How a Generative Encoding Fares as Problem-Regularity Decreases

  • Jeff Clune
  • Charles Ofria
  • Robert T. Pennock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


It has been shown that generative representations, which allow the reuse of code, perform well on problems with high regularity (i.e. where a phenotypic motif must be repeated many times). To date, however, generative representations have not been tested on irregular problems. It is unknown how they will fare on problems with intermediate and low amounts of regularity. This paper compares a generative representation to a direct representation on problems that range from having multiple types of regularity to one that is completely irregular. As the regularity of the problem decreases, the performance of the generative representation degrades to, and then underperforms, the direct encoding. The degradation is not linear, however, yet tends to be consistent for different types of problem regularity. Furthermore, if the regularity of each type is sufficiently high, the generative encoding can simultaneously exploit different types of regularities.


Evolution regularity modularity ANN NEAT HyperNEAT 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hornby, G.S., Pollack, J.B.: Creating High-Level Components with a Generative Representation for Body-Brain Evolution. Artificial Life 8(3), 223–246 (2002)CrossRefGoogle Scholar
  2. 2.
    D’Ambrosio, D.B., Stanley, K.O.: A novel generative encoding for exploiting neural network sensor and output geometry. In: Whitley, D., Goldber, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) GECCO 2007, pp. 974–981. ACM Press, New York (2007)Google Scholar
  3. 3.
    Gauci, J.J., Stanley, K.O.: Generating Large-Scale Neural Networks Through Discovering Geometric Regularities. In: Whitley, D., Goldber, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) GECCO 2007, pp. 997–1004. ACM Press, New York (2007)Google Scholar
  4. 4.
    Gruau, F.: Genetic Synthesis of Boolean Neural Networks with a Cell Rewriting Developmental Process. International Workshop on Combinations of Genetic Algorithms and Neural Networks 6, 55–74 (1992)Google Scholar
  5. 5.
    Gruau, F., Whitley, D., Pyeatt, L.: A Comparison Between Cellular Encoding and Direct Encoding for Genetic Neural Networks. In: Proc. 1st Ann. Conf. on Genetic Programming 1996, pp. 81–89. MIT Press, Cambridge (1996)Google Scholar
  6. 6.
    Stanley, K.O., Miikkulainen, R.: A Taxonomy for Artificial Embryogeny. Artificial Life 9(2), 93–130 (2003)CrossRefGoogle Scholar
  7. 7.
    Reisinger, J., Miikkulainen, R.: Acquiring Evolvability Through Adaptive Representations. In: Whitley, D., Goldber, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.-G. (eds.) GECCO 2007, pp. 1045–1052. ACM Press, New York (2007)Google Scholar
  8. 8.
    Nolfi, S., Miglino, O., Parisi, D.: Phenotypic Plasticity in Evolving Neural Networks. In: Proc. Intl. Conf. from Perception to Action. IEEE Press, Los Alamitos (1994)Google Scholar
  9. 9.
    Stanley, K.O.: Compositional Pattern Producing Networks: A Novel Abstraction of Development. Genetic Programming and Evolvable Machines Special Issue on Developmental Systems 8(2), 131–162 (2007)CrossRefGoogle Scholar
  10. 10.
    Southan, C.: Has the Yo-Yo Stopped? An Assessment of Human Protein-Coding Gene Number. Proteomics 4(6), 1712–1726 (2004)CrossRefGoogle Scholar
  11. 11.
    Stanley, K.O., Miikkulainen, R.: Evolving Neural Networks Through Augmenting Topologies. Evolutionary Computation 10(2), 99–127 (2002)CrossRefGoogle Scholar
  12. 12.
    Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jeff Clune
    • 1
  • Charles Ofria
    • 1
  • Robert T. Pennock
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA
  2. 2.Department of Philosophy & Lyman Briggs CollegeMichigan State UniversityEast LansingUSA

Personalised recommendations