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