  Evolutionary deep intelligence has recently shown great promise for producing
small, powerful deep neural network models via the synthesis of increasingly
efficient architectures over successive generations. Despite recent research
showing the efficacy of multi-parent evolutionary synthesis, little has been
done to directly assess architectural similarity between networks during the
synthesis process for improved parent network selection. In this work, we
present a preliminary study into quantifying architectural similarity via the
percentage overlap of architectural clusters. Results show that networks
synthesized using architectural alignment (via gene tagging) maintain higher
architectural similarities within each generation, potentially restricting the
search space of highly efficient network architectures.
