File:DE Meta-Fitness Landscape (Sphere and Rosenbrock).JPG
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Summary
| Description |
English: Performance landscape showing how basic Differential Evolution (DE) performs in aggregate on the Sphere and Rosenbrock 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 | 07:33, 19 March 2010 | | 1,050 × 698 (158 KB) | Optimering | {{Information |Description={{en|1=Performance landscape showing how basic Differential Evolution (DE) performs in aggregate on the Sphere and Rosenbrock benchmark problems when varying the two DE parameters NP and F, and keeping fixed CR=0.9. Lower meta-f |
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