Improving Generalization of Evolved Programs Through Automatic Simplification

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Helmuth:2017:GECCO,

• author = "Thomas Helmuth and Nicholas Freitag McPhee and Edward Pantridge and Lee Spector",
• title = "Improving Generalization of Evolved Programs Through Automatic Simplification",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4920-8",
• address = "Berlin, Germany",
• pages = "937--944",
• size = "8 pages",
• URL = "http://cs.hamilton.edu/~thelmuth/Pubs/2017-GECCO-simplification-for-generalization.pdf",
• URL = "http://doi.acm.org/10.1145/3071178.3071330",
• DOI = "doi:10.1145/3071178.3071330",
• acmid = "3071330",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, automatic simplification, generalization, overfitting, push",
• month = "15-19 " # jul,
• abstract = "Programs evolved by genetic programming unfortunately often do not generalize to unseen data. Reliable synthesis of programs that generalize to unseen data is therefore an important open problem. We present evidence that smaller programs evolved using the PushGP system tend to generalize better over a range of program synthesis problems. Like in many genetic programming systems, programs evolved by PushGP usually have pieces that can be removed without changing the behaviour of the program. We describe methods for automatically simplifying evolved programs to make them smaller and potentially improve their generalization. We present five simplification methods and analyse their strengths and weaknesses on a suite of general program synthesis benchmark problems. All of our methods use a straightforward hill-climbing procedure to remove pieces of a program while ensuring that the resulting program gives the same errors on the training data as did the original program. We show that automatic simplification, previously used both for post-run analysis and as a genetic operator, can significantly improve the generalization rates of evolved programs.",
• notes = "Also known as \cite{Helmuth:2017:IGE:3071178.3071330} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Thomas Helmuth Nicholas Freitag McPhee Edward R Pantridge Lee Spector

Citations