Exploiting gradient information in numerical multi-objective evolutionary optimization

GECCO 2005 - Genetic and Evolutionary Computation Conference

Volumn , Issue , 2005, Pages 755-762

  Bosman, Peter A N ^a   De Jong, Edwin D ^b  

^a CWI   (Netherlands)

^b UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Evolutionary algorithms; Gradients; Memetic algorithms; Multi objective optimization; Numerical optimization

Indexed keywords

BENCHMARKING; INFORMATION USE; MATHEMATICAL OPERATORS; MULTI AGENT SYSTEMS; NUMERICAL METHODS; OPTIMIZATION; PROBLEM SOLVING;

MEMETIC ALGORITHMS; MULTI-OBJECTIVE OPTIMIZATION; NUMERICAL OPTIMIZATION;

EVOLUTIONARY ALGORITHMS;

EID: 32444435022     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1068009.1068138     Document Type: Conference Paper

References (22)

1. P. A. N. Bosman and D. Thierens, Exploiting gradient information in continuous iterated density estimation evolutionary algorithms. In B. Kröse et al., editors, Proceedings of the 13th Belgium-Netherlands Artificial Intelligence Conference BNAIC'01, pages 69-76, 2001.
2. P. A. N. Bosman and D. Thierens, The balance between proximity and diversity in multi-objective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 7:174-188, 2003.
3. P. A. N. Bosman and D. Thierens. The naive MIDEA: a baseline multi-objective EA. In C. A. Coello Coello et al., editors, Evolutionary Multi-Criterion Optimization - EMO'05, pages 428-442, Berlin, 2005. Springer-Verlag.
4. M. Brown and R. E. Smith. Effective use of directional information in multi-objective evolutionary computation. In E. Cantú-Paz et al., editors, Proceedings of the GECCO-2003 Genetic and Evolutionary Computation Conference, pages 778-789, Berlin, 2003. Springer-Verlag.
5. K. Deb. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation, 7(3):205-230, 1999.
6. M. Dellnitz, O. Schütze, and T. Hestermeyer, Covering pareto sets by multilevel subdivision techniques. J. of Optimization Theory and Applications, 124(1):113-136, 2005.
7. J. Fliege and B. F. Svaiter. Steepest descent methods for multicriteria optimization. Mathematical Methods of Operations Research, 51(3):479-494, 2000.
8. M.R. Hestenes and E. Stiefel. Methods of conjugate gradients for solving linear systems. J. Res. Nat. Bur. Standards, 6:409-436, 1952.
9. H. Ishibuchi, T. Yoshida, and T. Murata. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans. on Evolutionary Computation, 7:204-223, 2003.
10. J. D. Knowles and D. Corne. M-PAES: a memetic algorithm for multiobjective optimization. In Proceedings of the 2000 Congress on Evolutionary Computation - CEC-2000, pages 325-332, Piscataway, New Jersey, 2000. IEEE Press.
11. J. D. Knowles and D. Corne. On metrics for comparing non-dominated sets. In Proceedings of the 2002 Congress on Evol. Comp. CEC 2002, pages 666-674, Piscataway, New Jersey, 2002. IEEE Press.
12. J. D. Knowles and D. W. Corne. A comparison of diverse approaches to memetic multiobjective combinatorial optimization. In W. Hart et al., editors, Proceedings of the Workshop on Memetic Algorithms WOMA at the Genetic and Evolutionary Computation Conference - GECCO-2000, pages 103-108, 2000.
13. M. Lahanas, D. Baltas, and S. Giannouli. Global convergence analysis of fast multiobjective gradient based dose optimization algorithms for high-dose-rate brachytherapy. Phys. Med. Biol., 48:599-617, 2003.
14. T. M. Mitchell. Machine Learning. McGraw-Hill, New York, New York, 1997.
15. P. Moscato, On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, 1989.
16. H. Mühlenbein and T. Mahnig. FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions. Evolutionary Computation, 7:353-376, 1999.
17. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. Numerical Recipes In C: The Art Of Scientific Computing. Cambridge University Press, Cambridge, 1992.
18. J. D. Schaffer. Multiple objective optimization with vector evaluated genetic algorithms. In J. J. Grefenstette, editor, Proceedings of the 1st International Conference on Genetic Algorithms, pages 93-100, Mahwah, New Jersey, 1985. Lawrence Erlbaum Associates, Inc.
19. S. Schäffler, R. Schultz, and K. Weinzierl. Stochastic method for the solution of unconstrained vector optimization problems. Journal of Optimization Theory and Applications, 114(1):209-222, 2002.
20. E.-G. Talbi, M. Rahoual, M.-H. Mabed, and C. Dhaenens. New genetic approach for multicriteria optimization problems: Application to the flow-shop. In E. Zitzler et al., editors, Evolutionary Multi-Criterion Optimization - EMO'01, Berlin, 2001. Springer-Verlag.
21. E. Zitzler, K. Deb, and L. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Computation, 8(2):173-195, 2000.
22. E. Zitzler, M. Laumanns, L. Thiele, C. M. Fonseca, and V. Grunert da Fonseca. Why quality assessment of multiobjective optimizers is difficult. In W. B. Langdon et al., editors, Proceedings of the GECCO-2002 Genetic and Evolutionary Computation Conference, pages 666-674, San Francisco, California, 2002, Morgan Kaufmann.