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