Abstract
In this paper, we investigate a neutral epoch during an optimisation run with complex genotype-to-fitness mapping. The behaviour of the search process during neutral epochs is of importance for evolutionary r obotics and other artificial-life approaches that evolve problem solutions; recent work has argued that evolvability may change during these epochs. We investigate the distribution of offspring fitnesses from the best individuals of each generation in a population-based genetic algorithm, and see no trends towards higher probabilities of producing higher fitness offspring, and no trends towards higher probabilities of not producing lower fitness offspring. A second experiment in which populations from across the neutral epoch are used as initial populations for the genetic algorithm, shows no difference between the populations in the number of generations required to produce high fitness. We conclude that there is no evidence for change in evolvability during the neutral epoch in this optimisation run; the population is not doing anything “useful” during this period.
The population may also drop in fitness; work on error thresholds looks at the conditions under which this transition to lower fitnesses may occur [16].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
L. Altenberg. The evolution of evolvability in genetic programming. In K.E. Kinnear Jr, editor, Advances in Genetic Programming, chapter 3, pages 47–74. MIT Press, 1994.
L. Barnett. Ruggedness and neutrality: The NKp family of fitness landscapes. In C. Adami, R.K. Belew, H. Kitano, and C.E. Taylor, editors, Artificial Life VI: Proceedings of the Sixth International Conference on Artificial Life. MIT Press / Bradford Books, 1998.
C.L. Burch and L. Chao. Evolvability of an RNA virus is determined by its muta-tional neighbourhood. Nature, 406:625–628, August 2000.
L.L. Cavalli-Sforza and M.W. Feldman. Evolution of continuous variation: Direct approach through joint distribution of genotypes and phenotypes. Proceedings of the National Academy of Sciences, USA, 73:1689–1692, 1976.
M. Ebner, P. Langguth, J. Albert, M. Shackleton, and R. Shipman. On neutral networks and evolvability. In Proceedings of the 2001 Congress on Evolutionary Computation: CEC2001. IEEE, Korea, 2001. To appear.
W. Hordijk. A measure of landscapes. Evolutionary Computation, 4(4):335–360, 1996.
P. Husbands. Evolving robot behaviours with diffusing gas networks. In P. Husbands and J.-A. Meyer, editors, Evolutionary Robotics: First European Workshop, EvoRobot98, pages 71–86. Springer-Verlag, Berlin, April 1998.
P. Husbands, T.M. Smith, N. Jakobi, and M. O’Shea. Better living through chemistry: Evolving GasNets for robot control. Connection Science, 10(3-4):185–210, December 1998.
M.A. Huynen, P.F. Stadler, and W. Fontana. Smoothness within ruggedness: The role of neutrality in adaptation. Proceedings of the National Academy of Sciences, itUSA, 93:394–401, 1996.
T. Jones and S. Forrest. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In L.J. Eshelmann, editor, Proceedings of the Sixth International Conference on Genetic Algorithms (ICGA95), pages 184–192. Morgan Kaufmann, CA, 1995.
M. Kimura. The Neutral Theory of Molecular Evolution. Cambridge University Press, 1983.
M. Kirschner and J. Gerhart. Evolvability. Proceedings of the National Academy of Sciences, USA, 95:8420–8427, July 1998.
P. Marrow. Evolvability: Evolution, computation, biology. In A.S. Wu, editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program (GECCO-99 Workshop on Evolvability), pages 30–33. 1999.
B. Naudts and L. Kallel. A comparison of predictive measures of problem difficulty in evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 4(1):1–15, April 2000.
M.E. J. Newman and R. Engelhardt. Effects of neutral selection on the evolution of molecular species. Proceedings of the Royal Society of London, B, 265:1333, 1998.
M. Nowak and P. Schuster. Error thresholds of replication in finite populations-mutation frequencies and the onset of Muller’s ratchet. Journal of Theoretical Biology, 137:375–395, 1989.
T.M.C. Smith, P. Husbands, and M. O’Shea. Not measuring evolvability: Initial exploration of an evolutionary robotics search space. In Proceedings of the 2001 Congress on Evolutionary Computation: CEC2001. IEEE, Korea, 2001. To appear.
T.M.C. Smith, P. Husbands, and M. O’Shea. Neutral networks in an evolutionary robotics search space. In Proceedings of the 2001 Congress on Evolutionary Computation: CEC2001. IEEE, Korea, 2001. To appear.
T.M.C. Smith and A. Philippides. Nitric oxide signalling in real and artificial neural networks. BT Technology Journal, 18(4):140–149, October 2000.
P. Turney. Increasing evolvability considered as a large-scale trend in evolution. In A.S. Wu, editor, Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program (GECCO-99 Workshop on Evolvability), pages 43–46. 1999.
E. van Nimwegen, J.P. Crutchfield, and M. Huynen. Neutral evolution of muta-tional robustness. Proceedings of the National Academy of Sciences, USA, 96:9716–9720, 1999.
G.P. Wagner and L. Altenberg. Complex adaptations and the evolution of evolvability. Evolution, 50(3):967–976, 1996.
E. Weinberger. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biological Cybernetics, 63:325–336, 1990.
C.O. Wilke. Adaptive evolution on neutral networks. Bulletin of Mathematical Biology, 2001. Submitted.
C.O. Wilke, J.L. Wang, C. Ofria, R.E. Lenski, and C. Adami. Evolution of digital organisms lead to survival of the flattest. Nature, 2001. Submitted.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Smith, T., Husbands, P., O’Shea, M. (2001). Neutral Networks and Evolvability with Complex Genotype-Phenotype Mapping. In: Kelemen, J., Sosík, P. (eds) Advances in Artificial Life. ECAL 2001. Lecture Notes in Computer Science(), vol 2159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44811-X_29
Download citation
DOI: https://doi.org/10.1007/3-540-44811-X_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42567-0
Online ISBN: 978-3-540-44811-2
eBook Packages: Springer Book Archive