- author = "Nicholas Freitag McPhee and Richard Lussier",
- title = "The Impact of Step Limits on Generalization and Stability in Software Synthesis",
- booktitle = "Genetic Programming Theory and Practice XX",
- year = "2023",
- editor = "Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu",
- series = "Genetic and Evolutionary Computation",
- pages = "87--104",
- address = "Michigan State University, USA",
- month = jun # " 1-3",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, PushGP, Step limits, Execution limits, Software synthesis, Generalisation, Stability",
- isbn13 = "978-981-99-8412-1",
-
DOI = "
doi:10.1007/978-981-99-8413-8_5",
- abstract = "Software synthesis research has historically relied on tools such as step limits to handle undesired behaviour like infinite loops. Here we explore the impact of different step limits on several benchmark problems, and see that these limits do affect the evolved behaviors both in terms of generalization and stability. To assess stability, we ran evolved programs with a range of step limits, and found several cases where programs failed to generalize with the step limit used during evolution, but generalized at other step limits. Two of our test problems evolved stable solutions in the sense that they correctly handled unseen test cases for all step limits above a certain point, i.e., correctly computed the answer. Our other two test problems, however, sometimes evolved unstable solutions which only generalised (i.e., correctly handled unseen test cases) for specific step limits. These programs relied on the step limit to terminate, and would no longer generalise if the step limit was modified slightly. This indicates that step limits can have a substantial impact on evolutionary performance, and suggests we need to revisit our notions of generalization in the context of evolutionary software synthesis.",
-
notes = "
Part of \cite{Hu:2023:GPTP} published after the workshop in 2024",
