Semantic geometric initialization

Abstract

A common approach in Geometric Semantic Genetic Programming (GSGP) is to seed initial populations using conventional, semantic-unaware methods like Ramped Half-and-Half. We formally demonstrate that this may limit GSGP’s ability to find a program with the sought semantics. To overcome this issue, we determine the desired properties of geometric-aware semantic initialization and implement them in Semantic Geometric Initialization (Sgi) algorithm, which we instantiate for symbolic regression and Boolean function synthesis problems. Properties of Sgi and its impact on GSGP search are verified experimentally on nine symbolic regression and nine Boolean function synthesis benchmarks. When assessed experimentally, Sgi leads to superior performance of GSGP search: better best-of-run fitness and higher probability of finding the optimal program.