Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms

Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) are widely used to solve expensive optimization problems where evaluating candidate solutions is computationally intensive. To reduce this cost, SAEAs employ surrogate models—machine learning models that approximate expensive evaluation functions. W...

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Bibliographic Details
Main Authors: Yuki Hanawa, Tomohiro Harada, Yukiya Miura
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Array
Subjects:
Surrogate-assisted evolutionary algorithms
Prediction accuracy
Performance analysis
Model management strategy
Online Access:http://www.sciencedirect.com/science/article/pii/S2590005625000888
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http://www.sciencedirect.com/science/article/pii/S2590005625000888

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