  It has been hypothesized that one of the main reasons evolution has been able
to produce such impressive adaptations is because it has improved its own
ability to evolve -- "the evolution of evolvability". Rupert Riedl, for
example, an early pioneer of evolutionary developmental biology, suggested that
the evolution of complex adaptations is facilitated by a developmental
organization that is itself shaped by past selection to facilitate evolutionary
innovation. However, selection for characteristics that enable future
innovation seems paradoxical: natural selection cannot favor structures for
benefits they have not yet produced, and favoring characteristics for benefits
that have already been produced does not constitute future innovation. Here we
resolve this paradox by exploiting a formal equivalence between the evolution
of evolvability and learning systems. We use the conditions that enable simple
learning systems to generalize, i.e., to use past experience to improve
performance on previously unseen, future test cases, to demonstrate conditions
where natural selection can systematically favor developmental organizations
that benefit future evolvability. Using numerical simulations of evolution on
highly epistatic fitness landscapes, we illustrate how the structure of evolved
gene regulation networks can result in increased evolvability capable of
avoiding local fitness peaks and discovering higher fitness phenotypes. Our
findings support Riedl's intuition: Developmental organizations that "mimic"
the organization of constraints on phenotypes can be favored by short-term
selection and also facilitate future innovation. Importantly, the conditions
that enable the evolution of such surprising evolvability follow from the same
non-mysterious conditions that permit generalization in learning systems.
