Enhancing surrogate model usability for optimisation experts through extended ML support in EvoAl
Citation Link: https://doi.org/10.15480/882.18312
Publikationstyp
Conference Paper
Date Issued
2026-08-13
Sprache
English
TORE-DOI
Start Page
1499
End Page
1502
Citation
Genetic and Evolutionary Computation Conference, GECCO 2026
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Association for Computing Machinery (ACM)
ISBN of container
979-840072488-6
Surrogate models are a crucial aspect in many real-world application optimisation problems. Thus, optimisation experts do not only have to focus on the optimisation problem at hand, but also work on selecting and configuring appropriate models. This may be challenging, when no extensive expertise in machine learning (ML) is present. To address this issue, we extend the open-source data science research tool EvoAl to better support optimisation practitioners in focusing on their core tasks. Specifically, we enhance EvoAl’s machine learning language to accommodate the growing complexity of surrogate modelling workflows. Our contributions include expanding the range of available models, introducing runtime validation of model configurations, and enabling the integration of pre-trained models via ONNX. These improvements lower the barrier to applying advanced ML techniques in optimisation, thereby facilitating more efficient and robust surrogate-based optimisation in practical applications.
Subjects
domain-specific languages
evolutionary algorithm
surrogate model
DDC Class
006.31: Machine Learning
Publication version
publishedVersion