Optimizing Existing Software with Genetic Programming
Optimizing Existing Software with Genetic Programming
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DOI：
10.1109/tevc.2013.2281544
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发表时间：
2015-02-01
影响因子：
14.3
通讯作者：
Harman, Mark
中科院分区：
文献类型：
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作者：
Langdon, William B.;Harman, Mark
我们表明，程序的遗传改善（GIP）可以通过在广泛使用且高度复杂的50 000线系统中发展性能来扩展。用于多个客观探索的软件（GISMOE）的遗传改进发现了代码的70倍（平均而言），但至少在功能上也是如此。确实，它甚至给出了很小的语义增益。
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.