  Differential evolution is one of the most prestigious population-based
stochastic optimization algorithm for black-box problems. The performance of a
differential evolution algorithm depends highly on its mutation and crossover
strategy and associated control parameters. However, the determination process
for the most suitable parameter setting is troublesome and time-consuming.
Adaptive control parameter methods that can adapt to problem landscape and
optimization environment are more preferable than fixed parameter settings.
This paper proposes a novel adaptive parameter control approach based on
learning from the optimization experiences over a set of problems. In the
approach, the parameter control is modeled as a finite-horizon Markov decision
process. A reinforcement learning algorithm, named policy gradient, is applied
to learn an agent (i.e. parameter controller) that can provide the control
parameters of a proposed differential evolution adaptively during the search
procedure. The differential evolution algorithm based on the learned agent is
compared against nine well-known evolutionary algorithms on the CEC'13 and
CEC'17 test suites. Experimental results show that the proposed algorithm
performs competitively against these compared algorithms on the test suites.
