File:DE Meta-Fitness Landscape (Sphere and Rosenbrock).JPG

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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.
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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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current07:33, 19 March 2010Thumbnail for version as of 07:33, 19 March 20101,050 × 698 (158 KB)Optimering (talk | contribs){{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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