Abstract
The artificial epigenetic network (AEN) is a computational model which is able to topologically modify its structure according to environmental stimulus. This approach is inspired by the functionality of epigenetics in nature, specifically, processes such as chromatin modifications which are able to dynamically modify the topology of gene regulatory networks. The AEN has previously been shown to perform well when applied to tasks which require a range of dynamical behaviors to be solved optimally. In addition, it has been shown that pruning of the AEN to remove non-functional elements can result in highly compact solutions to complex dynamical tasks. In this work, a method has been developed which provides the AEN with the ability to self prune throughout the optimisation process, whilst maintaining functionality. To test this hypothesis, the AEN is applied to a range of dynamical tasks and the most optimal solutions are analysed in terms of function and structure.
Keywords
- Complex Dynamic Tasks
- Objective Fitness Values
- Epigenetic Switch
- Epigenetic Molecules
- Artificial Gene Regulatory Networks
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bird, A.: Perceptions of epigenetics. Nature 447(7143), 396–398 (2007)
Bull, L.: Consideration of mobile DNA: new forms of artificial genetic regulatory networks. Nat. Comput. 12(4), 443–452 (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Hamann, H., Schmickl, T., Crailsheim, K.: Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, pp. 195–202. ACM (2011)
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T.N., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)
Huang, S.: Reprogramming cell fates: reconciling rarity with robustness. Bioessays 31(5), 546–560 (2009)
Lones, M.A., Turner, A.P., Fuente, L.A., Stepney, S., Caves, L.S.D., Tyrrell, A.M.: Biochemical connectionism. Nat. Comput. 12(4), 453–472 (2013)
Lones, M.A., Tyrrell, A.M., Stepney, S., Caves, L.S.: Controlling complex dynamics with artificial biochemical networks. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Uyar, A.Ş. (eds.) EuroGP 2010. LNCS, vol. 6021, pp. 159–170. Springer, Heidelberg (2010)
Masel, J., Trotter, M.V.: Robustness and evolvability. Trends Genet. 26(9), 406–414 (2010)
Moran, L.A., Horton, H.R., Scrimgeour, G., Perry, M.: Principles of Biochemistry. Pearson, Boston (2012)
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1717–1724. IEEE (2014)
Reid, C.R., Sumpter, D.J., Beekman, M.: Optimisation in a natural system: Argentine ants solve the Towers of Hanoi. J. Exp. Biol. 214(1), 50–58 (2011)
Reil, T.: Dynamics of gene expression in an artificial genome - implications for biological and artificial ontogeny. In: Floreano, D., Mondada, F. (eds.) ECAL 1999. LNCS, vol. 1674, pp. 457–466. Springer, Heidelberg (1999)
Turner, A.P.: The Artificial Epigenetic Network. Ph.D. thesis, University of York (2013)
Turner, A.P., Lones, M.A., Fuente, L.A., Stepney, S., Caves, L.S.D., Tyrrell, A.M.: The artificial epigenetic network. In: 10th Internation Conference on Evolvable Systems, Singapore, pp. 66–72. IEEE Press, April 2013
Turner, A.P., Lones, M.A., Fuente, L.A., Tyrrell, A.M., Stepney, S., Caves, L.S.D.: Controlling complex tasks using artificial epigenetic regulatory networks. BioSystems 112(2), 56–62 (2013)
Wang, R.S., Saadatpour, A., Albert, R.: Boolean modeling in systems biology: an overview of methodology and applications. Phys. Biol. 9(5), 055001 (2012)
Yang, X.S., Cui, Z., Xiao, R., Gandomi, A.H., Karamanoglu, M.: Swarm intelligence and bio-inspired computation: theory and applications. Newnes (2013)
Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32–49 (2011)
Acknowledgements
The authors would like to thank the EPSRC for their support of this work through the Platform Grant EP/K040820/1. Data created during this research is available at the following DOI: 10.15124/3f245e80- c306-4ada-8920-a0282e4962b3.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Turner, A.P., Trefzer, M.A., Lones, M.A., Tyrrell, A.M. (2015). Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic Network. In: Lones, M., Tyrrell, A., Smith, S., Fogel, G. (eds) Information Processing in Cells and Tissues. IPCAT 2015. Lecture Notes in Computer Science(), vol 9303. Springer, Cham. https://doi.org/10.1007/978-3-319-23108-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-23108-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23107-5
Online ISBN: 978-3-319-23108-2
eBook Packages: Computer ScienceComputer Science (R0)