  In the present article, we propose a paradigm shift on evolving Artificial
Neural Networks (ANNs) towards a new bio-inspired design that is grounded on
the structural properties, interactions, and dynamics of protein networks
(PNs): the Artificial Protein Network (APN). This introduces several advantages
previously unrealized by state-of-the-art approaches in NE: (1) We can draw
inspiration from how nature, thanks to millions of years of evolution,
efficiently encodes protein interactions in the DNA to translate our APN to
silicon DNA. This helps bridge the gap between syntax and semantics observed in
current NE approaches. (2) We can learn from how nature builds networks in our
genes, allowing us to design new and smarter networks through EA evolution. (3)
We can perform EA crossover/mutation operations and evolution steps,
replicating the operations observed in nature directly on the genotype of
networks, thus exploring and exploiting the phenotypic space, such that we
avoid getting trapped in sub-optimal solutions. (4) Our novel definition of APN
opens new ways to leverage our knowledge about different living things and
processes from biology. (5) Using biologically inspired encodings, we can model
more complex demographic and ecological relationships (e.g., virus-host or
predator-prey interactions), allowing us to optimise for multiple, often
conflicting objectives.
