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Home All issues Volume 1 / No 1 (October 2007) Int. J. Simul. Multidisci. Des. Optim., 1 1 (2007) 1-8 References
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Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 1, Number 1, October 2007
Page(s) 1 - 8
DOI https://doi.org/10.1051/ijsmdo:2007001
Published online 12 December 2007
  1. B.V. Babu, M. Mathew Leenus Jehan, Differential Evolution for Multi-Objective Optimization. In Proceedings of the 2003 Congress on Evolutionary Computation (CEC'2003), volume 4, pages 2696–2703, Canberra, Australia (December 2003). IEEE Press. [Google Scholar]
  2. S. Bleuler, M. Brack, E. Zitzler, Multiobjective genetic programming: Reducing bloat using spea2. In Proceedings of the 2001 Congress on Evolutionary Computation, pages 536–543 (2001). [Google Scholar]
  3. J. Branke, K. Deb, Integrating user preferences into evolutionary multi-objective optimization. In Y. Jin, editor, Knowledge Incorporation in Evolutionary Computation, pages 461–477. Hiedelberg, Germany: Springer (2004). [Google Scholar]
  4. J. Branke, K. Deb, K. Mietinnen, R. Slowinski, Multiobjective optimization: Interactive and evolutionary approaches. Springer-Verlag (in press). [Google Scholar]
  5. D. Brockhoff, E. Zitzler, Dimensionality Reduction in Multiobjective Optimization: The Minimum Objective Subset Problem. In K.H. Waldmann and U. M. Stocker, editors, Operations Research Proceedings 2006, pages 423–429. Springer (2007). [Google Scholar]
  6. C.A.C. Coello, G. Toscano, A micro-genetic algorithm for multi-objective optimization. Technical Report Lania-RI-2000-06, Laboratoria Nacional de Informatica Avanzada, Xalapa, Veracruz, Mexico (2000). [Google Scholar]
  7. C.A.C. Coello, M.S. Lechuga, MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization. In Congress on Evolutionary Computation (CEC'2002), volume 2, pages 1051–1056, Piscataway, New Jersey (May 2002). IEEE Service Center. [Google Scholar]
  8. C.A.C. Coello, Treating objectives as constraints for single objective optimization. Engineering Optimization 32, 275–308 (2000) [CrossRef] [Google Scholar]
  9. D. Corne, J. Knowles, M. Oates, The Pareto envelope-based selection algorithm for multiobjective optimization, In Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature VI (PPSN-VI), pages 839–848 (2000). [Google Scholar]
  10. D.W. Corne, N.R. Jerram, J.D. Knowles, M.J. Oates, PESA-II: Region-based selection in evolutionary multiobjective optimization, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 283–290. San Mateo, CA: Morgan Kaufmann Publishers (2001). [Google Scholar]
  11. D.W. Corne, J.D. Knowles, Techniques for highly multiobjective optimisation: some nondominated points are better than others, In GECCO'07: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 773–780, New York, NY, USA (2007). ACM Press. [Google Scholar]
  12. D. Daum, K. Deb, J. Branke, Reliability-based optimization for multiple constraints with evolutionary algorithms, In Proceedings of the Congress on Evolutionary Computation (CEC-2007), in press. [Google Scholar]
  13. K. Deb, Multi-objective optimization using evolutionary algorithms. Chichester, UK: Wiley (2001). [Google Scholar]
  14. K. Deb, S. Agrawal, A. Pratap, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002) [Google Scholar]
  15. K. Deb, H. Gupta, Searching for robust Pareto-optimal solutions in multi-objective optimization, In Proceedings of the Third Evolutionary Multi-Criteria Optimization (EMO-05) Conference (Also Lecture Notes on Computer Science 3410), pages 150–164 (2005). [Google Scholar]
  16. K. Deb, A. Kumar, Interactive evolutionary multi-objective optimization and decision-making using reference direction method, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2007), pages 781–788. New York: The Association of Computing Machinery (ACM), 2007. [Google Scholar]
  17. K. Deb, A. Kumar, Light beam search based multi-objective optimization using evolutionary algorithms, Technical Report KanGAL Report No. 2007005, Indian Institute of Technology Kanpur, India, 2007. [Google Scholar]
  18. K. Deb, M. Mohan, S. Mishra, Towards a quick computation of well-spread pareto-optimal solutions. In Proceedings of the Second Evolutionary Multi-Criterion Optimization (EMO-03) Conference (LNCS 2632), pages 222–236 (2003). [Google Scholar]
  19. K. Deb, D. Padmanabhan, S. Gupta, A.K. Mall, Reliability-based multi-objective optimization using evolutionary algorithms. In Proceedings of the Fourth International Conference on Evolutionary Multi-Criterion Optimization (EMO-2007) (LNCS, Springer), pages 66–80 (2007). [Google Scholar]
  20. K. Deb, D. Saxena, Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In Proceedings of the World Congress on Computational Intelligence (WCCI-2006), pages 3352–3360 (2006). [Google Scholar]
