Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks

  • Jan DrchalEmail author
  • Miroslav Šnorek
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)


In this paper we present a novel algorithm called GPAT (Genetic Programming of Augmenting Topologies) which evolves Genetic Programming (GP) trees in a similar way as a well-established neuro-evolutionary algorithm NEAT (NeuroEvolution of Augmenting Topologies) does. The evolution starts from a minimal form and gradually adds structure as needed. A niching evolutionary algorithm is used to protect individuals of a variable complexity in a single population. Although GPAT is a general approach we employ it mainly to evolve artificial neural networks by means of Hypercube-based indirect encoding which is an approach allowing for evolution of large-scale neural networks having theoretically unlimited size. We perform also experiments for directly encoded problems. The results show that GPAT outperforms both GP and NEAT taking the best of both.


Genetic Programming Synaptic Weight Visual Discrimination Node Type Symbolic Regression 
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  1. 1.
    Eggenberger-Hotz, P.: Creation of Neural Networks Based on Developmental and Evolutionary Principles. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 337–342. Springer, Heidelberg (1997)Google Scholar
  2. 2.
    Gruau, F.: Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. PhD thesis, Ecole Normale Supirieure de Lyon, France (1994)Google Scholar
  3. 3.
    Koutnik, J., Gomez, F., Schmidhuber, J.: Evolving Neural Networks in Compressed Weight Space. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation - GECCO 2010, p. 619. ACM Press, New York (2010)CrossRefGoogle Scholar
  4. 4.
    Gauci, J., Stanley, K.O.: Generating Large-Scale Neural Networks Through Discovering Geometric Regularities. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation - GECCO 2007, pp. 997–1004. ACM Press, New York (2007)CrossRefGoogle Scholar
  5. 5.
    Buk, Z., Koutník, J., Šnorek, M.: NEAT in HyperNEAT Substituted with Genetic Programming. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 243–252. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Stanley, K.O.: Efficient Evolution of Neural Networks through Complexification. PhD thesis, The University of Texas at Austin (2004)Google Scholar
  7. 7.
    Mahfoud, S.W.: A Comparison of Parallel and Sequential Niching Methods. In: Proceedings of the Sixth International Conference on Genetic Algorithms, pp. 136–143. Morgan Kaufmann (1995)Google Scholar
  8. 8.
    Poli, R., Langdon, W.B., Mcphee, N.F.: A Field Guide to Genetic Programming (March 2008), Published via
  9. 9.
    Yao, X., Yong, L., Guangming, L.: Evolutionary Programming Made Faster. IEEE Transactions on Evolutionary Computation 3, 82–102 (1999)CrossRefGoogle Scholar
  10. 10.
    Ekárt, A., Németh, S.Z.: A Metric for Genetic Programs and Fitness Sharing. In: Poli, R., Banzhaf, W., Langdon, W.B., Miller, J., Nordin, P., Fogarty, T.C. (eds.) EuroGP 2000. LNCS, vol. 1802, pp. 259–270. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Clune, J., Stanley, K.O., Pennock, R.T., Ofria, C.: On the Performance of Indirect Encoding Across the Continuum of Regularity. IEEE Transaction on Evolutionary Computation 15(3), 346–367 (2011)CrossRefGoogle Scholar
  12. 12.
    Drchal, J., Koutnik, J., Snorek, M.: HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment. In: CEC 2009 Proceedings of the Eleventh Conference on Congress on Evolutionary Computation, Trondheim, pp. 1087–1092. IEEE Press (2009)Google Scholar
  13. 13.
    Igel, C., Chellapilla, K.: Investigating the Influence of Depth and Degree of Genotypic Change on Fitness in Genetic Programming. In: Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, FL, USA, pp. 1061–1068. Morgan Kaufmann (1999)Google Scholar
  14. 14.
    Nguyen, T.H., Nguyen, X.H.: A Brief Overview of Population Diversity Measures in Genetic Programming. In: Proceedings of the Third Asian Pacific Workshop on Genetic Programming, pp. 128–139 (2006)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringFEE CTUPraha 2Czech Republic

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