Genetic Programming of Augmenting Topologies for Hypercube-Based Indirect Encoding of Artificial Neural Networks
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.
KeywordsGenetic Programming Synaptic Weight Visual Discrimination Node Type Symbolic Regression
Unable to display preview. Download preview PDF.
- 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.Gruau, F.: Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. PhD thesis, Ecole Normale Supirieure de Lyon, France (1994)Google Scholar
- 6.Stanley, K.O.: Efficient Evolution of Neural Networks through Complexification. PhD thesis, The University of Texas at Austin (2004)Google Scholar
- 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.Poli, R., Langdon, W.B., Mcphee, N.F.: A Field Guide to Genetic Programming (March 2008), Published via http://lulu.com
- 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.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.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