  We conduct an exhaustive survey of adaptive selection of operators (AOS) in
Evolutionary Algorithms (EAs). We simplified the AOS structure by adding more
components to the framework to built upon the existing categorisation of AOS
methods. In addition to simplifying, we looked at the commonality among AOS
methods from literature to generalise them. Each component is presented with a
number of alternative choices, each represented with a formula. We make three
sets of comparisons. First, the methods from literature are tested on the BBOB
test bed with their default hyper parameters. Second, the hyper parameters of
these methods are tuned using an offline configurator known as IRACE. Third,
for a given set of problems, we use IRACE to select the best combination of
components and tune their hyper parameters.
