Preference-driven co-evolutionary algorithms show promise for many-objective optimisation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6576 LNCS, Issue , 2011, Pages 136-150

  Purshouse, Robin C ^a   Jalbǎ, Cezar ^a   Fleming, Peter J ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

co evolution; comparative study; many objective optimisation

Indexed keywords

BEST-PRACTICES; CANDIDATE SOLUTION; CO-EVOLUTION; CO-EVOLUTIONARY ALGORITHM; CO-EVOLUTIONARY APPROACH; COMPARATIVE STUDY; DECISION MAKERS; EMPIRICAL ANALYSIS; HYPERVOLUME; MANY-OBJECTIVE OPTIMISATION; OBJECTIVE OPTIMISATION; OPTIMISATIONS; POSTERIORI DECISION; RANDOM SEARCHES; COMPARATIVE STUDIES; CONFLICTING OBJECTIVES; DECISION MAKER PREFERENCES; HYPERVOLUME INDICATORS;

DECISION MAKING; OPTIMIZATION; ECONOMIC AND SOCIAL EFFECTS; EVOLUTIONARY ALGORITHMS;

EVOLUTIONARY ALGORITHMS; OPTIMIZATION;

EID: 79953809063     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-19893-9_10     Document Type: Conference Paper

References (28)

1. Bentley, P.J., Wakefield, J.P.: Finding acceptable solutions in the Pareto-optimal range using multiobjective genetic algorithms. In: Chawdhry, P.K., Roy, R., Pant, R.K. (eds.) Soft Computing in Engineering Design and Manufacturing, pp. 231-240. Springer, Heidelberg (1997)
2. Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7(2), 174-188 (2003)
3. Brockhoff, D., Saxena, D.K., Deb, K., Zitzler, E.: On handling a large number of objectives a posteriori and during optimization. In: Knowles, J., Corne, D., Deb, K. (eds.) Multiobjective Problem Solving from Nature. Natural Computing Series, pp. 377-403. Springer, Heidelberg (2008)
4. Corne, D., Knowles, J.: Techniques for highly multiobjective optimisation: Some nondominated points are better than others. In: Proceedings of GECCO 2007, pp. 773-780 (2007)
5. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182-197 (2002)
6. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
7. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 1, pp. 825-830 (2002)
8. Fonseca, C.M., Fleming, P.J.: Multiobjective genetic algorithms made easy: Selection, sharing and mating restriction. In: Proceedings of the First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, pp. 42-52 (1995)
9. Fonseca, C.M., Fleming, P.J.: On the performance assessment and comparison of stochastic multiobjective optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.- M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584-593. Springer, Heidelberg (1996)
10. Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms - part I: A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 28(1), 26-37 (1998)
11. Fonseca, C.M., Paquete, L., López-Ibáñez, M.: An improved dimension-sweep algorithm for the hypervolume indicator. In: Proceedings of the 2006 Congress on Evolutionary Computation, pp. 1157-1163 (2006)
12. Goldberg, D.: The race, the hurdle and the sweet spot. In: Bentley, P. (ed.) Evolutionary Design by Computers, pp. 105-118. Morgan Kaufmann, San Francisco (1999)
13. Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods, 2nd edn. Wiley, New York (1999)
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)
15. Hughes, E.J.: MSOPS-II: A general-purpose many-objective optimiser. In: Proceedings of the 2007 Congress on Evolutionary Computation, pp. 3944-3951 (2007)
16. Hughes, E.J.: Fitness assignment methods for many-objective problems. In: Knowles, J., Corne, D., Deb, K. (eds.) Multiobjective Problem Solving from Nature. Natural Computing Series, pp. 307-329. Springer, Heidelberg (2008)
17. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: Proceedings of the 2008 Congress on Evolutionary Computation, pp. 2419-2426 (2008)
18. Kleeman, M.P., Lamont, G.B.: Coevolutionary multi-objective EAs: The next frontier? In: Proceedings of the 2006 Congress on Evolutionary Computation, pp. 1726-1735 (2006)
19. Lohn, J.D., Kraus, W.F., Haith, G.L.: Comparing a coevolutionary genetic algorithm for multiobjective optimization. In: Proceedings of the 2002 Congress on Evolutionary Computation, pp. 1157-1162 (2002)
20. Manly, B.F.J.: Randomization and Monte Carlo Methods in Biology. Chapman and Hall, Boca Raton (1991)
21. Purshouse, R.C., Fleming, P.J.: An adaptive divide-and-conquer methodology for evolutionary multi-criterion optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 133-147. Springer, Heidelberg (2003)
22. Purshouse, R.C., Fleming, P.J.: Conflict, harmony, and independence: Relationships in evolutionary multi-criterion optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 16-30. Springer, Heidelberg (2003)
23. Purshouse, R.C., Fleming, P.J.: On the evolutionary optimization of many conflicting objectives. IEEE Transactions on Evolutionary Computation 11(6), 770-784 (2007)
24. Rachmawati, L., Srinivasan, D.: Preference incorporation in multi-objective evolutionary algorithms: A survey. In: Proceedings of the 2006 Congress on Evolutionary Computation, pp. 962-968 (2006)
25. Wagner, T., Beume, N., Naujoks, B.: Pareto-, aggregation-, and indicator-based methods in many-objective optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 742-756. Springer, Heidelberg (2007)
26. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173-195 (2000)
27. Zitzler, E., Knowles, J., Thiele, L.: Quality assessment of pareto set approximations. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 373-404. Springer, Heidelberg (2008)
28. 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)