Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity
Created by W.Langdon from
gp-bibliography.bib Revision:1.7686
- @Article{BARBUDO:2024:asoc,
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author = "Rafael Barbudo and Aurora Ramirez and
Jose Raul Romero",
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title = "Grammar-based evolutionary approach for automated
workflow composition with domain-specific operators and
ensemble diversity",
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journal = "Applied Soft Computing",
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volume = "153",
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pages = "111292",
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year = "2024",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2024.111292",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494624000668",
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keywords = "genetic algorithms, genetic programming, AutoML,
Automated workflow composition, Algorithm selection,
Hyper-parameter optimisation, Grammar-guided genetic
programming, Ensemble learning, Classification",
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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",
- }
Genetic Programming entries for
Rafael Barbudo Lunar
Aurora Ramirez Quesada
Jose Raul Romero Salguero
Citations