Incorporating sub-programs as knowledge in program synthesis by PushGP and adaptive replacement mutation
Yifan He, Claus C. Aranha, Tetsuya Sakurai · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2022
Program synthesis aims to build an intelligent agent that composes computer programs to solve problems. Genetic programming (GP) provides an evolutionary solution for the program synthesis task. A typical GP includes a random initialization, an unguided variation, and a fitness-guided selection to search for a solution program. However, several recent studies have shown the importance of using prior knowledge in different components of the GP. This study investigates the effectiveness of incorporating sub-programs as "prior knowledge" into the variation process of GP by Replacement Mutation. We further design an adaptive strategy that allows the automatic selection of the helpful sub-programs to the search process from an archive (including helpful and unhelpful ones). With handcrafted sub-program archives, we verify the effectiveness of the Adaptive Replacement Mutation method in success rate. We demonstrate the effectiveness of our approach with transferred archives on two composite problems.