Abstract
In the past decades, a number of genetic programming techniques have been developed to evolve machine instructions. However, these approaches typically suffer from a lack of scalability that seriously impairs their applicability to real-world scenarios. In this paper, a novel self-scaling instruction generation method is introduced, which tries to overcome the scalability issue by using Cartesian Genetic Programming. In the proposed method, a dual-layer network architecture is created: one layer is used to evolve a series of instructions while the other is dedicated to the generation of loop control parameters.
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
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)
Nordin, P.: Evolutionary Program Induction of Binary Machine Code and its Applications. PhD thesis, der Universitat Dortmund am Fachereich Informatik (1997)
Spector, L., Robinson, A.: Genetic programming and autoconstructive evolution with the push programming language. Genetic Programming and Evolvable Machines 3, 7–40 (2002)
Kushchu, I.: Genetic programming and evolutionary generalization. IEEE Transactions on Evolutionary Computation 6(5), 431–442 (2002)
Yu, T.: Hierachical processing for evolving recursive and modular programs using higher order functions and lambda abstractions. Genetic Programming and Evolvable Machines 2, 345–380 (2001)
Wong, M.L., Mun, T.: Evolving recursive programs by using adaptive grammar based genetic programming. Genetic Programming and Evolvable Machines 6, 421–455 (2005)
Harding, S., Miller, J.F., Banzhaf, W.: Developments in cartesian genetic programming: Self-modifying cgp. Genetic Programming and Evolvable Machines 11, 397–439 (2010)
Miller, J.F.: An empirical study of the efficiency of learning boolean functions using a cartesian genetic programming approach. In: Proceedings of the 1st Genetic and Evolutionary Computation Conference, pp. 1135–1142 (1999)
Miller, J.F., Thomson, P.: Cartesian genetic programming. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 121–132. Springer, Heidelberg (2000)
Yu, T., Miller, J.F.: Neutrality and the evolvability of boolean function landscape. In: Miller, J., Tomassini, M., Lanzi, P.L., Ryan, C., Tetamanzi, A.G.B., Langdon, W.B. (eds.) EuroGP 2001. LNCS, vol. 2038, pp. 204–217. Springer, Heidelberg (2001)
Walker, J.A., Liu, Y., Tempesti, G., Tyrrell, A.M.: Automatic code generation on a MOVE processor using cartesian genetic programming. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds.) ICES 2010. LNCS, vol. 6274, pp. 238–249. Springer, Heidelberg (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Walker, J.A., Hilder, J.A., Tyrrell, A.M.: Towards evolving industry-feasible intrinsic variability tolerant cmos designs. In: IEEE Congress on Evolutionary Computation, pp. 1591–1598 (2009)
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
Liu, Y., Tempesti, G., Walker, J.A., Timmis, J., Tyrrell, A.M., Bremner, P. (2011). A Self-scaling Instruction Generator Using Cartesian Genetic Programming. 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_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-20407-4_26
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)