Automatic workflow composition (AWC) is a relevant problem in automated machine learning (AutoML) that allows finding suitable sequences of preprocessing and prediction models together with their optimal hyperparameters. This problem can be solved using evolutionary algorithms and, in particular, grammar-guided genetic programming (G3P). Current G3P approaches to AWC define a fixed grammar that formally specifies how workflow elements can be combined and which algorithms can be included. In this paper we present \ourmethod, an interactive G3P algorithm that allows users to dynamically modify the grammar to prune the search space and focus on their regions of interest. Our proposal is the first to combine the advantages of a G3P method with ideas from interactive optimisation and human-guided machine learning, an area little explored in the context of AutoML. To evaluate our approach, we present an experimental study in which 20 participants interact with \ourmethod to evolve workflows according to their preferences. Our results confirm that the collaboration between \ourmethod and humans allows us to find high-performance workflows in terms of accuracy that require less tuning time than those found without human intervention.

翻译：自动工作流组合（AWC）是自动化机器学习（AutoML）中的一个关键问题，旨在寻找预处理与预测模型及其最优超参数的有效序列组合。该问题可采用进化算法特别是语法引导遗传编程（G3P）进行求解。当前基于G3P的AWC方法定义固定语法，形式化规定工作流元素的组合方式及可包含的算法。本文提出\ourmethod——一种交互式G3P算法，允许用户动态修改语法以剪枝搜索空间并聚焦感兴趣区域。本方案首次将G3P方法的优势与交互式优化及人类引导机器学习的思想相结合，这是AutoML领域尚未充分探索的研究方向。为评估该方法，我们开展实验研究，邀请20名参与者通过交互\ourmethod根据自身偏好进化工作流。结果表明，\ourmethod与人类的协作能够发现具有更高精度的高性能工作流，且所需的调优时间少于无人类干预的方案。