  We consider how an (almost) optimal parameter adaptation process for an
adaptive DE might behave, and compare the behavior and performance of this
approximately optimal process to that of existing, adaptive mechanisms for DE.
An optimal parameter adaptation process is an useful notion for analyzing the
parameter adaptation methods in adaptive DE as well as other adaptive
evolutionary algorithms, but it cannot be known generally. Thus, we propose a
Greedy Approximate Oracle method (GAO) which approximates an optimal parameter
adaptation process. We compare the behavior of GAODE, a DE algorithm with GAO,
to typical adaptive DEs on six benchmark functions and the BBOB benchmarks, and
show that GAO can be used to (1) explore how much room for improvement there is
in the performance of the adaptive DEs, and (2) obtain hints for developing
future, effective parameter adaptation methods for adaptive DEs.
