Initial approaches to the application of islands-based parallel EDAs in continuous domains

Journal of Parallel and Distributed Computing

Volumn 66, Issue 8, 2006, Pages 991-1001

  delaOssa, Luis ^a   Gámez, José A ^a   Puerta, José M ^a  

^a UNIVERSITY OF CASTILLA LA MANCHA   (Spain)

Author keywords

Estimation of distribution algorithms; Numerical optimization; Parallel evolutionary algorithms

Indexed keywords

MATHEMATICAL MODELS; OPTIMIZATION; POPULATION STATISTICS; PROBABILITY; PROBLEM SOLVING; SAMPLING;

ESTIMATION OF DISTRIBUTION ALGORITHMS; NUMERICAL OPTIMIZATION; PARALLEL EVOLUTIONARY ALGORITHMS;

EVOLUTIONARY ALGORITHMS;

EID: 33745684637     PISSN: 07437315     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpdc.2006.03.005     Document Type: Article

References (22)

1. C.W. Ahn, D. Goldberg, R.S. Ramakhrishna, Multiple-deme parallel estimation of distribution algorithms: basic framework and application, in: Proceedings of Parallel Processing and Applied Mathematics (PPAM 2003), Lecture Notes in Computer Science, vol. 2774, Springer-Verlag, 2004, pp. 544-551.
2. Alba E., and Troya J.M. A survey of parallel distributed genetic algorithms. Complexity 4 (1990) 303-346
3. E. Alba (Ed.), Parallel Metaheuristics: A New Class of Algorithms, Wiley-Interscience, New York, 2005.
4. P.A.N. Bosman, D. Thierens, IDEAs bases on the normal kernels probability density function, Technical Report UU-CS-2000-11, Utrech University, 2000.
5. Cantú-Paz E. Efficient and Accurate Parallel Genetic Algorithms (2001), Kluwer, Dordrecht
6. L. delaOssa, J.A. Gámez, J.M. Puerta, Migration of probability models instead of individuals: an alternative when applying the island model to edas, in: Proceedings of Parallel Problem Solving from Nature (PPSN VIII), Lecture Notes in Computer Science, Springer-Verlag, New York, NY, 2004, pp. 242-252.
7. L. delaOssa, J.A. Gámez, J.M. Puerta, Initial approaches to the application of islands-based parallel EDAs in continuous domains, in: Proceedings of the 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), Oslo, 2005, pp. 580-587.
8. t: a univariate EDA in continuous domains based on the triangular and TSP distributions, in: 10th Information Processing and Management of Uncertainty in Knowledge-Based System, vol. 2, Perugia, 2004, pp. 781-788.
9. D.E. Goldberg, M. Pelikan, S. Tsutsui, Evolutionary algorithm using marginal histogram models in continuous domains, Technical Report 2001019, IlliGAL. University of Illinois at Urbana-Champaign, 2001.
10. Hansen N., and Ostermaier A. Completely derandomized self-adaption in evolutionary strategies. Evol. Comput. 9 2 (2001) 159-195
11. Herrera F., Lozano M., and Verdegay J. Tackling real-coded genetic algorithms: operators and tools for the behaviour analysis. Artificial Intelligence Rev. 12 (1998) 265-319
12. T. Hiroyasu, M. Miki, M. Sano, H. Shimosaka, S. Tsutsui, J. Dongarra, Distributed probabilistic model-building genetic algorithm, in: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2003), Lecture Notes in Computer Science, vol. 3242, Springer-Verlag, New York, NY, 2003, pp. 1015-1028.
13. P. Larrañaga, R. Etxeberria, J.A. Lozano, J.M. Peña, Optimization by learning and simulation of Bayesian and Gaussian networks, Technical Report. EHU-KZAA-IK-4-99, University of the Basque Country, 1999.
14. P. Larrañaga, R. Etxeberria, J.A. Lozano, J.M. Peña, Optimization in continuous domains by learning and simulation of gaussian networks, in: Proceedings of the 2000 Genetic and Evolutionary Computation Conference Workshop Program, Las Vegas, 2000, pp. 201-204.
15. Larrañaga P., and Lozano J.A. Estimation of distribution algorithms. A New Tool for Evolutionary Computation (2002), Kluwer, Dordrecht
16. Madera J., Alba E., and Ochoa A. A parallel island model for estimation of distribution algorithms. In: Lozano J.A., and Larrañaga P. (Eds). Towards a New Evolutionary Computation. Advances in the Estimation of Distribution Algorithms, Studies in Fuzziness and Soft Computing (2005), Springer, Verlag, New York, NY
17. Mühlenbein H. The equation for response to selection and its use for prediction. Evol. Comput. 5 (1998) 303-346
18. Mühlenbein H., and Schlierkamp-Voosen D. Predictive models for the breeder genetic algorithm I. Continuous parameter optimization. Evol. Comput. 1 (1993) 25-49
19. J. Ocenasek, Parallel estimation of distribution algorithms, Ph.D. Thesis, Faculty of Information Technology, Brno University of Technology, 2002.
20. J. Ocenasek, J. Schwartz, M. Pelikan, Design of multithreaded estimation of distribution algorithms, in: Proceedings of Genetic and Evolutionary Computation (GECCO 2003), Lecture Notes in Computer Science, vol. 2723, Springer-Verlag, New York, NY, 2003, pp. 1247-1258.
21. Rubinstein R., and Melamed B. Modern Simulation and Modelling (1998), Wiley-Interscience, New York
22. M. Sebag, A. Ducoulombier, Extending population-based incremental learning to continuous search spaces, in: Proceedings of Parallel Problem Solving from Nature-PPSN V, Lecture Notes in Computer Science, vol. 1498, Springer-Verlag, New York, NY, 1998, pp. 418-427.