Comparing Genetic Programming Approaches for Non-functional Genetic Improvement
Comparing Genetic Programming Approaches for Non-functional Genetic Improvement
复制标题
比较非功能性遗传改良的遗传编程方法
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
J. Petke
中科院分区：
文献类型：
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作者：
Aymeric Blot;J. Petke
遗传改进（GI）使用自动搜索来找到现有软件的改进版本。尽管大多数GI工作都使用基因编程（GP）作为基础搜索过程，但通常仅将重点放在目标软件上。结果，GP算法的GI细节尚未得到很好的理解，很少相比。在这项工作中，我们提出了一个强大的实验协议，以比较不同的GI搜索过程并研究基于GP和随机方法的几种变体。通过重复实验，我们使用先前使用的GI方案之一报告了这些方法的比较分析：Minisat Esclibility solver的运行时改善。我们得出的结论是，所使用的测试套件对GI结果具有最大的影响。尽管在随机情况下（（14.5 \％）vs.（80.1 \％）），基于随机和基于GP的方法都能找到改进的软件，即使可行软件变体的百分比明显较小。我们还报告，GI产生的微型变体最大值是从同一应用程序域中的原始实例集的原始速度的两倍。
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 ((14.5\%) vs. (80.1\%)). 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.
PyGGI 2.0：独立于语言的遗传改进框架
DOI：
10.1145/3338906.3341184
发表时间：
2019
期刊：
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影响因子：
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作者：
An G
通讯作者：
An G
影响因子：
14.3
作者：
Petke J
通讯作者：
Petke J