  Evolutionary deep intelligence has recently shown great promise for producing
small, powerful deep neural network models via the organic synthesis of
increasingly efficient architectures over successive generations. Existing
evolutionary synthesis processes, however, have allowed the mating of parent
networks independent of architectural alignment, resulting in a mismatch of
network structures. We present a preliminary study into the effects of
architectural alignment during evolutionary synthesis using a gene tagging
system. Surprisingly, the network architectures synthesized using the gene
tagging approach resulted in slower decreases in performance accuracy and
storage size; however, the resultant networks were comparable in size and
performance accuracy to the non-gene tagging networks. Furthermore, we
speculate that there is a noticeable decrease in network variability for
networks synthesized with gene tagging, indicating that enforcing a
like-with-like mating policy potentially restricts the exploration of the
search space of possible network architectures.
