3. Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks

  1. Matthias Dehmer2,
  2. Abbe Mowshowitz3 and
  3. Frank Emmert-Streib3
  1. Hervé Le Nagard and
  2. Olivier Tenaillon

Published Online: 12 JUL 2013

DOI: 10.1002/9783527670468.ch03

Advances in Network Complexity

Advances in Network Complexity

How to Cite

Le Nagard, H. and Tenaillon, O. (2013) Selection-Based Estimates of Complexity Unravel Some Mechanisms and Selective Pressures Underlying the Evolution of Complexity in Artificial Networks, in Advances in Network Complexity (eds M. Dehmer, A. Mowshowitz and F. Emmert-Streib), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527670468.ch03

Editor Information

  1. 2

    UMIT, Institut für Bioinformatik und, Translationale Forschung, Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tyrol, Austria

  2. 3

    The City College of New York, Department of Computer Science, 138th Street at Convent Avenue, New York, NY 10031, USA

Author Information

  1. University Paris Diderot, INSERM UMR-S 738, 75018, Paris, France

Publication History

  1. Published Online: 12 JUL 2013
  2. Published Print: 10 JUL 2013

ISBN Information

Print ISBN: 9783527332915

Online ISBN: 9783527670468

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Keywords:

  • artificial networks;
  • informational complexity (IC);
  • organismal complexity;
  • phenotypic complexity;
  • selective pressures

Summary

Using artificial networks and different estimates of complexity, this chapter discusses the biological meaning of the different estimates of complexity: while informational complexity (IC) reflects more the complicatedness of the implementation, phenotypic complexity reflects more the complexity of the task performed. The estimation methods of phenotypic complexity offer, therefore, a relevant framework to study complexity and its evolution. It suggests that complexity may be better understood through the interaction of the organisms and its selective environment. These methods are applied to more biological networks to try to uncover some real molecular determinants of complexity and to develop some further less-constrains models in which the size of the networks is free to evolve and the phenotypes used to infer fitness are less fixed. Finally combining these approaches with some topological estimates of network complexity may be an interesting perspective to understand the topological organizations that promote phenotypic complexity.