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Surrogate-assisted evolutionary algorithm with stage-adaptive infill sampling criterion for expensive multimodal multi-objective optimization
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AI
DOI：10.1016/j.swevo.2025.102068.png)
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Abstract
En 中文
• A stage-adaptive surrogate-assisted evolutionary algorithm is proposed. • Multiple surrogates are used to reduce prediction errors and guide sampling process. • A stage-adaptive infill criterion is designed to balance convergence and diversity.
Keywords:
surrogate-assisted evolutionary algorithm
multi-surrogate modeling
stage-adaptive infill criterion
convergence
diversity
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