- author = "David R. White and Andrea Arcuri and John A. Clark",
- title = "Evolutionary Improvement of Programs",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2011",
- volume = "15",
- number = "4",
- pages = "515--538",
- month = aug,
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Coevolution, embedded systems, execution time, genetic programming, multiobjective optimisation, nonfunctional criteria, search based software engineering STGP, SPEA2",
- ISSN = "1089-778X",
-
URL = "
http://crest.cs.ucl.ac.uk/fileadmin/crest/sebasepaper/WhiteAC.pdf",
-
video_url = "
http://crest.cs.ucl.ac.uk/cow/50/videos/arcuri_cow50_480p.mp4",
-
DOI = "
doi:10.1109/TEVC.2010.2083669",
- size = "24 pages",
- abstract = "Most applications of genetic programming (GP) involve the creation of an entirely new function, program or expression to solve a specific problem. In this paper, we propose a new approach that applies GP to improve existing software by optimising its non-functional properties such as execution time, memory usage, or power consumption. In general, satisfying non-functional requirements is a difficult task and often achieved in part by optimizing compilers. However, modern compilers are in general not always able to produce semantically equivalent alternatives that optimise non-functional properties, even if such alternatives are known to exist: this is usually due to the limited local nature of such optimisations. In this paper, we discuss how best to combine and extend the existing evolutionary methods of GP, multiobjective optimization, and coevolution in order to improve existing software. Given as input the implementation of a function, we attempt to evolve a semantically equivalent version, in this case optimised to reduce execution time subject to a given probability distribution of inputs. We demonstrate that our framework is able to produce non-obvious optimizations that compilers are not yet able to generate on eight example functions. We employ a coevolved population of test cases to encourage the preservation of the function's semantics. We exploit the original program both through seeding of the population in order to focus the search, and as an oracle for testing purposes. As well as discussing the issues that arise when attempting to improve software, we employ rigorous experimental method to provide interesting and practical insights to suggest how to address these issues.",
-
notes = "also known as \cite{5688317}
ECJ, C function, java, gnu -o2 gcc, hall of fame. M5 simulator (alpha microprocessor). Functionality 128 times more important than efficiency. Compilation and run time errors give fitness death penalty. Branch coverage. Run time limit. Java overflow. R nnls.
Presented in part at COW50: The 50th CREST Open Workshop - Genetic Improvement http://crest.cs.ucl.ac.uk/cow/50/ http://crest.cs.ucl.ac.uk/cow/50/slides/cow50_Arcuri.pdf http://crest.cs.ucl.ac.uk/cow/50/videos/arcuri_cow50_480p.mp4",
