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.
翻译:立国法选择是一种具有语义意识的母体选择方法,该方法在随机拼凑的数据流中评估单项测试案例,在遗传方案、遗传算法以及最近象征性回归和深层学习等多个研究领域都取得了成功。 立国法选择及其变体的一个潜在缺点是,选择程序要求在一个数据流中评估培训案例,使得难以处理评估量过重或数据集规模大的任务,例如深层学习。 在这项工作中,我们调查如何使用加权打拼方法提高单国法选择的效率。我们提出了一个新颖的方法,即快速选择单国法,其中包括单国法选择和部分评价。对典型的遗传方案和深层学习任务进行的实验表明,拟议的方法可以大大减少选择个人需要的单国法评估步骤的数量,在保持业绩的同时提高其效率。