Adaptation on the evolutionary time scale: A working hypothesis and basic experiments

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

Volumn 1363, Issue , 1998, Pages 251-262

  Salomon, Ralf ^a   Eggenberger, Peter ^a  

^a UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS;

FITNESS FUNCTIONS; SELECTION SCHEME; TIME-SCALES; VARIATION OPERATOR;

ARTIFICIAL INTELLIGENCE;

EID: 84957559616     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0026605     Document Type: Conference Paper

References (19)

1. Angeline, P., 1997, Tracking Extrema in Dynamic Environments, in: Proceedings of the Sixth Annual Conference on Evolutionary Programming 6, P.J, Angeline, R.G. Reynolds, J.R. McDonnell, and R. Eberhart (eds.) (Springer-Verlag), pp. 335-345.
2. Bäck, T., Schwefel, H.-P., 1993, An Overview of Evolutionary Algorithms for Parameter Optimization, Evolutionary Computation l(l):l-23, 1993.
3. De Jong, K.A., 1975, An Analysis of the Behavior of a Class of Genetic Adaptive Systems, Ph.D. Thesis (University of Michigan).
4. De Jong, K.A., 1993, Genetic Algorithms are NOT Function Optimizers, in: Foundations of Genetic Algorithms &, L.D. Whitley (ed.) (Morgan Kaufmann Publishers), pp. 5-17.
5. Fogel, D.B., 1995, Evolutionary Computation: Toward a New Philosophy of Machine Learning Intelligence, (IEEE Press).
6. Fogel, L.J., 1962, “Autonomous Automata”, Industrial Research, 4:14-19.
7. Goldberg, D.E., 1989, Genetic Algorithms in Search, Optimization and Machine Learning, (Addison-Wesley).
8. Holland, J., 1992, Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, (MIT Press).
9. McFarland, D., 1990, What it means for Robot Behaviour to be Adaptive, in: From Animals to Animats, Proceedings of the First International Conference on Simulation of Adaptive Behavior, J.-A. Meyer and S.W. Wilson (eds.) (MIT Press), pp. 22-28.
10. Menczer, F., Belew, R.K., Willuhn, W., 1995, Artificial life applied to adaptive information agents, in: AAAI Spring Symposium Series: Information gathering from heterogeneous, distributed environments. C. Knowblock, A. Levy, S-S. Chen, and G. Wiederhold (eds.) (AAAI).
11. Mühlenbein, H., Schlierkamp-Voosen, D., 1993, Predictive Models for the Breeder Genetic Algorithm I. Evolutionary Computation 1(1):25-50.
12. Rechenberg, I., 1973, Evolutions strategic. (Frommann-Holzboog).
13. Salomon, R., 1996, Reevaluating Genetic Algorithm Performance under Coordinate Rotation of Benchmark Functions, BioSystems 39(3):263-278.
14. Salomon, R., 1996. The Influence of Different Coding Schemes on the Computational Complexity of Genetic Algorithms in Function Optimization, in: Proceedings of The Fourth International Conference on Parallel Problem Solving from Nature (PPSN IV), H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel (eds.) (Springer-Verlag), pp. 227-235.
15. Schwefel, H.P., 1995, Evolution and Optimum Seeking (John Wiley and Sons).
16. Srinivas, M., Patnaik, L., 1994, Genetic Algorithms: A Survey, IEEE Computer, pp. 17-26.
17. Thierens, D., Goldberg, D., 1994, Convergence Models of Genetic Algorithm Selection Schemes, in: Proceedings of Parallel Problem. Solving from Nature 3, Y. Davidor, H.P. Schwefel, and R. Männer (eds.) (Springer-Verlag), pp. 119-129.
18. Vavak, F., Fogarty T.C., 1996, A Comparative Study of Steady State and Generational Genetic Algorithms, in: Evolutionary Computing: AISB Workshop, LNCS 1143, T.C. Fogarty (ed.) (Springer Verlag), pp. 297-304.
19. Volkenstein, M.V., 1994, Physical Approaches to Biological Evolution (Springer- Verlag).