- author = "Justyna Petke and Mark Harman and William B. Langdon and Westley Weimer",
- title = "Using Genetic Improvement \& Code Transplants to Specialise a C++ Program to a Problem Class",
- howpublished = "11th Annual Humies Awards 2014",
- year = "2014",
- month = "14 " # jul,
- note = "Winner Silver Humie",
- keywords = "genetic algorithms, genetic programming, Genetic Improvement, NFR",
-
URL = "
http://www.genetic-programming.org/hc2014/Petke-Text.txt",
- size = "3 page",
- abstract = "Genetic Improvement (GI) is a form of Genetic Programming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combinatorial Interaction Testing (CIT), using automated code transplantation. Our GI-evolved solver achieves overall 17percent improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem.",
-
notes = "See
\cite{Petke:2014:EuroGP}.
http://www.genetic-programming.org/combined.php HUMIES",
