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A simple model of unbounded evolutionary versatility as a largest-scale trend in organismal evolution

Turney, Peter (2000) A simple model of unbounded evolutionary versatility as a largest-scale trend in organismal evolution. [Journal (Paginated)]

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Abstract

The idea that there are any large-scale trends in the evolution of biological organisms is highly controversial. It is commonly believed, for example, that there is a large-scale trend in evolution towards increasing complexity, but empirical and theoretical arguments undermine this belief. Natural selection results in organisms that are well adapted to their local environments, but it is not clear how local adaptation can produce a global trend. In this paper, I present a simple computational model, in which local adaptation to a randomly changing environment results in a global trend towards increasing evolutionary versatility. In this model, for evolutionary versatility to increase without bound, the environment must be highly dynamic. The model also shows that unbounded evolutionary versatility implies an accelerating evolutionary pace. I believe that unbounded increase in evolutionary versatility is a large-scale trend in evolution. I discuss some of the testable predictions about organismal evolution that are suggested by the model.

Item Type:Journal (Paginated)
Keywords:evolutionary trends, evolutionary progress, large-scale trends, evolutionary versatility, evolvability, Baldwin effect.
Subjects:Biology > Evolution
Biology > Theoretical Biology
ID Code:1799
Deposited By:Turney, Peter
Deposited On:13 Sep 2001
Last Modified:11 Mar 2011 08:54

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Aboitiz, F. (1991). Lineage selection and the capacity to evolve. Medical Hypotheses,

36, 155-156.

2. Altenberg, L. (1994). The evolution of evolvability in genetic programming. In:

Advances in Genetic Programming, K. E. Kinnear Jr., (ed.). MIT Press.

3. Anderson, R.W. (1995). Learning and evolution: A quantitative genetics approach. Jour-nal

of Theoretical Biology, 175, 89-101.

4. Ayala F.J. (1974). The concept of biological progress. In Studies in the Philosophy of

Biology, ed. F.J. Ayala, T. Dobzhansky,19:339-55. New York: Macmillan.

5. Ayala F.J. (1988). Can “progress” be defined as a biological concept? In Evolutionary

Progress, ed. M Nitecki, pp. 75-96. Chicago: University of Chicago Press.

6. Bäch, T. (1992). Self-adaptation in genetic algorithms. In F.J. Varela and P. Bourgine

(eds.), Towards a Practice of Autonomous Systems. MIT Press, pp. 263-271.

7. Baldwin, J.M. (1896). A new factor in evolution. American Naturalist, 30, 441-451.

8. Bedau, M.A., and Seymour, R. (1995). Adaptation of mutation rates in a simple model

of evolution. Complexity International, 2.

9. Blickle, T., and Thiele, L. (1995). A Comparison of Selection Schemes used in Genetic

Algorithms. Technical Report No. 11. Gloriastrasse 35, CH-8092 Zurich: Swiss Federal

Institute of Technology (ETH) Zurich, Computer Engineering and Communications

Networks Lab (TIK).

10. Blickle, T., and Thiele, L. (1995). A mathematical analysis of tournament selection, In

Proceedings of the Sixth International Conference on Genetic Algorithms, ICGA-95,

L.J. Eshelman, Ed., Morgan Kaufmann, San Mateo, CA, pp. 9-16.

11. Davis, L. (1989). Adapting operator probabilities in genetic search. Proceedings of the

Third International Conference on Genetic Algorithms, ICGA-89, Morgan Kaufmann,

San Mateo, CA, pp. 61-69.

12. Dawkins, R. (1989). The evolution of evolvability. In: Artificial Life, C. Langton, (ed.).

Addison-Wesley.

13. Dawkins, R. (1996). Climbing Mount Improbable. New York: W.W. Norton and Co.

14. Fogel, D.B., Fogel, L.J., and Atmar, J.W. (1991). Meta-evolutionary programming. In

R.R. Chen (Ed.), Proceedings of the Twenty-fifth Asilomar Conference on Signals, Sys-tems,

and Computers, pp. 540-545, California: Maple Press.

15. Gilinsky, N.L., and Good, I.J. (1991). Probabilities of origination, persistence, and

extinction of families of marine invertebrate life. Paleobiology, 17, 145-166.

