Compressed Network Complexity Search

  • Faustino Gomez
  • Jan Koutník
  • Jürgen Schmidhuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)


Indirect encoding schemes for neural network phenotypes can represent large networks compactly. In previous work, we presented a new approach where networks are encoded indirectly as a set of Fourier-type coefficients that decorrelate weight matrices such that they can often be represented by a small number of genes, effectively reducing the search space dimensionality, and speed up search. Up to now, the complexity of networks using this encoding was fixed a priori, both in terms of (1) the number of free parameters (topology) and (2) the number of coefficients. In this paper, we introduce a method, called Compressed Network Complexity Search (CNCS), for automatically determining network complexity that favors parsimonious solutions. CNCS maintains a probability distribution over complexity classes that it uses to select which class to optimize. Class probabilities are adapted based on their expected fitness. Starting with a prior biased toward the simplest networks, the distribution grows gradually until a solution is found. Experiments on two benchmark control problems, including a challenging non-linear version of the helicopter hovering task, demonstrate that the method consistently finds simple solutions.


Compression Ratio Bias Weight Search Space Dimensionality Search Distribution Recurrent Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Faustino Gomez
    • 1
  • Jan Koutník
    • 1
  • Jürgen Schmidhuber
    • 1
  1. 1.IDSIAUSI-SUPSIManno-LuganoSwitzerland

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