- author = "Jerry Swan and Krzysztof Krawiec and Zoltan A. Kocsis",
- title = "Stochastic synthesis of recursive functions made easy with bananas, lenses, envelopes and barbed wire",
- journal = "Genetic Programming and Evolvable Machines",
- year = "2019",
- volume = "20",
- number = "3",
- pages = "327--350",
- month = sep,
- keywords = "genetic algorithms, genetic programming, Program synthesis, Algebraic Data Types, Recursion schemes, Catamorphisms, Pattern matching",
- ISSN = "1389-2576",
-
URL = "
https://rdcu.be/dr528",
-
DOI = "
doi:10.1007/s10710-019-09347-3",
- size = "24 pages",
- abstract = "Stochastic synthesis of recursive functions has historically proved difficult, not least due to issues of non-termination and the often ad hoc methods for addressing this. This article presents a general method of implicit recursion which operates via an automatically-derivable decomposition of datatype structure by cases, thereby ensuring well-foundedness. The method is applied to recursive functions of long-standing interest and the results outperform recent work which combines two leading approaches and employs human in the loop to define the recursion structure. We show that stochastic synthesis with the proposed method on benchmark functions is effective even with random search, motivating a need for more difficult recursive benchmarks in future.",
-
notes = "See \cite{Swan:2019:GECCOcomp}
Jerry Swan: There's a reasonable argument that the proposed approach is 'foundational' (in both the CS-sense and the 'effective searchability' sense) for tree-based GP
Department of Computer Science, University of York, YO10 5GH York, UK",
