Growth of Structured Artificial Neural Networks by Virtual Embryogenesis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Ronald Thenius , Michael Bodi , Thomas Schmickl , Karl Crailsheim

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. More... »

PAGES

118-125

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-21314-4_15

DOI

http://dx.doi.org/10.1007/978-3-642-21314-4_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1000765278


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