Growing Adaptive Machines

Volume 557 of the series Studies in Computational Intelligence pp 187-200


Using the Genetic Regulatory Evolving Artificial Networks (GReaNs) Platform for Signal Processing, Animat Control, and Artificial Multicellular Development

  • Borys Wróbel Affiliated withSystems Modelling Laboratory, IOPASEvolutionary Systems Laboratory, Adam Mickiewicz University in PoznanInstitute for Neuroinformatics, University of Zurich/ETHZ Email author 
  • , Michał  JoachimczakAffiliated withSystems Modelling Laboratory, IOPAS

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Building a system that allows for pattern formation and morphogenesis is a first step towards a biologically-inspired developmental-evolutionary approach to generate complex neural networks. In this chapter we present one such system, for Genetic Regulatory evolving artificial Networks (GReaNs). We review the results of previous experiments in which we investigated the evolvability of the encoding used in GReaNs in problems which involved: (i) controlling development of multicellular 2-dimensional (2D) soft-bodied animats; (ii) controlling development of 3-dimensional (3D) multicellular artificial bodies with asymmetrical shapes and patterning; (iii) directed movement of unicellular animats in 2D; and (iv) processing signals at the level of single cells. We also report a recent introduction of spiking neuron models in GReaNs. We then present a road map towards using this system for evolution and development of neural networks.