  Epigenetic Tracking (ET) is an Artificial Embryology system which allows for
the evolution and development of large complex structures built from artificial
cells. In terms of the number of cells, the complexity of the bodies generated
with ET is comparable with the complexity of biological organisms. We have
previously used ET to simulate the growth of multicellular bodies with
arbitrary 3-dimensional shapes which perform computation using the paradigm of
"metabolic computing". In this paper we investigate the memory capacity of such
computational structures and analyse the trade-off between shape and
computation. We now plan to build on these foundations to create a
biologically-inspired model in which the encoding of the phenotype is efficient
(in terms of the compactness of the genome) and evolvable in tasks involving
non-trivial computation, robust to damage and capable of self-maintenance and
self-repair.
