Functional and structural topologies in evolved neural networks

  • Authors:

  • Joseph T. Lizier;Mahendra Piraveenan;Dany Pradhana;Mikhail Prokopenko;Larry S. Yaeger

  • Affiliations:

  • CSIRO Information and Communications Technology Centre, North Ryde, NSW, Australia and School of Information Technologies, The University of Sydney, NSW, Australia;CSIRO Information and Communications Technology Centre, North Ryde, NSW, Australia and School of Information Technologies, The University of Sydney, NSW, Australia;CSIRO Information and Communications Technology Centre, North Ryde, NSW, Australia;CSIRO Information and Communications Technology Centre, North Ryde, NSW, Australia;School of Informatics, Indiana University, Bloomington, IN

  • Venue:
  • ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part I
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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.