Multi-objective improvement of software using co-evolution and smart seeding

Andrea ARCURI, David Robert WHITE, John CLARK, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

48 Citations (Scopus)


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. © 2008 Springer Berlin Heidelberg.
Original languageEnglish
Title of host publicationSimulated Evolution and Learning : 7th International Conference, SEAL 2008, Melbourne, Australia, December 7-10, 2008, Proceedings
EditorsXiaodong LI, Michael KIRLEY, Mengjie ZHANG, David GREEN, Vic CIESIELSKI, Hussein ABBASS, Zbigniew MICHALEWICZ, Tim HENDTLASS, Kalyanmoy DEB, Kay Chen TAN, Jürgen BRANKE, Yuhui SHI
PublisherSpringer Berlin Heidelberg
Number of pages10
ISBN (Electronic)9783540896944
ISBN (Print)9783540896937
Publication statusPublished - 2008
Externally publishedYes
Event7th International Conference on Simulated Evolution and Learning, SEAL 2008 - Melbourne, Australia
Duration: 7 Dec 200810 Dec 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Simulated Evolution and Learning, SEAL 2008


  • Pareto Front
  • Original Program
  • Gain Score
  • Test Data Generation
  • Strength Pareto Evolutionary Algorithm


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