A survey of genetic improvement search spaces
A survey of genetic improvement search spaces
复制标题
遗传改良搜索空间调查
DOI：
10.1145/3319619.3326870
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
D. White
中科院分区：
文献类型：
--
作者：
J. Petke;Brad J. Alexander;Earl T. Barr;A. Brownlee;Markus Wagner;D. White
遗传改进（GI）使用自动搜索来改善现有软件。大多数GI工作都集中在实证研究上，这些研究成功地将GI应用于改善软件的运行时间，修复错误，添加新功能等。关于GI为什么如此成功的研究很少。例如，遗传编程是GI中最常用的搜索算法。基因编程是GI的最佳选择吗？最初尝试回答这个问题的尝试探索了GI的突变搜索空间。本文总结了迄今为止在此问题上发布的工作。
Genetic Improvement (GI) uses automated search to improve existing software. Most GI work has focused on empirical studies that successfully apply GI to improve software's running time, fix bugs, add new features, etc. There has been little research into why GI has been so successful. For example, genetic programming has been the most commonly applied search algorithm in GI. Is genetic programming the best choice for GI? Initial attempts to answer this question have explored GI's mutation search space. This paper summarises the work published on this question to date.
影响因子：
14.3
作者：
Petke J
通讯作者：
Petke J