Going Bananas! - Unfolding Program Synthesis with Origami

Created by W.Langdon from gp-bibliography.bib Revision:1.8562

@InProceedings{Campos:BRACIS,

• author = "Matheus {Campos Fernandes} and Fabricio {Olivetti de Franca} and Emilio Francesquini",
• title = "Going Bananas! - Unfolding Program Synthesis with {Origami}",
• booktitle = "Brazilian Conference on Intelligent Systems",
• year = "2025",
• editor = "Aline Paes and Filipe A. N. Verri",
• volume = "15413",
• series = "LNAI",
• pages = "3--18",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Program Synthesis, Recursion Schemes",
• isbn13 = "978-3-031-79032-4",
• archiveprefix = "arXiv",
• eprint = "2406.01500",
• primaryclass = "cs.PL",
• URL = "https://arxiv.org/abs/2406.01500",
• URL = "https://doi.org/10.1007/978-3-031-79032-4_1",
• DOI = "doi:10.1007/978-3-031-79032-4_1",
• abstract = "Automatically creating a computer program using input-output examples can be a challenging task, especially when trying to synthesize computer programs that require loops or recursion. Even though the use of recursion can make the algorithmic description more succinct and declarative, this concept creates additional barriers to program synthesis algorithms such as the creation and the attempt to evaluate non-terminating programs. One reason is that the recursive function must define how to traverse (or generate) the data structure and, at the same time, how to process it. In functional programming, the concept of Recursion Schemes decouples these two tasks by putting a major focus on the data processing. This can also help to avoid some of the pitfalls of recursive functions during program synthesis, as argued in the paper that introduced the Origami technique. The authors showed how this technique can be effective in finding solutions for programs that require folding a list. In this work, we incorporate other Recursion Schemes into Origami, such as accumulated folding, unfolding, and the combination of unfolding and folding. We evaluated it on the 29 problems of the standard General Program Synthesis Benchmark Suite 1, obtaining favorable results against other well-known algorithms. Overall, Origami achieves the best results in 25 percent more problems than its predecessor (HOTGP) and an even higher increase when compared to other approaches. Not only that, but it can also consistently find a solution to problems for which other concurrent algorithms report a low success rate.",
• notes = "Federal University of ABC (UFABC), Sao Paulo, Santo Andre, Brazil",

}

Genetic Programming entries for Matheus Campos Fernandes Fabricio Olivetti de Franca Emilio de Camargo Francesquini

Citations