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| Keywords: DE Meta-Fitness Landscape (Sphere and Rosenbrock).JPG en 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 http //www hvass-labs org/projects/swarmops/ SwarmOps to uncover DE parameters with good performance own Pedersen M E H http //www hvass-labs org/people/magnus/thesis/pedersen08thesis pdf Tuning Simplifying Heuristical Optimization PhD Thesis 2010 University of Southampton School of Engineering Sciences Computational Engineering and Design Group 2010-01-01 Evolutionary algorithms Optimization Benchmarks | ||||
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