File:DE Meta-Fitness Landscape (12 benchmark problems).JPG

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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
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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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