Growth of structured artificial neural networks by virtual embryogenesis

  • Authors:

  • Ronald Thenius;Michael Bodi;Thomas Schmickl;Karl Crailsheim

  • Affiliations:

  • Artificial Life Laboratory of the Department of Zoology, Karl-Franzens University Graz, Graz, Austria;Artificial Life Laboratory of the Department of Zoology, Karl-Franzens University Graz, Graz, Austria;Artificial Life Laboratory of the Department of Zoology, Karl-Franzens University Graz, Graz, Austria;Artificial Life Laboratory of the Department of Zoology, Karl-Franzens University Graz, Graz, Austria

  • Venue:
  • ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
  • Year:
  • 2009

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Abstract

In the work at hand, a bio-inspired approach to robot controller evolution is described. By using concepts found in biological embryogenesis we developed a system of virtual embryogenesis, that can be used to shape artificial neural networks. The described virtual embryogenesis has the ability to structure a network, regarding the number of nodes, the degree of connectivity between the nodes and the amount and structure of sub-networks. Furthermore, it allows the development of inhomogeneous neural networks by cellular differentiation processes by the evolution predispositions of cells to different learning-paradigms or functionalities. The main goal of the described method is the evolution of a logical structure (e.g., artificial neural networks), that is able to control an artificial agent (e.g., robot). The method of developing, extracting and consolidation of an neural network from a virtual embryo is described. The work at hand demonstrates the ability of the described system to produce functional neural patterns, even after mutations have taken place in the genome.