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Evolving the Ability of Limited Growth and Self-Repair for Artificial Embryos

  • Felix Streichert
  • Christian Spieth
  • Holger Ulmer
  • Andreas Zell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2801)

Abstract

In this paper we address the problem of limited growth and the difficulty of self-repairing in the field of Artificial Embryology. For this purpose, we developed a topological simulation environment for multiple cells which is continuous and structure-oriented, with a dynamically connected network of growing cells and endogenous communication between these cells. The cell behavior is simulated based on models of gene regulatory networks like Random Boolean Networks and S-Systems. Evolutionary Algorithms are used to evolve and optimize the parameters of the models of the gene networks. We compare the performance of Random Boolean Networks and S-Systems when optimized by Evolutionary Algorithms on the problem of limited growth and two types of cell death with and without signaling of cell death on the problem of self-repair.

Keywords

Limited Growth Gene Regulatory Network Cell Behavior Dynamic Bayesian Network Neighborhood Relation 
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 2003

Authors and Affiliations

  • Felix Streichert
    • 1
  • Christian Spieth
    • 1
  • Holger Ulmer
    • 1
  • Andreas Zell
    • 1
  1. 1.Center for Bioinformatics Tübingen (ZBIT)University of TübingenTübingenGermany

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