Abstract
Evolutionary Algorithms (EA) are stochastic search algorithms inspired by the principles of selection and variation posited by the theory of evolution, mimicking in a simple way those mechanisms. In particular, EAs approach differently from nature the genotype - phenotype relationship, and this view is a recurrent issue among researchers. Moreover, in spite of some performance improvements, it is a true fact that biology knowledge has advanced faster than our ability to incorporate novel biological ideas into EAs. Recently, some researchers start exploring computationally our new comprehension about the multitude of the regulatory mechanisms that are fundamental in both processes of inheritance and of development in natural systems, trying to include those mechanism in the EA. One of the first successful proposals is the Artificial Gene Regulatory (ARN) model, by Wolfgang Banzhaf. Soon after some variants of the ARN with increased capabilities were tested. In this paper, we further explore the capabilities of one of those, the Regulatory Network Computational Device, empowering it with feedback connections. The efficacy and efficiency of this alternative is tested experimentally using a typical benchmark problem for recurrent and developmental systems. In order to gain a better understanding about the reasons for the improved quality of the results, we undertake a preliminary study about the role of neutral mutations during the evolutionary process.
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.
Similar content being viewed by others
References
Banzhaf, W.: Artificial regulatory networks and genetic programming. Genetic Programming Theory and Practice, 43–62, 2003
Bongard, J.: Evolving modular genetic regulatory networks. In: IEEE 2002 Congress on Evolutionary Computation (CEC 2002), pp. 1872–1877. IEEE Press (2002)
Davidson, E.H.: The regulatory genome: gene regulatory networks in development and evolution. Academic Press (2006)
Dwight Kuo, P., Banzhaf, W., Leier, A.: Network topology and the evolution of dynamics in an artificial genetic regulatory network model created by whole genome duplication and divergence. Bio Systems 85(3), 177–200 (2006)
Eggenberger, P.: Evolving morphologies of simulated 3D organisms based on differential gene expression. In: Husbands, P., Harvey, I. (eds.) Fourth European Conference of Artificial Life. MIT Press, Cambridge (1997)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Harding, S., Miller, J., Banzhaf, W.: Self modifying cartesian genetic programming: Fibonacci, squares, regression and summing. Genetic Programming, 133–144 (2009)
Lopes, R.L., Costa, E.: ReNCoDe: A Regulatory Network Computational Device. Genetic Programming 6621(EuroGP 2011), 142–153 (2011)
Lopes, R.L., Costa, E.: Using Feedback in a Regulatory Network Computational Device. In: GECCO 2011: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (to be published in, 2011)
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., Schoenauer, M., Banzhaf, W.: 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)
Roggen, D., Federici, D., Floreano, D.: Evolutionary morphogenesis for multi-cellular systems. Genetic Programming and Evolvable Machines 8(1), 61–96 (2006)
Spector, L., Stoffel, K.: Ontogenetic programming. In: Proceedings of the First Annual Conference on Genetic Programming, pp. 394–399. MIT Press, Cambridge (1996)
Teichmann, S.a., Babu, M.M.: Gene regulatory network growth by duplication. Nature Genetics 36(5), 492–496 (2004)
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). The Squares Problem and a Neutrality Analysis with ReNCoDe. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-24769-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24768-2
Online ISBN: 978-3-642-24769-9
eBook Packages: Computer ScienceComputer Science (R0)