Abstract
Artificial evolution is discussed in the context of a successful experiment evolving a hardware configuration for a silicon chip (a Field Programmable Gate Array); the real chip was used to evaluate individual configurations on a tone-recognition task. The evolutionary pathway is analysed; it is shown that the population is genetically highly converged and travels far through genotype space. Species Adaptation Genetic Algorithms (SAGA) are appropriate for this type of evolution, and it is shown how an appropriate mutation rate was chosen. The role of junk on the genotype is discussed, and it is suggested that neutral networks (paths through genotype space via mutations which leave fitness unchanged) may be crucial to the effectiveness of evolution.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
H. Asoh and H. Muehlenbein. On the mean convergence time of evolutionary algorithms without selection and mutation. In H.-P. Schwefel Y. Davidor and R. Männer, editors, Parallel Problem Solving from Nature (PPSN III), Lecture Notes in Computer Science 866, pages 88–97. Springer-Verlag, 1994.
N. L. Cramer. A representation for the adaptive generation of simple sequential programs. In J.J. Grefenstette, editor, Proceedings of an International Conference on Genetic Algorithms and Their Applications, Hillsdale NJ, 1985. Lawrence Erlbaum Associates.
M. Eigen. New concepts for dealing with the evolution of nucleic acids. In Cold Spring Harbor Symposia on Quantitative Biology, vol. LII, 1987.
M. Eigen, J. McCaskill, and P. Schuster. Molecular quasi-species. Journal of Physical Chemistry, 92:6881–6891, 1988.
D.S. Falconer. Introduction to Quantitative Genetics, 3rd edition. Longman, 1989.
D. B. Fogel. System Identification through Simulated Evolution. Ginn Press, Needham, MA, 1991.
L.J. Fogel, A.J. Owens, and M.J. Walsh. Artificial Intelligence through Simulated Evolution. John Wiley, New York, 1966.
C.V. Forst, C. Reidys, and J. Weber. Evolutionary dynamics and optimization: Neutral networks as model-landscapes for RNA secondary-structure folding-landscapes. In F. Moran, A. Moreno, J.J. Merelo, and P. Cachon, editors, Advances in Artificial Life: Proceedings of the Third European Conference on Artificial Life (ECAL95). Lecture Notes in Artificial Intelligence 929, pages 128–147. Springer Verlag, 1995.
D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading MA, 1989.
I. Harvey. Evolutionary robotics and SAGA: the case for hill crawling and tournament selection. In C. Langton, editor, Artificial Life III, Santa Fe Institute Studies in the Sciences of Complexity, Proc. Vol. XVI, pages 299–326. Addison Wesley, 1993.
I. Harvey, P. Husbands, and D. T. Cliff. Genetic convergence in a species of evolved robot control architectures. In S. Forrest, editor, Genetic Algorithms: Proceedings of Fifth Intl. Conference, page 636, San Mateo CA, 1993. Morgan Kaufmann.
I. Harvey. Species adaptation genetic algorithms: A basis for a continuing SAGA. In F. J. Varela and P. Bourgine, editors, Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life, pages 346–354. MIT Press/Bradford Books, Cambridge, MA, 1992.
I.R. Harvey. The Artificial Evolution of Behaviour. PhD thesis, COGS, University of Sussex. Also available via www on http://www.cogs.susx.ac.uk/users/inmanh/, 1995.
J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USA, 1975.
T. Hoshino and M. Tsuchida. Manifestation of neutral genes in evolving robot navigation. Presented at Artificial Life V, Nara, Japan, May 1996.
M. Huynen and P. Hogeweg. Pattern generation in molecular evolution: Exploitation of the variation in RNA landscapes. J. Mol. Evol., 39(7):1–79, 1994.
M.A. Huynen, P.F. Stadler, and W. Fontana. Smoothness within ruggedness: The role of neutrality in adaptation. Proc. Natl. Acad. Sci. USA, 93:397–401, January 1996.
N. Jakobi. Encoding scheme issues for open-ended artificial evolution. In Voigt, H.-M., et al. (eds.), Parallel Problem Solving From Nature IV: Proc. of the Int. Conf. on Evolutionary Computation, Vol. 1141 of LNCS. Heidelberg: Springer-Verlag. Forthcoming.
S. A. Kauffman. Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, 1993.
M. Kimura. The Neutral Theory of Molecular Evolution. Cambridge University Press, 1983.
J. R. Koza. Genetic Programming. MIT Press/Bradford Books, Cambridge MA, 1992.
S. Ohno. Evolution by Gene Duplication. Allen and Unwin, London, 1970.
I. Rechenberg. Evolutionsstrategie: Optimierung techniser Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart, 1973.
H.-P. Schwefel. Numerical Optimization of Computer Models. John Wiley, Chichester, U.K., 1981.
A. Thompson. Evolutionary techniques for fault tolerance. In Proceedings of the UKACC International Conference on Control 1996 (CONTROL'96), pages 693–698. IEE Conference Publication Number 427, 1996.
A. Thompson. An evolved circuit, intrinsic in silicon, entwined with physics. In this volume: Proceedings of The First International Conference on Evolvable Systems: From Biology to Hardware, 1996 (ICES96), Springer-Verlag.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Harvey, I., Thompson, A. (1997). Through the labyrinth evolution finds a way: A silicon ridge. In: Higuchi, T., Iwata, M., Liu, W. (eds) Evolvable Systems: From Biology to Hardware. ICES 1996. Lecture Notes in Computer Science, vol 1259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63173-9_62
Download citation
DOI: https://doi.org/10.1007/3-540-63173-9_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63173-6
Online ISBN: 978-3-540-69204-1
eBook Packages: Springer Book Archive