Grammar-based evolutionary approach for automated workflow composition
  with domain-specific operators and ensemble diversity
Rafael Barbudo Aurora Ramírez José Raúl Romero
The process of extracting valuable and novel insights from raw data involves
a series of complex steps. In the realm of Automated Machine Learning (AutoML),
a significant research focus is on automating aspects of this process,
specifically tasks like selecting algorithms and optimising their
hyper-parameters. A particularly challenging task in AutoML is automatic
workflow composition (AWC). AWC aims to identify the most effective sequence of
data preprocessing and ML algorithms, coupled with their best hyper-parameters,
for a specific dataset. However, existing AWC methods are limited in how many
and in what ways they can combine algorithms within a workflow.
  Addressing this gap, this paper introduces EvoFlow, a grammar-based
evolutionary approach for AWC. EvoFlow enhances the flexibility in designing
workflow structures, empowering practitioners to select algorithms that best
fit their specific requirements. EvoFlow stands out by integrating two
innovative features. First, it employs a suite of genetic operators, designed
specifically for AWC, to optimise both the structure of workflows and their
hyper-parameters. Second, it implements a novel updating mechanism that
enriches the variety of predictions made by different workflows. Promoting this
diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow
builds an ensemble whose workflows differ in their misclassified instances.
  To evaluate EvoFlow's effectiveness, we carried out empirical validation
using a set of classification benchmarks. We begin with an ablation study to
demonstrate the enhanced performance attributable to EvoFlow's unique
components. Then, we compare EvoFlow with other AWC approaches, encompassing
both evolutionary and non-evolutionary techniques. Our findings show that
EvoFlow's specialised genetic operators and updating mechanism substantially
outperform current leading methods[..]