Parallel heterogeneous genetic algorithms for continuous optimization

Proceedings - International Parallel and Distributed Processing Symposium, IPDPS 2003

Volumn , Issue , 2003, Pages

  Alba, Enrique ^a   Luna, Francisco ^a   Nebro, Antonio J ^a  

^a Fac Sci Camp Univ Teatinos U   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DISTRIBUTED PARAMETER NETWORKS; GENETIC ALGORITHMS; TOPOLOGY;

ASYNCHRONOUS VERSION; CONTINUOUS OPTIMIZATION; CROSSOVER OPERATOR; EFFICIENT GENETIC ALGORITHMS; EXPLORATION AND EXPLOITATION; ORIGINAL MODEL; PARALLELIZATIONS; SUB-POPULATIONS;

OPTIMIZATION;

EID: 84947285578     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IPDPS.2003.1213281     Document Type: Conference Paper

References (14)

1. E. Alba and M. Tomassini. Parallelism and evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 6(5):443-462, October 2002.
2. E. Alba and J. M. Troya. Analyzing synchronous and asynchronous parallel distributed genetic algorithms. FGCS, 17:451-465, January 2001.
3. J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In J. J. Grefenstette, editor, Proc. of 2nd Int. Conf. Genetic Algorithms Appl., pages 14-21, Hillsdale, NJ, 1987. Lawrence Erlbaum.
4. E. Cantú-Paz. A summary of research on parallel genetic algorithms. Technical Report 95007, Univ. Illinois, Urbana-Champaign, Illinois GA Laboratory, 1995.
5. L. J. Eshelman, K. E. Mathias, and J. D. Schaffer. Convergence controlled variation. In R. Belew and M. Vose, editors, Foundations of Genetic Algorithms 4, pages 203-224, San Mateo, CA, 1997. Morgan Kaufmann.
6. D. E. Goldberg, H. Kargupta, J. Horn, and E. Cantú-Paz. Critical deme size for serial and parallel genetic algorithms. Technical Report 95002, Univ. Illinois, Urbana-Campaign, Illinois Genetic Algorithms Laboratory, 1995.
7. F. Herrera and M. Lozano. Heterogeneous distributed genetic algorithms based on the crossover operator. In 2nd IEE/IEEE Int. Conf. Genetic Algorithms Eng. Syst. : Innovations Appl., pages 203-208, 1997.
8. F. Herrera and M. Lozano. Gradual distributed real-coded genetic algorithms. IEEE Transactions on Evolutionary Computation, 4(1):43-63, 2000.
9. F. Herrera, M. Lozano, and J. L. Verdegay. Fuzzy connective based crossover operators to model genetic algorithms population diversity. Technical Report DECSAI-95110, University of Granada, 18071 Granada, Spain, March 1995.
10. K. A. D. Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, Univ. Michigan, Ann Arbor, 1975.
11. Z. Michalewicz. Genetic Algorithms+Data Structures=Evolution Programs. Springer, Berlin, Germany, 1992.
12. A. J. Nebro, E. Alba, F. Luna, and J. M. Troya. NET as a platform for implementing concurrent objects. In B. Monien and R. Feldmann, editors, 8th Euro-Par, pages 125-130, Paderborn, GE, August 2002. Springer-Verlag.
13. R. Storn and K. Price. Differential evolution-A simple efficient adaptive scheme for global optimization over continuous spaces. Technical Report 95-012, Int. Compt. Sci. Inst., Berkeley, CA, 1995.
14. S. Tsutsui and Y. Fujimoto. Forking genetic algorithm with blocking and shrinking modes. In S. Forrest, editor, Proc. 5th Int. Conf. Genetic Algorithms, pages 206-213, San Mateo, CA, 1993. Morgan Kaufmann.