2011 | OriginalPaper | Chapter
Evolving Self-organizing Cellular Automata Based on Neural Network Genotypes
Authors : Wilfried Elmenreich, István Fehérvári
Published in: Self-Organizing Systems
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.