MOPED: A multi-objective Parzen-based estimation of distribution algorithm for continuous problems

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

Volumn 2632, Issue , 2003, Pages 282-294

  Costa, Mario ^a   Minisci, Edmondo ^a  

^a POLITECNICO DI TORINO   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; OPTIMIZATION; PROBABILITY DENSITY FUNCTION;

CONTINUOUS PROBLEMS; ESTIMATION OF DISTRIBUTION ALGORITHMS; EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; NON-DOMINATED SORTING GENETIC ALGORITHM - II; OBJECTIVE FUNCTIONS; OPTIMIZATION PROCEDURES; PROBABILITY DENSITIES; SPREADING TECHNIQUES;

EVOLUTIONARY ALGORITHMS;

EID: 35248812497     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36970-8_20     Document Type: Article

References (16)

1. Mühlenbein, H., The equation for the response to selection and its use for prediction, Evolutionary Computation 5(3), pp. 303-346, 1998.
2. Baluja, S., Population based incremental learning: A method for integrating genetic search based function optimization and competitive learning (Tech. Rep. No. CMU-CS-94-163). Pittsburgh, PA: Carnegie Mellon University, 1994.
3. Harik, G. R., Lobo, F. G., and Goldberg, D. E., The compact genetic algorithm, In Proceedings of the International Conference on Evolutionary Computation 1998, pp. 523-528, Piscataway, NJ: IEEE Service Center, 1998.
4. Sebag, M., Ducoulombier, A., Extending population-based incremental learning to continuous search spaces, In Parallel Problem Solving from Nature PPSN V, pp. 418-427, Berlin: Springer Verlag, 1998
5. De Bonet, J. S., Isbell, C. L., and Viola, P., MIMIC: Finding optima by estimating probability densities. In Mozer, M. C., Jordan, M. I., & Petsche, T. (Eds.), Advances in Neural Information Processing Systems, Vol. 9, pp. 424, The MIT Press, Cambridge, 1997.
6. Baluja, S., Davies, S., Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. In Proceedings of the 14th International Conference on Machine Learning, pp. 30-38, Morgan Kaufmann, 1997.
7. Pelikan, M., Mühlenbein, H., The bivariate marginal distribution algorithm. In Roy, R., Furuhashi, T., & Chawdhry, P. K. (Eds.), Advances in Soft Computing Engineering Design and Manufacturing, pp. 521-535, London: Springer-Verlag, 1999.
8. Mühlenbein, H., Mahnig, T., The Factorized Distribution Algorithm for Additively Decomposed Functions, Proceedings of the 1999 Congress on Evolutionary Computation, pp. 752-759, 1999,
9. Pelikan, M., Goldberg, D. E., and Cant'u-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In Banzhaf, W., Daida, J., Eiben, A. E., Garzon, M. H., Honavar, V., Jakiela, M., & Smith, R. E. (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, Vol. I, pp. 525-532. Orlando, FL, Morgan Kaufmann Publishers, San Francisco, CA, 1999.
10. Bosman, P.A.N., Thierens, D., Expanding from discrete to continuous estimation of distribution algorithms: The IDEA, in M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J.J. Merelo, and H.-P. Schwefel, eds., Parallel Problem Solving from Nature, pp 767-776, Springer, 2000.
11. Thierens, D., Bosman, P.A.N., Multi-Objective Mixture-based Iterated Density Estimation Evolutionary Algorithms L. Spector, E.D. Goodman, A. Wu, W.B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M.H. Garzon and E. Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2001, pages 663-670, Morgan Kaufmann Publishers, 2001.
12. Khan, N., Goldberg, D.E., and Pelikan, M., Multi-Objective Bayesian Optimization Algorithm, IlliGAL Report No. 2002009, March 2002.
13. Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., A fast and elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, April 2002.
14. Parzen, E., On Estimation of a Probability Density Function and Mode, Ann. Math. Stat., Vol. 33, pp. 1065-1076, 1962.
15. Cacoullos, T., Estimation of a Multivariate Density, Ann. Inst. Stat. Math., Vol. 18, pp. 179-189, 1966.
16. Deb, K., Multi-Objective Genetic Algorithm: Problem Difficulties and Construction of Test Problems, Evolutionary Computation, Vol. 7, No. 3, pp. 205-230, The MIT Press, 1999.