Execution trace caching for Linear Genetic Programming

In this paper we propose a new caching algorithm for Linear Genetic Programming (LGP) based on exploiting inter-generation program relationships. For each program we cache a partial summary of program execution, and use this summary to expedite the execution of all progeny. We study the theory behind our new caching algorithm and derive equations for optimizing algorithm performance. Through both theoretical and empirical results we demonstrate that our caching algorithm can decrease LGP execution time by up to 50%.