  This paper proposes the first-ever algorithmic framework for tuning
hyper-parameters of stochastic optimization algorithm based on reinforcement
learning. Hyper-parameters impose significant influences on the performance of
stochastic optimization algorithms, such as evolutionary algorithms (EAs) and
meta-heuristics. Yet, it is very time-consuming to determine optimal
hyper-parameters due to the stochastic nature of these algorithms. We propose
to model the tuning procedure as a Markov decision process, and resort the
policy gradient algorithm to tune the hyper-parameters. Experiments on tuning
stochastic algorithms with different kinds of hyper-parameters (continuous and
discrete) for different optimization problems (continuous and discrete) show
that the proposed hyper-parameter tuning algorithms do not require much less
running times of the stochastic algorithms than bayesian optimization method.
The proposed framework can be used as a standard tool for hyper-parameter
tuning in stochastic algorithms.
