Evolutionary optimization for computationally expensive problems
Despite all the appealing features of Evolutionary Algorithms (EAs), thousands of calls to the analysis or simulation codes are often required to locate a near optimal solution. Two major solutions for this issue are: 1) to use computationally less expensive surrogate models, and 2) to use parallel...
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| Format: | Thesis |
| Language: | English |
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2009
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| Online Access: | https://hdl.handle.net/10356/19262 |