A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Direct search for optimal parameters within simulation models
ANSS '79 Proceedings of the 12th annual symposium on Simulation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Optimisation and generalisation: footprints in instance space
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Automatic and interactive tuning of algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Statistical analysis of optimization algorithms with R
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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We investigate the adaptability of optimization algorithms for the real-valued case to concrete problems via tuning. However, the focus is not primarily on performance, but on the tuning potential of each algorithm/problem system, for which we define the empirical tuning potential measure (ETP). It is tested if this measure fulfills some trivial conditions for usability, which it does. We also compare the best obtained configurations of 4 adaptable algorithms (2 evolutionary, 2 classic) with classic algorithms under default settings. The overall outcome is quite mixed: Sometimes adapting algorithms is highly profitable, but some problems are already solved to optimality by classic methods.