Multi-Objective Improvement of Software using Co-evolution and Smart Seeding

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{ArcuriWCY08,

• author = "Andrea Arcuri and David Robert White and John Clark and Xin Yao",
• title = "Multi-Objective Improvement of Software using Co-evolution and Smart Seeding",
• booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)",
• year = "2008",
• editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi",
• volume = "5361",
• series = "Lecture Notes in Computer Science",
• pages = "61--70",
• address = "Melbourne, Australia",
• month = dec # " 7-10",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, SBSE",
• bibsource = "http://crestweb.cs.ucl.ac.uk/resources/sbse_repository/repository.html",
• isbn13 = "978-3-540-89693-7",
• DOI = "doi:10.1007/978-3-540-89694-4_7",
• size = "10 pages",
• abstract = "Optimising non-functional properties of software is an important part of the implementation process. One such property is execution time, and compilers target a reduction in execution time using a variety of optimisation techniques. Compiler optimisation is not always able to produce semantically equivalent alternatives that improve execution times, even if such alternatives are known to exist. Often, this is due to the local nature of such optimisations. In this paper we present a novel framework for optimising existing software using a hybrid of evolutionary optimisation techniques. Given as input the implementation of a program or function, we use Genetic Programming to evolve a new semantically equivalent version, optimised to reduce execution time subject to a given probability distribution of inputs. We employ a co-evolved population of test cases to encourage the preservation of the program's semantics, and exploit the original program through seeding of the population in order to focus the search. We carry out experiments to identify the important factors in maximising efficiency gains. Although in this work we have optimised execution time, other non-functional criteria could be optimised in a similar manner.",
• notes = "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}",
• bibsource = "DBLP, http://dblp.uni-trier.de",

}

Genetic Programming entries for Andrea Arcuri David Robert White John A Clark Xin Yao

Citations