Enhancing surrogate model usability for optimisation experts through extended ML support in EvoAl

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

Lizenz

https://creativecommons.org/licenses/by/4.0/

Publication version

publishedVersion

Name

3795101.3814674.pdf

Type

Main Article

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525.88 KB

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Adobe PDF

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Enhancing surrogate model usability for optimisation experts through extended ML support in EvoAl