The theory of evolution links random variation and selection to incremental adaptation. In a different intellectual domain, learning theory links incremental adaptation (e.g., from positive and/or negative reinforcement) to intelligent behaviour. Specifically, learning theory explains how incremental adaptation can acquire knowledge from past experience and use it to direct future behaviours toward favourable outcomes. Until recently such cognitive learning seemed irrelevant to the 'uninformed' process of evolution. In our opinion, however, new results formally linking evolutionary processes to the principles of learning might provide solutions to several evolutionary puzzles -the evolution of evolvability, the evolution of ecological organisation, and evolutionary transitions in individuality. If so, the ability for evolution to learn might explain how it produces such apparently intelligent designs. Learning and EvolutionNew insights and new ways of understanding are often provided by analogies. Analogous reasoning is regarded as a core faculty of human cognition [1], and necessary for complex abstract causal reasoning [2]. In biology, analogy is sometimes considered to be the poor cousin of homology -similar, but not really the same. But in science more generally, analogies can be founded on perfect equivalences, for example, mathematical isomorphisms or algorithmic equivalence, thus enabling the transfer of ready-made results from one system or discipline to another, for example, between quasispecies theory and population genetics [3,4], electromagnetic fields and hydrodynamics [5], and magnetism and neural networks [6]. The previously casual analogy between learning systems and evolution by natural selection has recently been deepened to a level where such transfer can begin.How Intelligent is Evolution? Evolution is sometimes likened to an active problem solver, seeking out ingenious solutions to difficult environmental challenges. The solutions discovered by evolution can certainly appear ingenious. Mechanistically, however, there appear to be good reasons to doubt that cognitive problem solving and evolution are equivalent in any real sense. For example, cognitive problem solving can utilise past knowledge about a problem domain to 'anticipate' future outcomes and direct exploration of solutions, whereas evolutionary exploration is myopic and dependent on undirected variation. Intelligent problem solvers can also form high-level or modular representations of a problem, making it easier to reuse partial solutions in new contexts, whereas evolution merely plods on, filtering random replication errors.Yet, this is not the whole story. Whilst genetic variation might be undirected, the pattern of phenotypic variation is shaped and biased by the processes of development. Moreover, the organisation of developmental processes (from gene regulatory interactions to morphological body plans) is itself, in large part, a product of past evolution. This affords the possibility that random genetic changes might produce...