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

The core strength of evolutionary learning is the wild, creative, generalpurpose generativity of the evolutionary process. The core weakness of evolutionary learning is its tendency to spend a lot of time exploring dead ends, even in cases where a bit of analytical or problem-specific reasoning would be able to identify the dead-end as such. Given this situation, it is natural that researchers have explored ways of injecting analytical (in particular, probabilistic) inference into the core of evolutionary algorithms - yielding a class of algorithms known as EDAs or Estimation of Distribution Algorithms [PGL02]. EDAs have proved successful for many types of problems. However, there is not yet a truly convincing EDA for optimizing problems centrally involving floating-point (rather than discrete) variables. And attempts to use EDAs for automated program learning, while interesting, have also failed to yield dramatically successful results.