A many-objective test problem for visually examining diversity maintenance behavior in a decision space

Genetic and Evolutionary Computation Conference, GECCO'11

Volumn , Issue , 2011, Pages 649-656

  Ishibuchi, Hisao ^a   Akedo, Naoya ^a   Nojima, Yusuke ^a  

^a Osaka Prefecture University   (Japan)

Author keywords

Decision space diversity; Evolutionary multiobjective optimization (EMO); Many objective optimization problems

Indexed keywords

CONVENIENCE STORES; DECISION SPACE; DIVERSITY MAINTENANCE; ELEMENTARY SCHOOLS; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; JUNIOR HIGH SCHOOLS; MANY-OBJECTIVE OPTIMIZATION PROBLEMS; MINIMIZATION PROBLEMS; PARETO-OPTIMAL; RAILWAY STATIONS; REAL WORLD SITUATIONS; TEST PROBLEM;

EVOLUTIONARY ALGORITHMS; MAINTENANCE; MAPS; MULTIOBJECTIVE OPTIMIZATION; TWO DIMENSIONAL;

TESTING;

EID: 84860405107     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2001576.2001666     Document Type: Conference Paper

References (25)

1. Auger, A., Bader, J., Brockhoff, D., and Zitzler, E. Theory of the hypervolume indicator: Optimal μ-distributions and the choice of the reference point. Proc. of Foundations of Genetic Algorithm X (2009) 87-102.
2. Beume, N., Naujoks, B., and Emmerich, M. SMS-EMOA: multiobjective selection based on dominated hypervolume. European Journal of Operational Research 180, 3 (2007) 1653-1669. (Pubitemid 46498552)
3. Coello, C. A. C. and Lamont, G. B. Applications of Multi-Objective Evolutionary Algorithms. World Scientific, Singapore (2004).
4. Coello, C. A. C., van Veldhuizen, D. A., and Lamont, G. B. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer, Boston (2002).
5. Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001).
6. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary Computation 6, 2 (2002) 182-197. (Pubitemid 34555372)
7. Friedrich, T., Horoba, C., and Neumann, F. Multiplicative approximations and the hypervolume indicator. Proc. of 2009 Genetic and Evolutionary Computation Conference (2009) 571-578.
8. Hughes, E. J. Evolutionary many-objective optimization: Many once or one many?. Proc. of 2005 IEEE Congress on Evolutionary Computation (2005) 222-227. (Pubitemid 41495859)
9. Ishibuchi, H., Hitotsuyanagi, Y., and Nojima, Y. Many-objective test problems to visually examine the behavior of multi-objective evolution in a decision space. Lecture Notes in Computer Science, Vol. 6239: PPSN XI, Springer, Berlin (2010) 91-100.
10. Ishibuchi, H., Tsukamoto, N., Hitotsuyanagi, Y., and Nojima, Y. Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems. Proc. of 2008 Genetic and Evolutionary Computation Conference (2008) 649-656.
11. Khara, V., Yao, X., and Deb, K. Performance scaling of multi-objective evolutionary algorithms. Lecture Notes in Computer Science, vol. 2632: Evolutionary Multi-Criterion Optimization - EMO 2003, Springer, Berlin (2004) 367-390.
12. Köppen, M. and Yoshida, K. Substitute distance assignments in NSGA-II for handling many-objective optimization problems. Lecture Notes in Computer Science, vol. 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 727-741. (Pubitemid 350270922)
13. Kramer, O. and Danielsiek, H. DBSCAN-based multi-objective niching to approximate equivalent Pareto-subsets. Proc. of 2010 Genetic and Evolutionary Computation Conference (2010) 503-510.
14. Purshouse, R. C. and Fleming, P. J. On the evolutionary optimization of many conflicting objectives. IEEE Trans. on Evolutionary Computation 11, 6 (2007) 770-784. (Pubitemid 350187490)
15. Rudolph, G., Naujoks, B., and Preuss, M. Capabilities of EMOA to detect and preserve equivalent Pareto subsets. Lecture Notes in Computer Science, vol. 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 36-50. (Pubitemid 350270876)
16. Schutze, O., Lara, A., and Coello, C. A. C. On the influence of the number of objectives on the hardness of a multiobjective optimization problem. IEEE Trans. on Evolutionary Computation (to appear: online available from IEEE Xplore).
17. Singh, H., Isaacs, A., Ray, T., and Smith, W. A study on the performance of substitute distance based approaches for evolutionary many-objective optimization. Lecture Notes in Computer Science, Vol. 5361: SEAL 2008, Springer, Berlin, (2008) 411-420.
18. Tan, K. C., Khor, E. F., and Lee, T. H. Multiobjective Evolutionary Algorithms and Applications, Springer, Berlin (2005).
19. Ulrich, T., Bader, J., and Zitzler, E. Integrating decision space diversity into hypervolume-based multiobjective search. Proc. of 2010 Genetic and Evolutionary Computation Conference (2010) 455-462.
20. Wagner, T., Beume, N., and Naujoks, B. Pareto-, aggregation-, and indicator-based methods in many-objective optimization. Lecture Notes in Computer Science, vol. 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 742-756. (Pubitemid 350270923)
21. Zhang, Q. and Li, H. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation 11, 6 (2007) 712-731. (Pubitemid 350187486)
22. Zitzler, E., Brockhoff, D., and Thiele, L. The hypervolume indicator revisited: On the design of Pareto-compliant indicators via weighted integration. Lecture Notes in Computer Science, vol. 4403: Evolutionary Multi-Criterion Optimization - EMO 2007, Springer, Berlin (2007) 862-876. (Pubitemid 350270931)
23. Zitzler, E. and Künzli, S. Indicator-based selection in multiobjective search. Lecture Notes in Computer Science 3242: Parallel Problem Solving from Nature - PPSN VIII, Springer, Berlin (2004) 832-842. (Pubitemid 39750284)
24. Zitzler, E., Laumanns, M., and Thiele, L. SPEA2: Improving the strength Pareto evolutionary algorithm," TIK-Report 103, Computer Engineering and Networks Laboratory (TIK), ETH, Zurich (2001).
25. Zitzler, E. and Thiele, L. Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. on Evolutionary Computation 3, 4 (1999) 257-271. (Pubitemid 30544879)