  Tuning hyper-parameters for evolutionary algorithms is an important issue in
computational intelligence. Performance of an evolutionary algorithm depends
not only on its operation strategy design, but also on its hyper-parameters.
Hyper-parameters can be categorized in two dimensions as structural/numerical
and time-invariant/time-variant. Particularly, structural hyper-parameters in
existing studies are usually tuned in advance for time-invariant parameters, or
with hand-crafted scheduling for time-invariant parameters. In this paper, we
make the first attempt to model the tuning of structural hyper-parameters as a
reinforcement learning problem, and present to tune the structural
hyper-parameter which controls computational resource allocation in the CEC
2018 winner algorithm by Q-learning. Experimental results show favorably
against the winner algorithm on the CEC 2018 test functions.
