面向程序综合的有效自适应变异率
Effective Adaptive Mutation Rates for Program Synthesis
AI总结：
针对程序综合中进化算法变异率超参数难调、现有自适应方案易出现变异率消失问题，提出基于多臂老虎机的自适应变异率方案，免去手动指定变异率，经软件综合与符号回归任务验证有效。
摘要：
许多进化算法（包括用于程序综合的遗传编程系统）的问题求解性能，取决于变异率等超参数的取值。目前在软件综合基准问题上取得部分最优结果的变异方法——增减统一变异（Uniform Mutation by Addition and Deletion，UMAD），会以预设速率向基因组中加入新基因，再以与加入速率平衡的速率删除基因，平均而言基因组大小不会发生变化。尽管采用预设加入速率的UMAD性能优于许多其他变异和交叉方案，但我们并不认为单一速率在进化系统单次运行的所有问题或所有世代中都是最优的。然而，当前许多自适应变异方案（如自适应变异率）存在诸如变异率消失问题之类的缺陷，即变异率会快速衰减至零。我们提出了一种基于多臂老虎机的自适应方案来解决该问题，且该方案从根本上免去了指定变异率的需求。尽管所提方案本身也引入了超参数，但我们要么将其设置为较优值，要么在合理范围内对其进行集成。在软件综合和符号回归问题上的实验结果验证了我们方法的有效性。
英文摘要：
The problem-solving performance of many evolutionary algorithms, including genetic programming systems used for program synthesis, depends on the values of hyperparameters including mutation rates. The mutation method used to produce some of the best results to date on software synthesis benchmark problems, Uniform Mutation by Addition and Deletion (UMAD), adds new genes into a genome at a predetermined rate and then deletes genes at a rate that balances the addition rate, producing no size change on average. While UMAD with a predetermined addition rate outperforms many other mutation and crossover schemes, we do not expect a single rate to be optimal across all problems or all generations within one run of an evolutionary system. However, many current adaptive mutation schemes such as self-adaptive mutation rates suffer from pathologies like the vanishing mutation rate problem, in which the mutation rate quickly decays to zero. We propose an adaptive bandit-based scheme that addresses this problem and essentially removes the need to specify a mutation rate. Although the proposed scheme itself introduces hyperparameters, we either set these to good values or ensemble them in a reasonable range. Results on software synthesis and symbolic regression problems validate the effectiveness of our approach.
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