论文标题
词汇酶选择
Lexicase Selection at Scale
论文作者
论文摘要
词汇酶选择是一种语义感知的父级选择方法，它在随机抛弃的数据流中评估单个测试用例。它在多个研究领域表现出了成功，包括遗传编程，遗传算法以及最近的象征性回归和深度学习。词汇酶选择及其变体的一个潜在缺点是，选择程序需要评估单个数据流中的训练案例，这使得很难处理评估在计算上很重的任务，否则数据集是大规模的，例如深度学习。在这项工作中，我们研究了如何采用加权的洗牌方法来提高词汇酶选择的效率。我们提出了一种新颖的方法，即快速词汇酶选择，该方法结合了词汇酶选择和与部分评估的加权洗牌。对经典基因编程和深度学习任务的实验表明，所提出的方法可以显着减少词汇酶选择以选择个体所需的评估步骤的数量，从而提高其效率，同时保持性能。
Lexicase selection is a semantic-aware parent selection method, which assesses individual test cases in a randomly-shuffled data stream. It has demonstrated success in multiple research areas including genetic programming, genetic algorithms, and more recently symbolic regression and deep learning. One potential drawback of lexicase selection and its variants is that the selection procedure requires evaluating training cases in a single data stream, making it difficult to handle tasks where the evaluation is computationally heavy or the dataset is large-scale, e.g., deep learning. In this work, we investigate how the weighted shuffle methods can be employed to improve the efficiency of lexicase selection. We propose a novel method, fast lexicase selection, which incorporates lexicase selection and weighted shuffle with partial evaluation. Experiments on both classic genetic programming and deep learning tasks indicate that the proposed method can significantly reduce the number of evaluation steps needed for lexicase selection to select an individual, improving its efficiency while maintaining the performance.
