On optimal static and dynamic parameter choices for fixed-target optimization

Various flavours of parameter setting, such as (static) parameter tuning and (dynamic) parameter control, receive a lot of attention both in applications and in theoretical investigations. It is widely acknowledged that the choice of parameter values influences the efficiency of evolutionary algorithms (and other search heuristics) a lot, and a considerable amount of work has been dedicated to finding (near-)optimal choices of parameters, or of parameter control strategies. It is perhaps surprising that all the recent theoretic attempts aim at making smaller the time needed to reach the optimum, whereas in most practical settings we may only hope to reach certain realistic fitness targets. One of the ways to close this gap is to study static and dynamic parameter choices in fixed-target settings, and to understand how these choices are different from those tuned towards reaching the optimum. In this paper we investigate some of these settings, using a mixture of exact theory-driven computations and experimental evaluation, and find few remarkably generic trends, some of which may explain a number of misconceptions found in evolutionary computation.