Many-objective Grammar-guided Genetic Programming with Code Similarity Measurement for Program Synthesis

The approach known as Grammar-Guided Genetic Programming (G3P) is widely acknowledged as a highly effective method for program synthesis, which involves automatically generating code based on high-level formal specifications. Given the increasing quantity and scale of open software repositories and generative artificial intelligence techniques, there exists a significant range of methods for retrieving or generating source code using textual problem descriptions. Therefore, in light of the prevailing circumstances, it becomes imperative to introduce G3P into alternative means of user intent, with a specific focus on textual depictions. In our previous work, we assessed the potential for G3P to evolve programs based on bi-objectives that combine the similarity to the target program using four different similarity measures and the traditional input/output error rate. The result showed that such an approach improved the success rate for generating correct solutions for some of the considered problems. Nevertheless, it is noteworthy that despite the inclusion of various similarity measures, there is no single measure that uniformly improves the success rate of G3P across all problems. Instead, certain similarity measures exhibit effectiveness in addressing specific problems while demonstrating limited efficacy in others. In this paper, we would like to expand the bi-objective framework with different similarity measures to a many-objective framework to enhance the general performance of the algorithm to more range of problems. Our experiments show that compared to the bi-objective G3P (BOG3P), the Many-objective G3P (MaOG3P) approach could achieve the best result of all BOG3P algorithms with different similarity measures.