Approximation-guided evolutionary multi-objective optimization

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1198-1203

  Bringmann, Karl ^a   Friedrich, Tobias ^a   Neumann, Frank ^b   Wagner, Markus ^b  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^b UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; HEURISTIC APPROACH; MULTI-OBJECTIVE OPTIMIZATION PROBLEM; MULTI-OBJECTIVE PROBLEM; OBJECTIVE SPACE;

ARTIFICIAL INTELLIGENCE; EVOLUTIONARY ALGORITHMS; HEURISTIC METHODS; MULTIOBJECTIVE OPTIMIZATION;

APPROXIMATION ALGORITHMS;

EID: 84866871315     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-204     Document Type: Conference Paper

References (20)

1. R. B. Agrawal and K. Deb. Simulated binary crossover for continuous search space. Technical report, 1994.
2. N. Beume, B. Naujoks, and M. Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653-1669, 2007. (Pubitemid 46498552)
3. K. Bringmann and T. Friedrich. Approximating the volume of unions and intersections of high-dimensional geometric objects. Computational Geometry: Theory and Applications, 43:601-610, 2010.
4. K. Bringmann and T. Friedrich. The maximum hypervolume set yields near-optimal approximation. In Proc. 12th annual Conference on Genetic and Evolutionary Computation (GECCO '10), pages 511-518. ACM Press, 2010.
5. K. Bringmann and T. Friedrich. Tight bounds for the approximation ratio of the hypervolume indicator. In Proc. 11th International Conference Parallel Problem Solving from Nature (PPSN XI), volume 6238 of LNCS, pages 607-616. Springer, 2010.
6. C. Daskalakis, I. Diakonikolas, and M. Yannakakis. How good is the Chord algorithm? In Proc. 21st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA '10), pages 978-991, 2010.
7. K. Deb. Multi-objective optimization using evolutionary algorithms. Wiley, Chichester, UK, 2001.
8. K. Deb, A. Pratap, S. Agrawal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evolutionary Computation, 6(2):182-197, 2002. (Pubitemid 34555372)
9. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable test problems for evolutionary multiobjective optimization. In Evolutionary Multiobjective Optimization, Advanced Information and Knowledge Processing, pages 105-145. 2005.
10. I. Diakonikolas and M. Yannakakis. Small approximate Pareto sets for biobjective shortest paths and other problems. SIAM Journal on Computing, 39:1340-1371, 2009.
11. J. J. Durillo, A. J. Nebro, and E. Alba. The jMetal framework for multi-objective optimization: Design and architecture. In Proc. Congress on Evolutionary Computation (CEC '10), pages 4138-4325. IEEE Press, 2010.
12. M. Ehrgott. Multicriteria optimization. Berlin, Springer, 2nd edition, 2005.
13. M. T. M. Emmerich, N. Beume, and B. Naujoks. An EMO algorithm using the hypervolume measure as selection criterion. In Proc. Third International Conference on Evolutionary Multi-Criterion Optimization (EMO '05), pages 62-76. Springer, 2005.
14. M. Gong, L. Jiao, H. Du, and L. Bo. Multiobjective immune algorithm with nondominated neighbor-based selection. Evolutionary Computation, 16(2):225-255, 2008.
15. C. Igel, N. Hansen, and S. Roth. Covariance matrix adaptation for multi-objective optimization. Evolutionary Computation, 15:1-28, 2007.
16. M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 10(3):263-282, 2002.
17. C. H. Papadimitriou and M. Yannakakis. On the approximability of trade-offs and optimal access of web sources. In Proc. 41st annual Symposium on Foundations of Computer Science (FOCS '00), pages 86-92. IEEE Press, 2000.
18. E. Zitzler and S. Künzli. Indicator-based selection in multiobjective search. In Proc. 8th International Conference Parallel Problem Solving from Nature (PPSN VIII), volume 3242 of LNCS, pages 832-842. Springer, 2004.
19. E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In Proc. Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95-100, 2002.
20. E. Zitzler and L. Thiele. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evolutionary Computation, 3:257-271, 1999. (Pubitemid 30544879)