Optimizing Existing Software with Genetic Programming

被引：115

作者：

Langdon, William B. ^{[1
]}

Harman, Mark ^{[1
]}

机构：

[1] Univ London Univ Coll, Dept Comp Sci, London WC1E 6BT, England

来源：

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | 2015年 / 19卷 / 01期

基金：

英国工程与自然科学研究理事会;

关键词：

Automatic software reengineering; Bowtie2(GP); genetic programming (GP); multiple objective exploration; search based software engineering (SBSE);

D O I：

10.1109/TEVC.2013.2281544

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We show that the genetic improvement of programs (GIP) can scale by evolving increased performance in a widely-used and highly complex 50 000 line system. Genetic improvement of software for multiple objective exploration (GISMOE) found code that is 70 times faster (on average) and yet is at least as good functionally. Indeed, it even gives a small semantic gain.

引用

页码：118 / 135

页数：18

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