Semantically Embedded Genetic Programming: Automated Design of Abstract Program Representations

We propose an alternative program representation that relies on automatic semantic-based embedding of programs into discrete multidimensional spaces. An embedding imposes a well-structured hypercube topology on the search space, endows it with a semantic-aware neighborhood, and enables convenient search using Cartesian coordinates. The embedding algorithm consists in locality-driven optimization and operates in abstraction from a specific fitness function, improving locality of all possible fitness landscapes simultaneously. We experimentally validate the approach on a large sample of symbolic regression tasks and show that it provides better search performance than the original program space. We demonstrate also that semantic embedding of small programs can be exploited in a compositional manner to effectively search the space of compound programs.