Evolving self-organizing cellular automata based on neural network genotypes

Authors:
Wilfried Elmenreich;István Fehérvári
Affiliations:
Lakeside Labs, Institute for Networked and Embedded Systems, University of Klagenfurt;Lakeside Labs, Institute for Networked and Embedded Systems, University of Klagenfurt
Venue:
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
Year:
2011

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Abstract

This paper depicts and evaluates an evolutionary design process for generating a complex self-organizing multicellular system based on Cellular Automata (CA). We extend the model of CA with a neural network that controls the cell behavior according to its internal state. The model is used to evolve an Artificial Neural Network controlling the cell behavior in a way a previously defined reference pattern emerges by interaction of the cells. Generating simple regular structures such as flags can be learned relatively easy, but for complex patterns such as for example paintings or photographs the output is only a rough approximation of the overall mean color scheme. The application of a genotypical template for all cells in the automaton greatly reduces the search space for the evolutionary algorithm, which makes the presented morphogenetic approach a promising and innovative method for overcoming the complexity limits of evolutionary design approaches.