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Publication Detail
Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class
  • Publication Type:
    Conference
  • Authors:
    Langdon W, Petke J, Harman M, Weimer W
  • Publisher:
    Springer Verlag
  • Publication date:
    2014
  • Place of publication:
    Berlin/Heidelberg, Germany
  • Pagination:
    137, 149
  • Published proceedings:
    Proceedings of the 17th European Conference on Genetic Programming, EuroGP 2014
  • Volume:
    8599
  • Series:
    Lecture Notes in Computer Science
  • Editors:
    Heywood M,Nicolau M,Krawiec K
  • ISBN-13:
    9783662443026
  • Status:
    Published
  • Name of conference:
    EuroGP 2014: 17th European Conference on Genetic Programming
  • Conference place:
    Granada, Spain
  • Conference start date:
    23/04/2014
  • Conference finish date:
    25/04/2014
  • Language:
    English
  • Keywords:
    Genetic improvement, code transplants, code specialisation, Boolean satisfiability
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.
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