, LION 11. Lecture Notes in Computer Science, vol. 10556, pp. 235–250. Springer, Cham (2017)

    Google Scholar 

  63. Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer, New York (2005)

    Google Scholar 

  64. Robič, T., Filipič, B.: DEMO: Differential evolution for multiobjective optimization. In: Coello Coello, C.A., Aguirre, A.H., Zitzler, E. (eds.) Evolutionary Multi-criterion Optimization, EMO 2005. Lecture Notes in Computer Science, vol. 3410, pp. 520–533. Springer, Heidelberg (2005)

    Google Scholar 

  65. Schütze, O., Esquivel, X., Lara, A., Coello Coello, C.A.: Using the averaged Hausdorff distance as a performance measure in evolutionary multiobjective optimization. IEEE Trans. Evol. Comput. 16(4), 504–522 (2012)

    Article  Google Scholar 

  66. Stützle, T.: ACOTSP: A software package of various ant colony optimization algorithms applied to the symmetric traveling salesman problem (2002). http://www.aco-metaheuristic.org/aco-code/

  67. Stützle, T., Hoos, H.H.: \({\cal{M}}{\cal{A}}{\cal{X}}\)-\({\cal{M}}{\cal{I}}{\cal{N}}\) Ant System. Futur. Gener. Comput. Syst. 16(8), 889–914 (2000)

    Article  Google Scholar 

  68. Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In: Dhillon, I.S., Koren, Y., Ghani, R., Senator, T.E., Bradley, P., Parekh, R., He, J., Grossman, R.L., Uthurusamy, R. (eds.) The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 847–855. ACM Press, New York (2013)

    Google Scholar 

  69. Tušar, T., Filipič, B.: Differential evolution versus genetic algorithms in multiobjective optimization. In: Obayashi, S., et al. (eds.) Evolutionary Multi-criterion Optimization, EMO 2007. Lecture Notes in Computer Science, vol. 4403, pp. 257–271. Springer, Heidelberg (2007)

    Google Scholar 

  70. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evol. Comput. 8(2), 125–147 (2000)

    Article  Google Scholar 

  71. Wessing, S., Beume, N., Rudolph, G., Naujoks, B.: Parameter tuning boosts performance of variation operators in multiobjective optimization. In: Schaefer, R., Cotta, C., Kolodziej, J., Rudolph, G. (eds.) Parallel Problem Solving from Nature, PPSN XI. Lecture Notes in Computer Science, vol. 6238, pp. 728–737. Springer, Heidelberg (2010)

    Google Scholar 

  72. Zhang, T., Georgiopoulos, M., Anagnostopoulos, G.C.: S-Race: A multi-objective racing algorithm. In: Blum, C., Alba, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2013, pp. 1565–1572. ACM Press, New York (2013)

    Google Scholar 

  73. Zhang, T., Georgiopoulos, M., Anagnostopoulos, G.C.: SPRINT: Multi-objective model racing. In: Silva, S., Esparcia-Alcázar, A.I. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2015, pp. 1383–1390. ACM Press, New York (2015)

    Google Scholar 

  74. Zhang, T., Georgiopoulos, M., Anagnostopoulos, G.C.: Multi-objective model selection via racing. IEEE Trans. Cybern. 46(8), 1863–1876 (2016)

    Article  Google Scholar 

  75. Zitzler, E., Thiele, L., Bader, J.: On set-based multiobjective optimization. IEEE Trans. Evol. Comput. 14(1), 58–79 (2010)

    Article  Google Scholar 

  76. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

This work received support from the COMEX project within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office. Thomas Stützle acknowledges support from the Belgian F.R.S.-FNRS, of which he is a research director.

Author information

Authors and Affiliations

  1. Instituto Metrópole Digital (IMD), Universidade Federal do Rio Grande do Norte (UFRN), Natal, RN, Brazil

    Leonardo C. T. Bezerra

  2. Alliance Manchester Business School, University of Manchester, Manchester, UK

    Manuel López-Ibáñez

  3. IRIDIA, Université Libre de Bruxelles (ULB), Brussels, Belgium

    Thomas Stützle

Authors
  1. Leonardo C. T. Bezerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel López-Ibáñez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Stützle
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Stützle .

Editor information

Editors and Affiliations

  1. TH Köln, Cologne, Germany

    Thomas Bartz-Beielstein

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Bogdan Filipič

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  4. University Lille, Lille, France

    El-Ghazali Talbi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bezerra, L.C.T., López-Ibáñez, M., Stützle, T. (2020). Automatic Configuration of Multi-objective Optimizers and Multi-objective Configuration. In: Bartz-Beielstein, T., Filipič, B., Korošec, P., Talbi, EG. (eds) High-Performance Simulation-Based Optimization. Studies in Computational Intelligence, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-18764-4_4

Download citation

Publish with us

Policies and ethics

Search

Navigation