应用多目标遗传算法在程序生成中的高效选择

Hiroto Watanabe, S. Matsumoto, Yoshiki Higo, S. Kusumoto, Toshiyuki Kurabayashi, Hiroyuki Kirinuki, Haruto Tanno
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引用次数: 0

摘要

自动程序生成(APG)是自动生成计算机程序的概念。为了实现这一目标,可以考虑将自动程序修复(APR)转换为APG。APR修改有bug的输入源代码以通过所有的测试用例。APG将空源代码视为最初所有测试用例失败,即包含多个错误。基于搜索的APR反复生成程序变量并对其进行评估。许多传统的APR系统基于通过测试用例的数量来评估变量的适应度。但是,当源代码包含多个错误时,该适应度函数缺乏变体的表达能力。本文提出将多目标遗传算法应用于APR,以提高效率。我们还提出了一种新的交叉方法,将两个变量与互补的测试结果结合起来,利用多目标遗传算法的高表达能力进行评估。我们测试了该方法在竞争性规划任务中的有效性。得到的结果表明,在成功试验次数和所需的生成时间上存在显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Multi-Objective Genetic Algorithm for Efficient Selection on Program Generation
Automated program generation (APG) is a concept of automatically making a computer program. Toward this goal, transferring automated program repair (APR) to APG can be considered. APR modifies the buggy input source code to pass all test cases. APG regards empty source code as initially failing all test cases, i.e., containing multiple bugs. Search-based APR repeatedly generates program variants and evaluates them. Many traditional APR systems evaluate the fitness of variants based on the number of passing test cases. However, when source code contains multiple bugs, this fitness function lacks the expressive power of variants. In this paper, we propose the application of a multi-objective genetic algorithm to APR in order to improve efficiency. We also propose a new crossover method that combines two variants with complementary test results, taking advantage of the high expressive power of multi-objective genetic algorithms for evaluation. We tested the effectiveness of the proposed method on competitive programming tasks. The obtained results showed significant differences in the number of successful trials and the required generation time.
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