Run transferable libraries - Learning functional bias in problem domains

This paper introduces the notion of Run Transferable Libraries, a mechanism to pass knowledge acquired in one GP run to another. We demonstrate that a system using these libraries can solve a selection of standard benchmarks considerably more quickly than GP with ADFs by building knowledge about a problem. Further, we demonstrate that a GP system with these libraries can scale much better than a standard ADF GP system when trained initially on simpler versions of difficult problems.