- author = "David {Vella Zarb} and Geoff Parks and Timoleon Kipouros",
- title = "Program Synthesis with {LLM-Predicted} Minimal Specialized Grammars",
- booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
- year = "2025",
- editor = "Aniko Ekart and Nelishia Pillay",
- pages = "1072--1080",
- address = "Malaga, Spain",
- series = "GECCO '25",
- month = "14-18 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, ANN",
- isbn13 = "979-8-4007-1465-8",
-
URL = "
https://doi.org/10.1145/3712256.3726430",
-
DOI = "
doi:10.1145/3712256.3726430",
- size = "9 pages",
- abstract = "Grammatical evolution (GE) has proven effective at program synthesis. However, its performance is hindered by the exponential growth of search spaces as grammar complexity increases. Methods using recent advances in large language models and genetic programming have outperformed GE on some benchmarks, affirming the need for scalable approaches. This paper addresses the scalability challenge by proposing an in-context learning method to automatically generate minimal specialized grammars (MSGs), which are problem-specific subsets of a larger grammar designed to reduce search space complexity. To the best of our knowledge, this represents the first use of in-context learning for program synthesis within GE. Our approach conditions a language model on examples of Backus-Naur Form grammars to generate MSGs tailored to individual synthesis tasks. We evaluate this framework on a benchmark suite widely used in GE research. Experimental results show our method almost always outperforms the baseline GE approach, improving both the number and frequency of problems solved while reducing computational cost as measured by fitness evaluations.",
-
notes = "GECCO-2025 GP
A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",
