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The Scientific World Journal
Volume 2015, Article ID 439307, 10 pages
http://dx.doi.org/10.1155/2015/439307
Research Article

A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

1School of Computer, China University of Geosciences, Wuhan 430074, China
2Department of Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow G1 1XJ, UK

Received 23 June 2014; Revised 15 September 2014; Accepted 30 December 2014

Academic Editor: Shifei Ding

Copyright © 2015 Zhiming Song et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. Back, D. B. Fogel, and Z. Michalewicz, Handbook of Evolutionary Computation, Oxford University Press, Oxford, UK, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  2. J. D. Schaffer, Multiple objective optimization with vector evaluated genetic algorithms [Ph.D. thesis], Vanderbilt University, Nashville, Tenn, USA, 1984.
  3. C. A. C. Coello, D. A. van Veldhuizen, and G. B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems, Kluwer Academic Publishers, New York, NY, USA, 2002.
  4. C. A. C. Coello, “A comprehensive survey of evolutionary-based multiobjective optimization techniques,” Knowledge and Information Systems, vol. 1, no. 3, pp. 269–308, 1999. View at Publisher · View at Google Scholar
  5. Q. Zhang, A. Zhou, and Y. Jin, “RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 41–63, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. A. Jaszkiewicz, “Genetic local search for multi-objective combinatorial optimization,” European Journal of Operational Research, vol. 137, no. 1, pp. 50–71, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. K. Deb, A. Sinha, and S. Kukkonen, “Multi-objective test problems, linkages, and evolutionary methodologies,” in Proceedings of the 8th Annual Genetic and Evolutionary Computation Conference (GECCO '06), pp. 1141–1148, Seattle, Wash, USA, July 2006. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Jin and B. Sendhoff, “Connectedness, regularity and the success of local search in evolutionary multi-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '03), vol. 3, pp. 1910–1917, IEEE, Canberra, Australia, December 2003. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Zhou, Q. Zhang, Y. Jin, E. Tsang, and T. Okabe, “A model-based evolutionary algorithm for Bi-objective optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '05), pp. 2568–2575, Edinburgh, UK, September 2005. View at Scopus
  10. A. Zhou, Y. Jin, Q. Zhang, B. Sendhoff, and E. Tsang, “Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06), pp. 3234–3241, Vancouve, Canada, July 2006. View at Scopus
  11. A. Zhou, Q. Zhang, and Y. Jin, “Approximating the set of pareto-optimal solutions in both the decision and objective spaces by an estimation of distribution algorithm,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 5, pp. 1167–1189, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. O. Schutze, S. Mostaghim, M. Dellnitz, and J. Teich, “Covering Pareto sets by multilevel evolutionary subdivision techniques,” in Evolutionary Multi-Criterion Optimization: Second International Conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings, vol. 2632 of Lecture Notes in Computer Science, pp. 118–132, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  13. T. Hastie and W. Stuetzle, “Principal curves,” Journal of the American Statistical Association, vol. 84, no. 406, pp. 502–516, 1989. View at Publisher · View at Google Scholar · View at MathSciNet
  14. N. Kambhatla and T. K. Leen, “Dimension reduction by local principal component analysis,” Neural Computation, vol. 9, no. 7, pp. 1493–1516, 1997. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Wang, G. Dai, and H. Hu, “Improved NSGA-II algorithm for optimization of constrained functions,” in Proceedings of the International Conference on Machine Vision and Human-Machine Interface, pp. 673–675, Kaifeng, China, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Deb and S. Jain, “Running performance metrics for evolutionary multiobjective optimization,” Tech. Rep. 2002004, KanGAL, India Institute of Technology, Kanpur, India, 2004. View at Google Scholar