Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings
Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings
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基因编程 - 第 23 届欧洲会议，EuroGP 2020，作为 EvoStar 2020 的一部分，于 2020 年 4 月 15-17 日在西班牙塞维利亚举行，会议记录
DOI：
10.1007/978-3-030-44094-7_5
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发表时间：
2020
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影响因子：
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通讯作者：
Blot A
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文献类型：
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作者：
Blot A
遗传改良（GI）使用自动搜索来找到现有软件的改进版本。虽然大多数GI工作使用遗传编程（GP）作为基本的搜索过程，但通常只关注目标软件。因此，用于GI的GP算法的细节没有被很好地理解，并且很少相互比较。在这项工作中，我们提出了一个强大的实验协议来比较不同的GI搜索过程，并调查了几个变种的GP和随机为基础的方法。通过反复的实验，我们报告了这些方法的比较分析，使用以前使用的GI方案之一：改进的MiniSAT可满足性求解器的运行时间。我们得出结论，所使用的测试套件对GI结果的影响最显著。随机和基于GP的方法都能够找到改进的软件，即使在随机情况下可行的软件变体的百分比显着较小（与）。我们还报告说，GI产生MiniSAT变种的速度高达原来的两倍，从同一个应用程序域的集合以前看不见的实例。
Genetic improvement (GI) uses automated search to find improved versions of existing software. While most GI work use genetic programming (GP) as the underlying search process, focus is usually given to the target software only. As a result, specifics of GP algorithms for GI are not well understood and rarely compared to one another. In this work, we propose a robust experimental protocol to compare different GI search processes and investigate several variants of GP- and random-based approaches. Through repeated experiments, we report a comparative analysis of these approaches, using one of the previously used GI scenarios: improvement of runtime of the MiniSAT satisfiability solver. We conclude that the test suites used have the most significant impact on the GI results. Both random and GP-based approaches are able to find improved software, even though the percentage of viable software variants is significantly smaller in the random case (vs.). We also report that GI produces MiniSAT variants up to twice as fast as the original on sets of previously unseen instances from the same application domain.