  Representations for black-box optimisation methods (such as evolutionary
algorithms) are traditionally constructed using a delicate manual process. This
is in contrast to the representation that maps DNAs to phenotypes in biological
organisms, which is at the hear of biological complexity and evolvability.
Additionally, the core of this process is fundamentally the same across nearly
all forms of life, reflecting their shared evolutionary origin. Generative
models have shown promise in being learnable representations for black-box
optimisation but they are not per se designed to be easily searchable. Here we
present a system that can meta-learn such representation by directly optimising
for a representation's ability to generate quality-diversity. In more detail,
we show our meta-learning approach can find one Neural Cellular Automata, in
which cells can attend to different parts of a "DNA" string genome during
development, enabling it to grow different solvable 2D maze structures. We show
that the evolved genotype-to-phenotype mappings become more and more evolvable,
not only resulting in a faster search but also increasing the quality and
diversity of grown artefacts.
