论文标题
通过自适应替代突变和自动构建子程序档案的知识驱动程序合成
Knowledge-Driven Program Synthesis via Adaptive Replacement Mutation and Auto-constructed Subprogram Archives
论文作者
论文摘要
我们将知识驱动的程序综合（KDP）作为程序综合任务的变体介绍，该任务需要代理解决程序合成问题的顺序。在KDP中，代理应使用早期问题中的知识来解决后期问题。我们提出了一种基于PushGP的新方法来解决KDPS问题，该问题将子程序作为知识。所提出的方法通过偶数分区（EP）方法从先前解决的问题的解中提取子程序，并使用这些子程序使用自适应替代突变（ARM）来解决即将到来的编程任务。我们将此方法称为PushGP+EP+ARM。使用PushGP+EP+ARM，在知识提取和利用过程中不需要人类的努力。我们将所提出的方法与PushGP进行了比较，以及使用人手动提取的子程序的方法。我们的PushGP+EP+ARM比PushGP实现了更好的火车错误，成功计数和更快的收敛速度。此外，当连续解决六个程序合成问题的序列时，我们证明了PushGP+EP+组的优势。
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.
