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It has been hypothesized that one of the main reasons evolution has been ableto produce such impressive adaptations is because it has improved its ownability to evolve -- "the evolution of evolvability". Rupert Riedl, forexample, an early pioneer of evolutionary developmental biology, suggested thatthe evolution of complex adaptations is facilitated by a developmentalorganization that is itself shaped by past selection to facilitate evolutionaryinnovation. However, selection for characteristics that enable futureinnovation seems paradoxical: natural selection cannot favor structures forbenefits they have not yet produced, and favoring characteristics for benefitsthat have already been produced does not constitute future innovation. Here weresolve this paradox by exploiting a formal equivalence between the evolutionof evolvability and learning systems. We use the conditions that enable simplelearning systems to generalize, i.e., to use past experience to improveperformance on previously unseen, future test cases, to demonstrate conditionswhere natural selection can systematically favor developmental organizationsthat benefit future evolvability. Using numerical simulations of evolution onhighly epistatic fitness landscapes, we illustrate how the structure of evolvedgene regulation networks can result in increased evolvability capable ofavoiding local fitness peaks and discovering higher fitness phenotypes. Ourfindings support Riedl's intuition: Developmental organizations that "mimic"the organization of constraints on phenotypes can be favored by short-termselection and also facilitate future innovation. Importantly, the conditionsthat enable the evolution of such surprising evolvability follow from the samenon-mysterious conditions that permit generalization in learning systems.

Resolving the paradox of evolvability with learning theory: How evolution learns to improve evolvability on rugged fitness landscapes