Combining global and local surrogate models to accelerate evolutionary optimization
In this paper, we present a novel surrogate-assisted evolutionary optimization framework for solving computationally expensive problems. The proposed framework uses computationally cheap hierarchical surrogate models constructed through online learning to replace the exact computationally expensive...
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| Main Authors: | , , , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2021
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/147970 |
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| 機構: | Nanyang Technological University |
| 語言: | English |