Improving memory's usage in evolutionary algorithms for changing environments

2007 IEEE Congress on Evolutionary Computation, CEC 2007

Volumn , Issue , 2007, Pages 276-283

  Simoes A ^a   Costa, E ^b  

^a POLYTECHNIC INSTITUTE OF COIMBRA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

EXPLICIT MEMORY; SEARCH SPACE;

CONVERGENCE OF NUMERICAL METHODS; DATA STORAGE EQUIPMENT; PROBLEM SOLVING;

EVOLUTIONARY ALGORITHMS;

EID: 79955316751     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2007.4424482     Document Type: Conference Paper

References (16)

1. J. Branke. Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. Proc. of Congress on Evolutionary. Computation 1999, pp. 1875-1882, IEEE, 1999.
2. J. J. Grefenstette. Genetic Algorithms for Changing Environments. Proc. of the 2nd Int. Conf. Parallel Problem Solving from Nature 2, pp. 137-144, North-Holland, 1992.
3. I. Harvey. The Microbial Genetic Algorithm. Unpublished, 1996.
4. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. 3rd Edition Springer Verlag Berlin, 1999.
5. N. Mori, H. Kita, and Y. Nishikawa. Adaptation to a Changing Environment by Means of the Thermodynamical Genetic Algorithm. In H.-M. Voigt, editor, Parallel Problem Solving from Nature, 1141 in LNCS, pp. 513-522. SpringerVerlagBerlin, 1996.
6. P. J. Russell. Genetics. 5th edition, Addison-Wesley, 1998.
7. A. Simoes and E. Costa. On Biologically Inspired Genetic Operators: Transformation in the Standard Genetic Algorithm. In Spector, L., E. Goodman, A. Wu, W.B. Langdon, H.-M. Voigt, M. Gen, S. Sen, M. Dorigo, S. Pezeshk, M. Garzon, andE. Burke, (eds). 2001 Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001. pp. 584-591, San Francisco, USA, 7-11 July, CA: Morgan Kaufmann Publishers, 2001
8. A. Simoes and E. Costa. An Immune System-Based Genetic Algorithm to Deal with Dynamic Environments: Diversity and Memory. Proc. of the 6th Int. Conf. on Artificial Neural Networks, pp. 168-174, Roanne, France, 23-25 April, Springer, 2003.
9. A. Simoes and E. Costa. Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments. In M. Giacobini et al. (Eds.): EvoWorkshops 2007, Applications of Evolutionary Computing, LNCS 4448, pp. 617-626, SpringerVerlag, 2007.
10. A. Simoes and E. Costa. VMEA: Studies of the impact of different replacing strategies in the algorithm's performance an in the population's diversity when dealing with dynamic environments. CISUC Technical Report TR2007/001, ISSN 0874-338X, February 2007.
11. A. Simoes and E. Costa. Improving Memory-based Evolutionary Algorithms in Changing Environments. CISUC Technical Report TR2007/004, ISSN 0874-338X, March 2007.
12. P. Smith. Conjugation: A Bacterially Inspired Form of Genetic. Late Breaking Papers at the Genetic Programming 1996 Conf., Stanford Univ., July 28-31, 1996.
13. S. Yang and X. Yao. Experimental Study on Population-Based Incremental Learning Algorithms for Dynamic Optimization problems. Soft Computing, vol. 9, no 11, pp. 815-834, 2005.
14. S. Yang. Memory-Based Immigrants for Genetic Algorithms in Dynamic Environments. Proc. of the 2005 Genetic and Evolutionary ComputationConference, Vol. 2, pp. 1115-1122, ACMPress, 2005.
15. S. Yang. A Comparative Study of Immune System Based Genetic Algorithms in Dynamic Environments. Proc. of the 2006 Genetic and Evolutionary Computation Conference, pp. 1377-1384, ACM Press, 2006.
16. S. Yang. Associative Memory Scheme for Genetic Algorithms in Dynamic Environments. Applications of Evolutionary Computing, LNCS 3097, pp. 788-799, SpringerVerlagBerlin, 2006.