An analysis of dynamic severity and population size

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

Volumn 1917, Issue , 2000, Pages 159-168

  Weicker, Karsten ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONS;

ALGORITHMIC TECHNIQUES; DIFFERENT CLASS; DYNAMIC PROBLEM; DYNAMIC SEVERITY; FITNESS FUNCTIONS; MATHEMATICAL FRAMEWORKS; POPULATION SIZES; PROBLEM PROPERTIES;

POPULATION STATISTICS;

EID: 84947917443     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45356-3_16     Document Type: Conference Paper

References (18)

1. Branke, J. (1999a). Evolutionary approaches to dynamic optimization problems: A survey. In J. Branke & T. Back (Eds.), Evolutionary algorithms for dynamic optimization problems (pp. 134-137). (part of GECCO Workshops, A. Wu (ed.))
2. Branke, J. (1999b). Memory enhanced evolutionary algorithms for changing optimization problems. In 1999 Congress on Evolutionary Computation (pp. 1875-1882). Piscataway, NJ: IEEE Service Center.
3. Cobb, H. G. (1990). An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments (Tech. Rep. No. 6760 (NLR Memorandum)). Washington, D.C.: Navy Center for Applied Research in Artificial Intelligence.
4. Cobb, H. G., k Grefenstette, J. J. (1993). Genetic algorithms for tracking changing environments. In S. Forrest (Ed.), Proc. of the Fifth Int. Conf. on Genetic Algorithms (pp. 523-530). San Mateo, CA: Morgan Kaufmann.
5. De Jong, K. A., k Spears, W. M. (1991). An analysis of the interacting roles of population size and crossover in genetic algorithms. In H.-P. Schwefel & R. Männer (Eds.), Parallel Problem Solving from Nature: 1st Workshop, PPSN 1 (pp. 3847). Berlin: Springer.
6. Deb, K., k Agrawal, S. (1999). Understanding interactions among genetic algorithm parameters. In W. Banzhaf k C. Reeves (Eds.), Foundations of Genetic Algorithms 5 (pp. 265-286). San Francisco, CA: Morgan Kaufmann.
7. Goldberg, D. E., k Smith, R. E. (1987). Nonstationary function optimization using genetic algorithms with dominance and diploidy. In J. J. Grefenstette (Ed.), Proc. of the Second Int. Conf. on Genetic Algorithms (pp. 59-68). Hillsdale, NJ: Lawrence Erlbaum Associates.
8. Grefenstette, J. J. (1999). Evolvability in dynamic fitness landscapes: a genetic algorithm approach. In 1999 Congress on Evolutionary Computation (pp. 2031-2038). Piscataway, NJ: IEEE Service Center.
9. Lewis, J., Hart, E., k Ritchie, G. (1998). A comparison of dominance mechanisms and simple mutation on non-stationary problems. In A. E. Eiben, T. Bäck, M. Schoenauer, k H.-P. Schwefel (Eds.), Parallel Problem Solving from Nature -PPSN V (pp. 139-148). Berlin: Springer. (Lecture Notes in Computer Science 1498)
10. Mahfoud, S. W. (1994). Population sizing for sharing methods (Tech. Rep. No. I11IGAL 94005). Urbana, IL: Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign.
11. Miller, B. L. (1997). Noise, sampling, and efficient genetic algorithms. Unpublished doctoral dissertation, University of Illinois at Urbana-Champaign, Urbana, IL. (I11IGAL Report No. 97001)
12. Mori, N., Imanishi, S., Kita, H., k Nishikawa, Y. (1997). Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm. In T. Bäck (Ed.), Proc. of the Seventh Int. Conf. on Genetic Algorithms (pp. 299-306). San Francisco, CA: Morgan Kaufmann.
13. Morrison, R. W., k De Jong, K. A. (1999). A test problem generator for non-stationary environments. In 1999 Congress on Evolutionary Computation (pp. 2047-2053). Piscataway, NJ: IEEE Service Center.
14. Ronnewinkel, C, Wilke, C. O., & Martinetz, T. (2000). Genetic algorithms in time-dependent environments. In L. Kallel, B. Naudts, k A. Rogers (Eds.), Theoretical aspects of evolutionary computing (pp. 263-288). Berlin: Springer.
15. Rowe, J. E. (1999). Finding attractors for periodic fitness functions. In W. Banzhaf, J. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, k R. E. Smith (Eds.), Proc. of the Genetic and Evolutionary Computation Conf. GECCO-99 (pp. 557-563). San Francisco, CA: Morgan Kaufmann.
16. Smith, R. E. (1997). Population size. In T. Bäck, D. B. Fogel, k Z. Michalewicz (Eds.), Handbook of Evolutionary Computation (pp. El.1:1-5). Bristol, New York: Institute of Physics Publishing and Oxford University Press.
17. Trojanowski, K., k Michalewicz, Z. (1999). Searching for optima in non-stationary environments. In 1999 Congress on Evolutionary Computation (pp. 1843-1850). Piscataway, NJ: IEEE Service Center.
18. Weicker, K., k Weicker, N. (1999). On evolution strategy optimization in dynamic environments. In 1999 Congress on Evolutionary Computation (pp. 2039-2046). Piscataway, NJ: IEEE Service Center.