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A novel optimization strategy, Info-Evo, is described, in which naturalgradient search using nonparametric Fisher information is used to provideongoing guidance to an evolutionary learning algorithm, so that theevolutionary process preferentially moves in the directions identified as"shortest paths" according to the natural gradient. Some specifics regardingthe application of this approach to automated program learning are reviewed,including a strategy for integrating Info-Evo into the MOSES program learningframework.

Info-Evo: Using Information Geometry to Guide Evolutionary Program Learning