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|Title:||Gin: Genetic improvement research made easy|
|Citation:||GECCO 2019: Proceedings of the 2019 Genetic and Evolutionary Computation Conference, 2019 / pp.985-993|
|Publisher Place:||New York|
|Conference Name:||Genetic and Evolutionary Computation Conference (GECCO) (13 Jul 2019 - 17 Jul 2019 : Prague, Czech Republic)|
|Alexander E. I. Brownlee, Justyna Petke, Brad Alexander, Earl T. Barr, Markus Wagner, David R. White|
|Abstract:||Genetic improvement (GI) is a young field of research on the cusp of transforming software development. GI uses search to improve existing software. Researchers have already shown that GI can improve human-written code, ranging from program repair to optimising run-time, from reducing energy-consumption to the transplantation of new functionality. Much remains to be done. The cost of re-implementing GI to investigate new approaches is hindering progress. Therefore, we present Gin, an extensible and modifiable toolbox for GI experimentation, with a novel combination of features. Instantiated in Java and targeting the Java ecosystem, Gin automatically transforms, builds, and tests Java projects. Out of the box, Gin supports automated test-generation and source code profiling. We show, through examples and a case study, how Gin facilitates experimentation and will speed innovation in GI.|
|Keywords:||Genetic Improvement; GI; Search-based Software Engineering; SBSE|
|Rights:||© 2019 Copyright held by the owner/author(s). Publication rights licensed to the Association for Computing Machinery.|
|Appears in Collections:||Computer Science publications|
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