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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Acknowledgments
The work in BW’s lab is supported by the Polish National Science Centre (Project 2011/03/B/ST6/00399), with computational resources provided by the Tri-city Academic Computer Centre (TASK) and the Interdisciplinary Centre for Molecular and Mathematical Modeling (ICM, University of Warsaw; Project G33-8). We are grateful to Rene Doursat, Taras Kowaliw and Volker Steuber for discussions, and to Ahmed Abdelmotaleb for technical assistance in preparing Fig. 6.
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Wróbel , B., Joachimczak, M. (2014). Using the Genetic Regulatory Evolving Artificial Networks (GReaNs) Platform for Signal Processing, Animat Control, and Artificial Multicellular Development. In: Kowaliw, T., Bredeche, N., Doursat, R. (eds) Growing Adaptive Machines. Studies in Computational Intelligence, vol 557. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55337-0_6
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DOI: https://doi.org/10.1007/978-3-642-55337-0_6
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