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On a Quantitative Measure for Modularity Based on Information Theory

  • Daniel Polani
  • Peter Dauscher
  • Thomas Uthmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)

Abstract

The concept of modularity appears to be crucial for many questions in the field of Artificial Life research. However, there have not been many quantitative measures for modularity that are both general and viable. In this paper we introduce a measure for modularity based on information theory. Due to the generality of the information theory formalism, this measure can be applied to various problems and models; some connections to other formalisms are presented.

Keywords

Genetic Algorithm Mutual Information Modular Decomposition Intrinsic Information Tual Information 
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 2005

Authors and Affiliations

  • Daniel Polani
    • 1
  • Peter Dauscher
    • 2
  • Thomas Uthmann
    • 2
  1. 1.University of HertfordshireHatfieldUK
  2. 2.Johannes Gutenberg Universität MainzGermany

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