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On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming

机译：论语法引导遗传编程合成的综合性

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Grammar-guided Genetic Programming is a common approach for program synthesis where the user's intent is given by a set of input/output examples. For use in real-world software development, the generated programs must work on previously unseen test cases too. Therefore, we study in this work the generalizability of programs synthesized by grammar-guided GP with lexicase selection. As benchmark, we analyze proportionate and tournament selection too. We find that especially for program synthesis problems with a low output cardinality (e.g., a Boolean output) lexicase selection overfits the training cases and does not generalize well to unseen test cases. An analysis using common software metrics shows for such a problem that lexicase selection generates more complex programs with many code lines and a heavier use of control structures compared to the other studied selection methods. Nevertheless, the generalizability can be improved when we do not stop a GP run as usual after a first program is found that solves all training cases correctly, but give GP more time to find further solution candidates (also solving correctly all training cases) and select the smallest program (measured with different software metrics) out of these.
机译：语法引导遗传编程是程序综合的常见方法，其中用户的意图由一组输入/输出示例给出。对于真实世界的软件开发，生成的程序也必须在以前的看不见的测试用例上工作。因此，我们研究了这项工作，通过语法引导GP与词典选择合成的程序的普遍性。作为基准，我们也分析了比例和锦标赛选择。我们发现，特别是对于低输出基数（例如，布尔输出）词典酶选择的程序综合问题，这些问题会过度培训案例，并且不完全概括到未经证明的测试用例。使用常见软件度量的分析表明，与其他研究方法相比，对词典酶选择产生具有许多代码线的更复杂程序和更重的控制结构的问题。尽管如此，在发现所有培训案例正确地解决所有培训案例后，我们不会停止像往常一样停止GP运行时可以提高概括性。其中最小的程序（用不同的软件度量测量）。

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