Adaptability of Algorithms for Real-Valued Optimization

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.

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