Abstract
We extend genetic programming (GP) with a local memory and vectorization to evolve simple, perceptron-like programs capable of learning by error correction. The local memory allows for a scalar value or vector to be stored and manipulated within a local scope of GP tree. Vectorization consists in grouping input variables and processing them as vectors. We demonstrate these extensions, along with an island model, allow to evolve general perceptron-like programs, i.e. working for any number of inputs. This is unlike in standard GP, where inputs are represented explicitly as scalars, so that scaling up the problem would require to evolve a new solution. Moreover, we find vectorization allows to represent programs more compactly and facilitates the evolutionary search.
Keywords
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
Christensen, S., Oppacher, F.: An Analysis of Koza’s Computational Effort Statistic for Genetic Programming. In: Foster, J.A., Lutton, E., Miller, J., Ryan, C., Tettamanzi, A.G.B. (eds.) EuroGP 2002. LNCS, vol. 2278, pp. 182–191. Springer, Heidelberg (2002)
Floreano, D., Dürr, P., Mattiussi, C.: Neuroevolution: From Architectures to Learning. Evolutionary Intelligence 1(1), 47–62 (2008)
Koza, J.R.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufmann Pub., San Francisco (1999)
Koza, J.R., Keane, M.A., Streeter, M.J.: What’s AI Done for Me Lately? Genetic Programming’s Human-Competitive Results. IEEE Intell. Syst., 25–31 (2003)
Miikkulainen, R.: Evolving Neural Networks. In: Proc. of the 2007 GECCO Conf. Comp. on Genetic and Evol. Comput., pp. 3415–3434. ACM, New York (2007)
Montana, D.J.: Strongly Typed Genetic Programming. Evolutionary Computation 3(2), 199–230 (1995)
Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Press (2008)
Teller, A.: Turing Completeness in the Language of Genetic Programming with Indexed Memory. In: Proc. of the 1994 IEEE World Congr. on Comput. Intell., vol. 1, pp. 136–141 (1994)
Tomassini, M.: Spatially Structured Evolutionary Algorithms. Springer, Heidelberg (2005)
Widrow, B., Lehr, M.A.: 30 Years of Adaptive Neural Networks: Perceptron, Madaline and Backpropagation. Proc. IEEE 78(9), 1415–1442 (1990)
Woodward, J.R., Bai, R.: Why Evolution Is Not a Good Paradigm for Program Induction: A Critique of Genetic Programming. In: Proc. of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 593–600 (2009)
Yao, X.: Evolving Artificial Neural Networks. Proc. IEEE 87(9), 1423–1447 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suchorzewski, M. (2010). Extending Genetic Programming to Evolve Perceptron-Like Learning Programs. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-13232-2_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13231-5
Online ISBN: 978-3-642-13232-2
eBook Packages: Computer ScienceComputer Science (R0)