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Epigenetic Tracking (ET) is an Artificial Embryology system which allows forthe evolution and development of large complex structures built from artificialcells. In terms of the number of cells, the complexity of the bodies generatedwith ET is comparable with the complexity of biological organisms. We havepreviously used ET to simulate the growth of multicellular bodies witharbitrary 3-dimensional shapes which perform computation using the paradigm of"metabolic computing". In this paper we investigate the memory capacity of suchcomputational structures and analyse the trade-off between shape andcomputation. We now plan to build on these foundations to create abiologically-inspired model in which the encoding of the phenotype is efficient(in terms of the compactness of the genome) and evolvable in tasks involvingnon-trivial computation, robust to damage and capable of self-maintenance andself-repair.

Evolution and development of complex computational systems using the paradigm of metabolic computing in Epigenetic Tracking