Optimum tracking with evolution strategies

Evolutionary Computation

Volumn 14, Issue 3, 2006, Pages 291-308

  Arnold, Dirk V ^a   Beyer, Hans Georg ^b  

^a DALHOUSIE UNIVERSITY   (Canada)

^b VORARLBERG UNIVERSITY OF APPLIED SCIENCES   (Austria)

Author keywords

Cumulative step length adaptation; Dynamic optimization; Evolution strategies; Genetic and evolutionary computation; Tracking problem

Indexed keywords

ADAPTATION; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; EVOLUTION; MUTATION; POPULATION GENETICS;

ADAPTATION, BIOLOGICAL; ALGORITHMS; EVOLUTION; GENETICS, POPULATION; MODELS, GENETIC; MUTATION;

EID: 33750584276     PISSN: 10636560     EISSN: 15309304     Source Type: Journal    
DOI: 10.1162/evco.2006.14.3.291     Document Type: Article

References (30)

1. Angeline, P. J. (1997). Tracking extrema in dynamic environments. In Angeline, P. J. et al., editors, Proc. of the Sixth International Conference on Evolutionary Programming - EP'97, pages 335-345. Springer Verlag, Heidelberg.
2. Arnold, D. V. (2002). Noisy Optimization with Evolution Strategies. Genetic Algorithms and Evolutionary Computation Series. Kluwer Academic Publishers, Boston, MA.
3. Arnold, D. V. and Beyer, H.-G. (2002). Random dynamics optimum tracking with evolution strategies. In Merelo, J. J. et al, editors, Parallel Problem Solving from Nature. - PPSN VII, pages 3-12. Springer Verlag, Heidelberg.
4. Arnold, D. V. and Beyer, H.-G. (2004). Performance analysis of evolutionary optimization with cumulative step length adaptation. IEEE Transactions on Automatic Control, 49(4):617-622.
5. Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.
6. Bäck, T. (1998). On the behavior of evolutionary algorithms in dynamic environments. In Proc. of the 1998 International Conference on Evolutionary Computation, pages 446-451. IEEE Press, Piscataway, NJ.
7. Beyer, H.-G. (2001). The Theory of Evolution Strategies. Natural Computing Series. Springer Verlag, Heidelberg.
8. Beyer, H.-G. and Schwefel, H.-P. (2002). Evolution strategies - A comprehensive introduction. Natural Computing, 1(1):3-52.
9. Beyer, H.-G., Schwefel, H.-P., and Wegener, I. (2002). How to analyze evolutionary algorithms. Theoretical Computer Science, 287(1):101-130.
10. Branke, J. (2001). Evolutionary Optimization in Dynamic Environments. Genetic Algorithms and Evolutionary Computation Series. Kluwer Academic Publishers, Boston, MA.
11. Bürger, R. (2000). The Mathematical Theory of Selection, Recombination, and Mutation. John Wiley & Sons, Chichester.
12. Droste, S. (2002). Analysis of the (1 + 1) EA for a dynamically changing objective function. In Proc. of the 2002 Congress on Evolutionary Computation. IEEE Press, Piscataway, NJ.
13. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading, MA.
14. Hansen, N. (1998). Verallgemeinerte individuelle Schrittweitenregelung in der Evolutionsstrategie. Mensch & Buch Verlag, Berlin.
15. Hansen, N. and Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159-195.
16. Herdy, M. (1992). Reproductive isolation as a strategy parameter in hierarchically organized evolution strategies. In Männer, R. and Manderick, B., editors, Parallel Problem Solving from Nature -PPSN II, pages 207-217. Elsevier, Amsterdam.
17. Jansen, T. and Schellbach, U. (2005). Theoretical analysis of a mutation-based evolutionary algorithm for a tracking problem in the lattice. In Beyer, H.-G. et al., editors, Proc. of the Genetic and Evolutionary Computation Conference - GECCO 2005, pages 841-848. ACM Press, New York.
18. Jin, Y and Branke, J. (2005). Evolutionary optimization in uncertain environments - A survey. IEEE Transactions on Evolutionary Computation, 9(3):303-317.
19. Michalski, R. S. (2000). Learnable evolution model: Evolutionary processes guided by machine learning. Machine Learning, 38(1-2):9-40.
20. Mitchell, M. (1996). An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA.
21. Morrison, R. W. (2004). Designing Evolutionary Algorithms for Dynamic Environments. Natural Computing Series. Springer Verlag, Heidelberg.
22. Ostermeier, A., Gawelczyk, A., and Hansen, N. (1994). Step-size adaptation based on non-local use of selection information. In Davidor, Y., Schwefel, H.-P., and Männer, R., editors, Parallel Problem Solving from Nature -PPSN III, pages 189-198. Springer Verlag, Heidelberg.
23. Ravisé, C. and Sebag, M. (1996). An advanced evolution should not repeat its past errors. In Saitta, L., editor, Proc. of the 13th International Conference on Machine Learning, pages 400-408. Morgan Kaufmann Publishers, San Francisco, CA.
24. Rechenberg, I. (1994). Evolutionsstrategie '94. Frommann-Holzboog Verlag, Stuttgart.
25. Salomon, R. and Eggenberger, P. (1997). Adaptation on the evolutionary time scale: A working hypothesis and basic experiments. In Hao, J.-K. et al., editors, Proc. of the Third Conference on Artificial Evolution - EA'97, pages 251-262. Springer Verlag, Heidelberg.
26. Schwefel, H.-P. (1995). Evolution and Optimum Seeking. Wiley, New York.
27. Sebag, M., Schoenauer, M., and Ravisé, C. (1997). Inductive learning of mutation step-size in evolutionary parameter optimization. In Angeline, P. J. et al., editors, Proc. of the Sixth International Conference on Evolutionary Programming - EP'97, pages 247-261. Springer Verlag, Heidelberg.
28. van Nimwegen, E. and Crutchfield, J. P. (2001). Optimizing epochal evolutionary search: Population-size dependent theory. Machine Learning, 45(1):77-114.
29. Weicker, K. (2004). An empirical investigation of optimum tracking with evolution strategies. In Proc. of the 9th Online World Conference on Soft Computing and Industrial Applications - WSC9. Springer Verlag, Heidelberg.
30. Weicker, K. and Weicker, N. (1999). On evolution strategy optimization in dynamic environments. In Proc. of the 1999 Congress on Evolutionary Computation, pages 2039-2046. IEEE Press, Piscataway, NJ.