Environments Conducive to Evolution of Modularity

  • Vineet R. Khare
  • Bernhard Sendhoff
  • Xin Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)


Modularity has been recognised as one of the crucial aspects of natural complex systems. Since these are results of evolution, it has been argued that modular systems must have selective advantages over their monolithic counterparts. Simulation results with artificial neuro-evolutionary complex systems, however, are indecisive in this regard. It has been shown that advantages of modularity, if judged on a static task, in these systems are very much dependent on various factors involved in the training of these systems. We present a couple of dynamic environments and argue that environments like these might be partly responsible for the evolution of modular systems. These environments allow for a better, more direct use of structural information present within modular systems hence limit the influence of other factors. We support these arguments with the help of a co-evolutionary model and a fitness measure based on system performance in these dynamic environments.


Dynamic Environment Hide Unit Related Task Modular System Cross Entropy 
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 2006

Authors and Affiliations

  • Vineet R. Khare
    • 1
  • Bernhard Sendhoff
    • 2
  • Xin Yao
    • 1
  1. 1.Natural Computation Group, School of Computer ScienceThe University of BirminghamBirminghamUK
  2. 2.Honda Research Institute Europe GmbHOffenbach/MainGermany

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