- author = "Rafael Barbudo and Aurora Ramirez and Jose Raul Romero",
- title = "Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity",
- journal = "Applied Soft Computing",
- year = "2024",
- volume = "153",
- pages = "111292",
- keywords = "genetic algorithms, genetic programming, AutoML, Automated workflow composition, Algorithm selection, Hyper-parameter optimisation, Grammar-guided genetic programming, Ensemble learning, Classification",
- ISSN = "1568-4946",
-
URL = "
https://www.sciencedirect.com/science/article/pii/S1568494624000668",
-
DOI = "
doi:10.1016/j.asoc.2024.111292",
- size = "14 pages",
-
abstract = "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
machine learning 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 in predictive performance. Additionally, EvoFlow is capable of discovering workflow structures that other approaches in the literature have not considered",
