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Functional and Structural Topologies in Evolved Neural Networks

  • Joseph T. Lizier
  • Mahendra Piraveenan
  • Dany Pradhana
  • Mikhail Prokopenko
  • Larry S. Yaeger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5777)

Abstract

The topic of evolutionary trends in complexity has drawn much controversy in the artificial life community. Rather than investigate the evolution of overall complexity, here we investigate the evolution of topology of networks in the Polyworld artificial life system. Our investigation encompasses both the actual structure of neural networks of agents in this system, and logical or functional networks inferred from statistical dependencies between nodes in the networks. We find interesting trends across several topological measures, which together imply a trend of more integrated activity across the networks (with the networks taking on a more “small-world” character) with evolutionary time.

Keywords

Mutual Information Structural Topology Information Transfer Functional Network Closeness Centrality 
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 2011

Authors and Affiliations

  • Joseph T. Lizier
    • 1
    • 2
  • Mahendra Piraveenan
    • 1
    • 2
  • Dany Pradhana
    • 1
  • Mikhail Prokopenko
    • 1
  • Larry S. Yaeger
    • 3
  1. 1.CSIRO Information and Communications Technology CentreNorth RydeAustralia
  2. 2.School of Information TechnologiesThe University of SydneyAustralia
  3. 3.School of InformaticsIndiana UniversityBloomingtonUSA

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