File:DE Meta-Fitness Landscape (12 benchmark problems).JPG
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Summary
| Description |
English: Performance landscape showing how basic Differential Evolution (DE) performs in aggregate on several benchmark problems when varying the two DE parameters NP and F, and keeping fixed CR=0.9. Lower meta-fitness values means better DE performance. Such a performance landscape is very time-consuming to compute, especially for optimizers with several behavioural parameters, but it can be searched efficiently using the simple meta-optimization approach by Pedersen implemented in SwarmOps to uncover DE parameters with good performance. |
| Date | |
| Source | Own work |
| Author | Pedersen, M.E.H., Tuning & Simplifying Heuristical Optimization, PhD Thesis, 2010, University of Southampton, School of Engineering Sciences, Computational Engineering and Design Group. |
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I, the copyright holder of this work, release this work into the public domain. This applies worldwide. In some countries this may not be legally possible; if so: I grant anyone the right to use this work for any purpose, without any conditions, unless such conditions are required by law. |
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 09:06, 19 March 2010 | | 1,054 × 694 (145 KB) | Optimering | {{Information |Description={{en|1=Performance landscape showing how basic Differential Evolution (DE) performs in aggregate on several benchmark problems when varying the two DE parameters NP and F, and keeping fixed CR=0.9. Lower meta-fitness values mean |
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