- author = "Alberto Bartoli and Andrea {De Lorenzo} and Eric Medvet and Fabiano Tarlao",
- title = "On the Automatic Construction of Regular Expressions from Examples (GP vs. Humans 1-0)",
- booktitle = "GECCO 2016 Hot of the Press",
- year = "2016",
- editor = "Benjamin Doerr and Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
- pages = "155--156",
- keywords = "genetic algorithms, genetic programming",
- month = "20-24 " # jul,
- organisation = "SIGEVO",
- address = "Denver, USA",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
- isbn13 = "978-1-4503-4323-7",
-
DOI = "
doi:10.1145/2908961.2930946",
-
abstract = "Regular expressions are systematically used in a
number of different application domains. Writing a
regular expression for solving a specific task is
usually quite difficult, requiring significant
technical skills and creativity. We have developed a
tool based on Genetic Programming capable of
constructing regular expressions for text extraction
automatically, based on examples of the text to be
extracted.
We have recently demonstrated that our tool is human-competitive in terms of both accuracy of the regular expressions and time required for their construction. We base this claim on a large-scale experiment involving more than 1700 users on 10 text extraction tasks of realistic complexity. The F-measure of the expressions constructed by our tool was almost always higher than the average F-measure of the expressions constructed by each of the three categories of users involved in our experiment (Novice, Intermediate, Experienced). The time required by our tool was almost always smaller than the average time required by each of the three categories of users. The experiment is described in full detail in Can a machine replace humans? A case study. IEEE Intelligent Systems, 2016 \cite{Bartoli:2016:ieeeIS}",
- notes = "Distributed at GECCO-2016.",
