Keywords: DE Meta-Fitness Landscape (12 benchmark problems).JPG en 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 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 |