Genetic improvement (GI) uses automated search to improve existing software. It has been successfully used to optimize various program properties, such as runtime or energy consumption, as well as for the purpose of bug fixing. GI typically navigates a space of thousands of patches in search for the program mutation that best improves the desired software property. While genetic programming (GP) has been dominantly used as the search strategy, more recently other search strategies, such as local search, have been tried. It is, however, still unclear which strategy is the most effective and efficient. In this article, we conduct an in-depth empirical comparison of a total of 18 search processes using a set of eight improvement scenarios. Additionally, we also provide new GI benchmarks and we report on new software patches found. Our results show that, overall, local search approaches achieve better effectiveness and efficiency than GP approaches. Moreover, improvements were found in all scenarios (between 15% and 68%). A replication package can be found online: https://github.com/bloa/tevc_2020_artefact .

中文翻译：

---

用于软件遗传改进的搜索启发式的实证比较

遗传改进 (GI) 使用自动搜索来改进现有软件。它已成功用于优化各种程序属性，例如运行时间或能耗，以及用于错误修复。GI 通常会在数千个补丁的空间中导航，以寻找最能改善所需软件属性的程序变异。虽然遗传编程 (GP) 已被主要用作搜索策略，但最近也尝试了其他搜索策略，例如本地搜索。然而，目前尚不清楚哪种策略最有效和最有效。在本文中，我们使用一组八个改进方案对总共 18 个搜索过程进行了深入的实证比较。此外，我们还提供新的 GI 基准测试，并报告发现的新软件补丁。我们的结果表明，总体而言，局部搜索方法比 GP 方法实现了更好的有效性和效率。此外，在所有场景中都发现了改进（在 15% 到 68% 之间）。可以在网上找到复制包：https://github.com/bloa/tevc_2020_artefact .