Growing Adaptive Machines pp 187-200

Part of the Studies in Computational Intelligence book series (SCI, volume 557)

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

Chapter

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Systems Modelling Laboratory, IOPASSopotPoland
  2. 2.Evolutionary Systems LaboratoryAdam Mickiewicz University in PoznanPoznanPoland
  3. 3.Institute for NeuroinformaticsUniversity of Zurich/ETHZZurichSwitzerland

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