论文标题
一种自适应且接近无参数的进化计算方法，用于自动化的真实自动化
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML
论文作者
论文摘要
进化计算方法的普遍主张是，它们可以在不需要人类干预的情况下取得良好的结果。但是，对此的一种批评是，仍然有一些超级参数必须调整以实现良好的表现。在这项工作中，我们提出了一种近乎“无参数”的遗传编程方法，该方法可以适应整个演化的超参数值，而无需手动指定。我们将其应用于自动化机器学习（通过扩展TPOT）的领域，以产生可以有效地声称没有人类输入的管道，并表明结果与使用手工选择的超参数值的现有最新ART竞争。管道以随机选择的估计器开头，并演变为自动竞争管道。这项工作朝着真正的自动方法进行汽车迈进。
A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near "parameter-free" genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automatic approach to AutoML.
