  In many practical applications, usually, similar optimisation problems or
scenarios repeatedly appear. Learning from previous problem-solving experiences
can help adjust algorithm components of meta-heuristics, e.g., adaptively
selecting promising search operators, to achieve better optimisation
performance. However, those experiences obtained from previously solved
problems, namely offline experiences, may sometimes provide misleading
perceptions when solving a new problem, if the characteristics of previous
problems and the new one are relatively different. Learning from online
experiences obtained during the ongoing problem-solving process is more
instructive but highly restricted by limited computational resources. This
paper focuses on the effective combination of offline and online experiences. A
novel hybrid framework that learns to dynamically and adaptively select
promising search operators is proposed. Two adaptive operator selection modules
with complementary paradigms cooperate in the framework to learn from offline
and online experiences and make decisions. An adaptive decision policy is
maintained to balance the use of those two modules in an online manner.
Extensive experiments on 170 widely studied real-value benchmark optimisation
problems and a benchmark set with 34 instances for combinatorial optimisation
show that the proposed hybrid framework outperforms the state-of-the-art
methods. Ablation study verifies the effectiveness of each component of the
framework.
