Abstract
In recent years, our biologic understanding was increased with the comprehension of the multitude of regulatory mechanisms that are fundamental in both processes of inheritance and of development, and some researchers advocate the need to explore computationally this new understanding. One of the outcomes was the Artificial Gene Regulatory (ARN) model, first proposed by Wolfgang Banzhaf. In this paper, we use this model as representation for a computational device and introduce new variation operators, showing experimentally that it is effective in solving a set of benchmark problems.
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
Banzhaf, W.: Artificial Regulatory Networks and Genetic Programming. In: Riolo, R.L., Worzel, B. (eds.) Genetic Programming Theory and Practice, ch. 4, pp. 43–62. Kluwer, Dordrecht (2003)
Davidson, E.H.: The regulatory genome: gene regulatory networks in development and evolution. Academic Press, London (2006)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Field, A.P., Hole, G.: How to design and report experiments. Sage Publications Ltd., Thousand Oaks (2003)
Koza, J., Keane, M.: Genetic breeding of non-linear optimal control strategies for broom balancing. Analysis and Optimization of Systes 144, 47–56 (1990)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs (Complex Adaptive Systems). MIT Press, Cambridge (1994)
Kuo, P., et al.: Evolving dynamics in an artificial regulatory network model. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 571–580. Springer, Heidelberg (2004)
Kuo, P.D., et al.: Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Biosystems 85(3), 177–200 (2006)
Langdon, W.: Why ants are hard. Cognitive Science Research Papers, 193–201 (1998)
Nicolau, M., Schoenauer, M.: Evolving specific network statistical properties using a gene regulatory network model. In: Raidl, G., et al. (eds.) GECCO 2009: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 723–730. ACM, Montreal (2009)
Nicolau, M., et al.: Evolving Genes to Balance a Pole. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 196–207. Springer, Heidelberg (2010)
Whitley, D., et al.: Alternative evolutionary algorithms for evolving programs: evolution strategies and steady state GP. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 919–926. ACM, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lopes, R.L., Costa, E. (2011). ReNCoDe: A Regulatory Network Computational Device. In: Silva, S., Foster, J.A., Nicolau, M., Machado, P., Giacobini, M. (eds) Genetic Programming. EuroGP 2011. Lecture Notes in Computer Science, vol 6621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20407-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-20407-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20406-7
Online ISBN: 978-3-642-20407-4
eBook Packages: Computer ScienceComputer Science (R0)