A semi-supervised learning technique assisted multi-objective evolutionary algorithm for computationally expensive problems
Abstract Existing multi-objective evolutionary algorithms (MOEAs) have demonstrated excellent efficiency when tackling multi-objective tasks. However, its use in computationally expensive multi-objective issues is hindered by the large number of reliable evaluations needed to find Pareto-optimal sol...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-01-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01715-6 |
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