  21. K. Deb, A. Srinivasan, Innovization: Innovating design principles through optimization. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2006), pages 1629–1636, New York: The Association of Computing Machinery (ACM) (2006). [Google Scholar]
  22. K. Deb, J. Sundar, N. Uday, S. Chaudhuri, Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research 2, 273–286 (2006) [Google Scholar]
  23. C.M. Fonseca, P.J. Fleming, Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization, In Proceedings of the Fifth International Conference on Genetic Algorithms, pages 416–423 (1993). [Google Scholar]
  24. M. Gravel, W.L. Price, C. Gagné, Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic. Eur. J. Oper. Res. 143, 218–229 (2002) [CrossRef] [Google Scholar]
  25. J. Handl, J. Knowles, An evolutionary approach to multiobjective clustering. IEEE T. Evolut. Comput. 11, 56–76 (2007) [CrossRef] [Google Scholar]
  26. J. Horn, N. Nafploitis, D.E. Goldberg, A niched Pareto genetic algorithm for multi-objective optimization, In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 82–87 (1994). [Google Scholar]
  27. J. Knowles, D. Corne, K. Deb, Multiobjective problem solving from nature, Springer Natural Computing Series, Springer-Verlag (in press). [Google Scholar]
  28. J.D. Knowles, D.W. Corne, Approximating the non-dominated front using the Pareto archived evolution strategy. Evol. Comput. 8, 149–172 (2000) [CrossRef] [Google Scholar]
  29. P. Korhonen, J. Laakso, A visual interactive method for solving the multiple criteria problem. Eur. J. Oper. Res. 24, 277–287 (1986) [CrossRef] [Google Scholar]
  30. M. Laumanns, L. Thiele, K. Deb, E. Zitzler, Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10, 263–282 (2002) [CrossRef] [Google Scholar]
  31. D.H. Loughlin, S. Ranjithan, The neighborhood constraint method: A multiobjective optimization technique, In Proceedings of the Seventh International Conference on Genetic Algorithms, pages 666–673 (1997). [Google Scholar]
  32. M. Luque, K. Miettinen, P. Eskelinen, F. Ruiz, Three different ways for incorporating preference information in interactive reference point based methods, Technical Report W-410, Helsinki School of Economics, Helsinki, Finland (2006). [Google Scholar]
  33. P.R. McMullen, An ant colony optimization approach to addessing a JIT sequencing problem with multiple objectives. Artificial Intelligence in Engineering 15, 309–317 (2001) [CrossRef] [Google Scholar]
  34. K. Miettinen, Nonlinear Multiobjective Optimization, Kluwer, Boston (1999). [Google Scholar]
  35. S. Mostaghim, J. Teich, Strategies for Finding Good Local Guides in Multi-objective Particle Swarm Optimization (MOPSO), In 2003 IEEE Swarm Intelligence Symposium Proceedings, pages 26–33, Indianapolis, Indiana, USA (April 2003). IEEE Service Center. [Google Scholar]
  36. D. Sasaki, M. Morikawa, S. Obayashi, K. Nakahashi, Aerodynamic shape optimization of supersonic wings by adaptive range multiobjective genetic algorithms, In Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization (EMO 2001), pages 639–652 (2001). [Google Scholar]
  37. D. Saxena, K. Deb, Trading on infeasibility by exploiting constraint's criticality through multi-objectivization: A system design perspective, In Proceedings of the Congress on Evolutionary Computation (CEC-2007), in press. [Google Scholar]
  38. C. Seepersad, F. Mistree, J.K. Allen, A quantitative approach for designing multiple product platforms for an evolution portfolio of products, In Proceedings of ASME Design Engineering Technical Conferences, pages 593–602 (2002). [Google Scholar]
  39. N. Srinivas, K. Deb, Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol. Comput. 2, 221–248 (1994) [Google Scholar]
  40. L. Thiele, K. Miettinen, P. Korhonen, J. Molina, A preference-based interactive evolutionary algorithm for multiobjective optimization, Technical Report Working Paper Number W-412, Helsingin School of Economics, Helsingin Kauppakorkeakoulu, Finland (2007). [Google Scholar]
  41. D. Van Veldhuizen, G.B. Lamont, Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evol. Comput. 8, 125–148 (2000) [CrossRef] [PubMed] [Google Scholar]
  42. A.P. Wierzbicki, The use of reference objectives in multiobjective optimization, In G. Fandel and T. Gal, editors, Multiple Criteria Decision Making Theory and Applications, pages 468–486. Berlin: Springer-Verlag (1980). [Google Scholar]
  43. E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization, In K.C. Giannakoglou, D.T. Tsahalis, J. Périaux, K.D. Papailiou, and T. Fogarty, editors, Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pages 95–100, Athens, Greece, 2001. International Center for Numerical Methods in Engineering (Cmine). [Google Scholar]
  44. E. Zitzler, L. Thiele, Multiobjective optimization using evolutionary algorithms – A comparative case study, In Parallel Problem Solving from Nature V (PPSN-V), pages 292–301 (1998). [Google Scholar]

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