Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives

TL;DR

This paper presents a novel knowledge-driven program synthesis method that leverages adaptive mutation and auto-constructed subprogram archives to improve success rates and convergence speed in solving sequences of programming tasks.

Contribution

The paper introduces PushGP+EP+ARM, a fully automated approach that extracts and utilizes subprograms for program synthesis, outperforming baseline methods without human intervention.

Findings

PushGP+EP+ARM achieves lower train error

It has higher success counts in experiments

It converges faster than baseline methods

Abstract

We introduce Knowledge-Driven Program Synthesis (KDPS) as a variant of the program synthesis task that requires the agent to solve a sequence of program synthesis problems. In KDPS, the agent should use knowledge from the earlier problems to solve the later ones. We propose a novel method based on PushGP to solve the KDPS problem, which takes subprograms as knowledge. The proposed method extracts subprograms from the solution of previously solved problems by the Even Partitioning (EP) method and uses these subprograms to solve the upcoming programming task using Adaptive Replacement Mutation (ARM). We call this method PushGP+EP+ARM. With PushGP+EP+ARM, no human effort is required in the knowledge extraction and utilization processes. We compare the proposed method with PushGP, as well as a method using subprograms manually extracted by a human. Our PushGP+EP+ARM achieves better train error, success count, and faster convergence than PushGP. Additionally, we demonstrate the superiority of PushGP+EP+ARM when consecutively solving a sequence of six program synthesis problems.