  Evolutionary algorithms, such as Differential Evolution, excel in solving
real-parameter optimization challenges. However, the effectiveness of a single
algorithm varies across different problem instances, necessitating considerable
efforts in algorithm selection or configuration. This paper aims to address the
limitation by leveraging the complementary strengths of a group of algorithms
and dynamically scheduling them throughout the optimization progress for
specific problems. We propose a deep reinforcement learning-based dynamic
algorithm selection framework to accomplish this task. Our approach models the
dynamic algorithm selection a Markov Decision Process, training an agent in a
policy gradient manner to select the most suitable algorithm according to the
features observed during the optimization process. To empower the agent with
the necessary information, our framework incorporates a thoughtful design of
landscape and algorithmic features. Meanwhile, we employ a sophisticated deep
neural network model to infer the optimal action, ensuring informed algorithm
selections. Additionally, an algorithm context restoration mechanism is
embedded to facilitate smooth switching among different algorithms. These
mechanisms together enable our framework to seamlessly select and switch
algorithms in a dynamic online fashion. Notably, the proposed framework is
simple and generic, offering potential improvements across a broad spectrum of
evolutionary algorithms. As a proof-of-principle study, we apply this framework
to a group of Differential Evolution algorithms. The experimental results
showcase the remarkable effectiveness of the proposed framework, not only
enhancing the overall optimization performance but also demonstrating favorable
generalization ability across different problem classes.
