Abstract
Recent works in both evolution theory and molecular biology have brought up the part played by selective neutrality in evolution dynamics. Contrary to the classical metaphor of fitness landscapes, the dynamics are not viewed as a climb towards optimal solutions but rather as explorations of networks of equivalent selective genotypes followed by jumps towards fitter networks. Although the benefit of neutrality is well known, it is hardly exploited in the genetic algorithm (GA) field. The only works about this subject deal with the influence of the inherent neutrality of a fitness landscape for evolution dynamics. In this paper, we propose a very different approach which consists to introduce “handmade” neutrality into the fitness landscape. Without any hypothesis about the inherent neutrality, we show that a GA is able to exploit new paths through the fitness landscape owing to the synthetic neutrality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Barnett, L.: Evolutionary dynamics on fitness landscapes with neutrality. Master’s thesis, University of East Sussex, Brighton (1997)
Barrett, C.L., Mortveit, H., Reidys, C.M.: Elements of a theory of simulation ii: sequential dynamical systems. Appl. Math. & Comp. (1998)
Collard, P., Aurand, J.P.: DGA: An efficient genetic algorithm. In: Cohn, A.G. (ed.) ECAI 1994: European Conference on Artificial Intelligence, pp. 487–491. John Wiley & Sons, Chichester (1994)
Collard, P., Escazut, C.: Relational schemata: A way to improve the expressiveness of classifiers. In: Eshelman, L. (ed.) ICGA 1995: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 397–404. Morgan Kaufmann, San Francisco (1995)
Collins, T.D.: Using Software Visualization Technologies to Help Evolutionary Algorithm Users Validate their Solutions. In: Bäck, T. (ed.) Proceedings of the seventh International Conference on Genetic Algorithms, pp. 307–314. Morgan Kaufmann, San Francisco (1998)
Forst, C.V., Reidys, C., Weber, J.: Evolutionary dynamics and optimization: Neutral networks as model-landscapes for rna secondary-structure foldinglandscapes. In: Morán, F., Merelo, J.J., Moreno, A., Chacon, P. (eds.) ECAL 1995. LNCS (LNAI), vol. 929, Springer, Heidelberg (1995)
Hordijk, W.: Correlation analysis of the synchronizing-ca landscape. Physica D 107, 255–264 (1997)
Huynen, M.A., Stadler, P.F., Fontana, W.: Smoothness within ruggedness: the role of neutrality in adaptation. In: Nijholt, A. (ed.) Context-Free Grammars. LNCS, vol. 93, pp. 397–401. Springer, Heidelberg (1980)
Kimura, M.: The Neutral Theory of Molecular Evolution. Cambridge University Press, Cambridge (1983)
Wright, S.: The theory of gene frequency. In: Evolution and the genetics of Population, vol. 2, pp. 120–143. University of Chicago Press, Chicago (1969)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Collard, P., Clergue, M., Defoin-Platel, M. (2000). Synthetic Neutrality for Artificial Evolution. In: Fonlupt, C., Hao, JK., Lutton, E., Schoenauer, M., Ronald, E. (eds) Artificial Evolution. AE 1999. Lecture Notes in Computer Science, vol 1829. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10721187_19
Download citation
DOI: https://doi.org/10.1007/10721187_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67846-5
Online ISBN: 978-3-540-44908-9
eBook Packages: Springer Book Archive