16. Gould S.J. (1988). Trends as changes in variance: A new slant on progress and direc-tionality

in evolution. Journal of Paleontology, 62, 319-29.

17. Gould S.J. (1997). Full House: The Spread of Excellence from Plato to Darwin. New

York: Harmony.

18. Hinton, G.E., and Nowlan, S.J. (1987). How learning can guide evolution. Complex Sys-tems,

1, 495-502.

19. Lewin, R. (1985). Red Queen runs into trouble? Science, 227, 399-400.

20. McShea, D.W. (1996). Metazoan complexity and evolution: Is there a trend? Evolution,

50, 477-492.

21. McShea, D.W. (1998). Possible largest-scale trends in organismal evolution: Eight “Live

Hypotheses”. Annual Review of Ecology and Systematics, 29, 293-318.

22. Nitecki, M.H. (1988). Evolutionary Progress. Edited collection. Chicago: University of

Chicago Press.

23. Raup, D.M. (1975). Taxonomic survivorship curves and Van Valen's Law. Paleobiology,

1, 82-86.

24. Riedl, R. (1977). A systems-analytical approach to macro-evolutionary phenomena.

Quarterly Review of Biology, 52, 351-370.

25. Riedl, R. (1978). Order in Living Organisms: A Systems Analysis of Evolution. Trans-lated

by R.P.S. Jefferies. Translation of Die Ordnung Des Lebendigen. New York: Wiley.

26. Ruse, M. (1996). Monad to Man: The Concept of Progress in Evolutionary Biology.

Massachusetts: Harvard University Press.

27. Simon, H.A. (1962). The architecture of complexity. Proceedings of the American

Philosophical Society, 106, 467-482.

28. Syswerda, G. (1989). Uniform crossover in genetic algorithms. Proceedings of the Third

International Conference on Genetic Algorithms (ICGA-89), pp. 2-9. California: Mor-gan

Kaufmann.

29. Syswerda, G. (1991). A study of reproduction in generational and steady-state genetic

algorithms. Foundations of Genetic Algorithms. G. Rawlins, editor. Morgan Kaufmann.

pp. 94-101.

30. Turney, P.D. (1989). The architecture of complexity: A new blueprint. Synthese, 79,

515-542.

31. Turney, P.D. (1996). How to shift bias: Lessons from the Baldwin effect. Evolutionary

Computation, 4, 271-295.

32. Turney, P.D. (1999). Increasing evolvability considered as a large-scale trend in evolu-tion.

In A. Wu, ed., Proceedings of 1999 Genetic and Evolutionary Computation Con-ference

Workshop Program (GECCO-99 Workshop on Evolvability), pp. 43-46.

33. Van Valen, L. (1973). A new evolutionary law. Evolutionary Theory, 1, 1-30.

34. Vermeij, G.J. (1970). Adaptive versatility and skeleton construction. American Natural-ist,

104, 253-260.

35. Vermeij, G.J. (1971). Gastropod evolution and morphological diversity in relation to

shell geometry. Journal of Zoology, 163, 15-23.

36. Vermeij, G.J. (1973). Biological versatility and earth history. Proceedings of the

National Academy of Sciences of the United States of America, 70, 1936-1938.

37. Vermeij, G.J. (1974). Adaptation, versatility, and evolution. Systematic Zoology, 22,

466-477.

38. Wagner, G.P. and Altenberg, L. (1996). Complex adaptations and the evolution of evolv-ability.

Evolution, 50, 967-976.

39. Whitley, D., and Kauth, J. (1988). GENITOR: A different genetic algorithm. Proceed-ings

of the Rocky Mountain Conference on Artificial Intelligence, Denver, CO. pp. 118-

130.

40. Whitley, D. (1989). The GENITOR algorithm and selective pressure. Proceedings of the

Third International Conference on Genetic Algorithms (ICGA-89), pp. 116-121. Califor-nia:

Morgan Kaufmann.

41. Whitley, D., Dominic, S., Das, R., and Anderson, C.W. (1993). Genetic reinforcement

learning for neurocontrol problems. Machine Learning, 13, 259-284.

42. Williams, G.C. (1966). Adaptation and Natural Selection. New Jersey: Princeton Uni-versity

Press.

43. Wolpert, D.H., and Macready, W.G. (1997). No free lunch theorems for optimization.

IEEE Transactions on Evolutionary Computation, 1, 67-82.

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