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GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator

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The second author acknowledges support from CONACyT project no. 221551.

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References

  1. Bader, J., Zitzler, E.: HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization. Evolutionary Computation 19(1), 45–76 (2011)

    Article  Google Scholar 

  2. Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research 181(3), 1653–1669 (2007)

    Article  MATH  Google Scholar 

  3. Bringmann, K., Friedrich, T.: Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Theoretical Computer Science 425, 104–116 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  4. Brockhoff, D., Wagner, T., Trautmann, H.: On the properties of the \(R2\) indicator. In: 2012 Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, USA, pp. 465–472. ACM Press, July 2012. ISBN: 978-1-4503-1177-9

    Google Scholar 

  5. Coello Coello, C.A., Cortés, N.C.: Solving Multiobjective Optimization Problems using an Artificial Immune System. Genetic Programming and Evolvable Machines 6(2), 163–190 (2005)

    Google Scholar 

  6. Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007). ISBN 978-0-387-33254-3

    Google Scholar 

  7. Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002). ISBN 0-3064-6762-3

    Google Scholar 

  8. Das, I., Dennis, J.E.: Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems. SIAM J. on Optimization 8(3), 631–657 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  9. 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. Theoretical Advances and Applications, pp. 105–145. Springer, London (2005)

    Google Scholar 

  10. Domínguez-Medina, C., Rudolph, G., Schüetze, O., Trautmann, H.: Evenly spaced pareto fronts of quad-objective problems using PSA partitioning technique. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, June 20–23, pp. 3190–3197. IEEE Press (2013). ISBN 978-1-4799-0454-9

    Google Scholar 

  11. Farina, M., Amato, P. : On the optimal solution definition for many-criteria optimization problems. In: Proceedings of the NAFIPS-FLINT International Conference 2002, Piscataway, New Jersey, pp. 233–238. IEEE Service Center, June 2002

    Google Scholar 

  12. Gerstl, K., Rudolph, G., Schütze, O., Trautmann, H. : Finding evenly spaced fronts for multiobjective control via averaging hausdorff-measure. In: The 2011 8th International Conference on Electrical Engineering, Computer Science and Automatic Control (CCE 2011), Mérida, Yucatán, México, pp. 975–980. IEEE Press, October 2011

    Google Scholar 

  13. Gómez, R.H., Coello Coello, C.A.: MOMBI: a new metaheuristic for many-objective optimization based on the \(R2\) indicator. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, June 20–23, pp. 2488–2495. IEEE Press (2013). ISBN 978-1-4799-0454-9

    Google Scholar 

  14. Huband, S., Hingston, P., Barone, L., While, L.: A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)

    Article  Google Scholar 

  15. Igel, C., Hansen, N., Roth, S.: Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), 1–28 (2007)

    Article  Google Scholar 

  16. Knowles, J., Corne, D.: Properties of an Adaptive Archiving Algorithm for Storing Nondominated Vectors. IEEE Transactions on Evolutionary Computation 7(2), 100–116 (2003)

    Article  Google Scholar 

  17. Menchaca-Mendez, A., Coello Coello, C.A.: A new selection mechanism based on hypervolume and its locality property. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, June 20–23, pp. 924–931. IEEE Press (2013)

    Google Scholar 

  18. Menchaca-Mendez, A., Coello Coello, C.A.: MD-MOEA : a new MOEA based on the maximin fitness function and euclidean distances between solutions. In: 2014 IEEE Congress on Evolutionary Computation (CEC 2014), Beijing, China, July 6–11, pp. 2148–2155. IEEE Press (2014). ISBN 978-1-4799-1488-3

    Google Scholar 

  19. Rodríguez Villalobos, C.A., Coello Coello, C.A.: A new multi-objective evolutionary algorithm based on a performance assessment indicator. In: 2012 Genetic and Evolutionary Computation Conference (GECCO 2012), Philadelphia, USA, pp. 505–512. ACM Press, July 2012. ISBN: 978-1-4503-1177-9

    Google Scholar 

  20. Schütze, O., Esquivel, X., Lara, A., Coello, C.A.: Coello. Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 16(4), 504–522 (2012)

    Article  Google Scholar 

  21. Trautmann, Heike, Wagner, Tobias, Brockhoff, Dimo: R2-EMOA: Focused Multiobjective Search Using R2-Indicator-Based Selection. In: Nicosia, Giuseppe, Pardalos, Panos (eds.) LION 7. LNCS, vol. 7997, pp. 70–74. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  22. Phan, D.H., Suzuki, J.: R2-IBEA: R2 Indicator based evolutionary algorithm for multiobjective optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, June 20–23, pp. 1836–1845. IEEE Press (2013). ISBN 978-1-4799-0454-9

    Google Scholar 

  23. Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio, May 1999

    Google Scholar 

  24. Van Veldhuizen, D.A., Lamont, G.B.: Evolutionary computation and convergence to a pareto front. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1998 Conference, Stanford University, California, pp. 221–228. Stanford University Bookstore (1998)

    Google Scholar 

  25. Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective evolutionary algorithm test suites. In: Carroll, J., Haddad, H., Oppenheim, D., Bryant, B., Lamont, G.B. (eds.) Proceedings of the 1999 ACM Symposium on Applied Computing. San Antonio, Texas, pp. 351–357. ACM (1999)

    Google Scholar 

  26. Wagner, Tobias, Trautmann, Heike, Brockhoff, Dimo: Preference Articulation by Means of the R2 Indicator. In: Purshouse, Robin C., Fleming, Peter J., Fonseca, Carlos M., Greco, Salvatore, Shaw, Jane (eds.) EMO 2013. LNCS, vol. 7811, pp. 81–95. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  27. Zhang, Q., Li, H.: MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 11(6), 712–731 (2007)

    Article  Google Scholar 

  28. Zitzler, Eckart, Künzli, Simon: Indicator-Based Selection in Multiobjective Search. In: Yao, Xin, Burke, Edmund K., Lozano, José A., Smith, Jim, Merelo-Guervós, Juan Julián, Bullinaria, John A., Rowe, Jonathan E., Tiňo, Peter, Kabán, Ata, Schwefel, Hans-Paul (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  29. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M.: Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. CINVESTAV-IPN (Evolutionary Computation Group), Departamento de Computación, 07300, Mexico D.F., Mexico

    Adriana Menchaca-Mendez & Carlos A. Coello Coello

Authors
  1. Adriana Menchaca-Mendez
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  2. Carlos A. Coello Coello
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Corresponding author

Correspondence to Adriana Menchaca-Mendez .

Editor information

Editors and Affiliations

  1. Institute for Polymers and Composites/I3N,University of Minho, Guimaraes, Portugal

    António Gaspar-Cunha

  2. Dept. of Electrical and Computer Engg, University of Coimbra, Coimbra, Portugal

    Carlos Henggeler Antunes

  3. CINVESTAV-IPN Depto. de Computacíon, Col. San Pedro Zacatenco, México DF, Mexico

    Carlos Coello Coello

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Menchaca-Mendez, A., Coello Coello, C.A. (2015). GD-MOEA: A New Multi-Objective Evolutionary Algorithm Based on the Generational Distance Indicator. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-15934-8_11

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  • Online ISBN: 978-3-319-15934-8

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