Variable-size memory evolutionary algorithm to deal with dynamic environments

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

Volumn 4448 LNCS, Issue , 2007, Pages 617-626

  Simões, Anabela ^a,b   Costa, Ernesto ^b  

^a POLYTECHNIC INSTITUTE OF COIMBRA   (Portugal)

^b UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STORAGE EQUIPMENT; DYNAMICAL SYSTEMS; PROBLEM SOLVING;

DYNAMIC ENVIRONMENTS; GENETIC OPERATOR; SEARCH POPULATION;

EVOLUTIONARY ALGORITHMS;

EID: 34548077354     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71805-5_68     Document Type: Conference Paper

References (22)

1. J. Branke. Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. Proc. of the 1999 IEEE Congress on Evolutionary Computation, pp. 1875-1882, 1999.
2. J. Branke. Evolutionary Optimization in Dynamic Environments. Norwell MA: Kluwer, 2001.
3. J. Branke, T. Kaußler, C. Schmidt and H. Schmeck. A Multi-Population Approach to Dynamic Optimization Problems. Adaptive Computing in Design and Manufacture (ACDM 2000), pp. 299-308, 2000.
4. H. Cobb. An Investigation into the Use of Hypermutation as an Adaptive Operator in Genetic Algorithms Having Continuous, Time-Dependent Non-Stationary Environments. Technical Report AIC-90-001, 1990.
5. nd Int. Conference, on Genetic Algorithms, pp. 59-68, 1987.
6. nd Int. Conference Parallel Problem Solving from Nature 2, pp. 137-144, North-Holland, 1992.
7. I. Harvey. The Microbial Genetic Algorithm. Unpublished, 1996.
8. W. Liles and K. De Jong. The Usefulness of Tag Bits in Changing Environments. Proceedings of 1999 IEEE Congress on Evolutionary Computation, pp. 2054-2060, 1999.
9. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. 3rd Edition Springer-Verlag, 1999.
10. R. W. Morrison and K. De Jong. Triggered Hypermutation Revisited. Proc. of the 1999 IEEE Congress on Evolutionary Computation, pp. 1025-1032, 1999.
11. th Int. Conference on Genetic Algorithms, pp. 159-166, 1995.
12. P. J. Russell. Genetics. 5th edition, Addison-Wesley, 1998.
13. A. Sima Uyar and A. Emre Harmanci. A New Population Based Adaptive Domination Change Mechanism for Diploid Genetic Algorithms in Dynamic Environments. Soft Computing, vol. 9, pp. 803-814, 2005.
14. th Int. Conf. on Artificial Neural Networks, pp. 612- 19, 1999.
15. th Int. Conference on Artificial Neural Networks, pp. 168-174, 2003.
16. A. Simões and E. Costa. Variable-size Memory Evolutionary Algorithm to Deal with Dynamic Environments: an empirical study. CISUC Technical Report, TR 2006/004 - ISSN 0874-338X, November 2006.
17. P. Smith. Conjugation: A Bacterially Inspired Form of Genetic. Late Breaking Papers at the Genetic Programming 1996 Conference, 1996.
18. M. Wineberg and F. Oppacher. Enhancing GA's Ability to Cope with Dynamic Environments. Proc. of the 2000 Genetic and Evolutionary Comput. Conf, pp. 3-10, 2000.
19. 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, 2006.
20. S. Yang. Associative Memory Scheme for Genetic Algorithms in Dynamic Environments. Proc. of the EvoWorkshops 2006, LNCS 3097, pp. 788-799, 2006.
21. 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.
22. S. Yang. Memory-Based Immigrants for Genetic Algorithms in Dynamic Environments. Proc. of the 2005 Genetic and Evolutionary Computation Conference, vol. 2, pp. 1115-1122, 2005.