The anticipated mean shift and cluster registration in mixture-based EDAs for multi-objective optimization

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 351-358

  Bosman, Peter A N ^a  

^a CWI   (Netherlands)

Author keywords

Anticipation; Estimation of distribution algorithms; Mixture distribution; Multi Objective optimization

Indexed keywords

ANTICIPATION; BENCH-MARK PROBLEMS; CLUSTERING TECHNIQUES; DISTRIBUTION PARAMETERS; ESTIMATION OF DISTRIBUTION ALGORITHMS; GAUSSIANS; MEAN SHIFT; MIXTURE DISTRIBUTION; MIXTURE-MODELLING; MULTI OBJECTIVE; OBJECTIVE SPACE; OPTIMIZERS; OVERFITTING;

CONVERGENCE OF NUMERICAL METHODS; ESTIMATION; EVOLUTIONARY ALGORITHMS; MIXTURES; VECTOR QUANTIZATION;

MULTIOBJECTIVE OPTIMIZATION;

EID: 77955893229     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830549     Document Type: Conference Paper

References (13)

1. P. A. N. Bosman. On empirical memory design, faster selection of Bayesian factorizations and parameter-free Gaussian EDAs. In G. Raidi et al., editors, Proc. of the Genetic and Evolutionary Comp. Conf. - GECCO-2009, pages 389-396, New York, New York, 2009. ACM Press.
2. P. A. N. Bosman andD. Thierens. Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms. International J. of Approx. Reasoning, 31(3):259-289, 2002.
3. P. A. N. Bosman andD. Thierens. Adaptive variance scaling in continuous multi-objective estimation-of-distribution algorithms. In D. Thierens et al., editors, Proc. of the Genetic and Evol. Comp. Conf. - GECCO-2007, pages 500-507, New York, New York, 2007. ACM Press.
4. C. A. Coello Coello, G. B. Lamont, andD. A. Van Veldhuizen. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer-Verlag, Berlin, 2007.
5. M. Gallagher andM. Frean. Population-based continuous optimization, probabilistic modelling and mean shift. Evolutionary Computation, 13(1):29-42, 2005.
6. C. González, J. A. Lozano, andP. Larrañaga. Mathematical modelling of UMDAc algorithm with tournament selection, behaviour on linear and quadratic functions. International Journal of Approximate Reasoning, 31(3):313-340, 2002.
7. J. Grahl, S. Minner, andF. Rothlauf. Behaviour of UMDAc with truncation selection on monotonous functions. In D. Corne et al., editors, Proceedings of the IEEE Congress on Evolutionary Computation - CEC-2005, pages 2553-2559, Piscataway, New Jersey, 2005. IEEE Press.
8. N. Hansen. The CMA evolution strategy: a comparing review. In J. A. Lozano et al., editors, Towards a New Evolutionary Computation. Advances in Estimation of Distribution Algorithms. Springer-Verlag, Berlin, 2006.
9. K. Harada, J. Sakuma, andS. Kobayashi. Local search for multiobjective function optimization: Pareto descend method. In M. Keijzer et al., editors, Proc. of the Genetic and Evolutionary Computation Conf. - GECCO-2006, pages 659-666, New York, New York, 2006. ACM Press.
10. T. Hiroyasu, M. Nishioka, M. Miki, andH. Yokouchi. Discussion of search strategy for multi-objective genetic algorithm with consideration of accuracy and broadness of Pareto optimal solutions. In X. Li et al., editors, Simulated Evolution and Learning - SEAL-2008, pages 339-348, Berlin, 2008. Springer-Verlag.
11. C. Igel, N. Hansen, and S. Roth. Covariance matrix adaptation for multi-objective optimization. Evolutionary Computation, 15(1):l-28, 2007.
12. M. Pelikan, K. Sastry, andD. E. Goldberg. Multiobjective hBOA, clustering and scalability. In H.-G. Beyer et al., editors, Proceedings of the Genetic and Evolutionary Computation Conference - GECCO-2005, pages 663-670, New York, New York, 2005. ACM Press.
13. E. Zitzler, K. Deb, andL. Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2):173-195, 2000.