  Optimisation problems, particularly combinatorial optimisation problems, are
difficult to solve due to their complexity and hardness. Such problems have
been successfully solved by evolutionary and swarm intelligence algorithms,
especially in binary format. However, the approximation may suffer due to the
the issues in balance between exploration and exploitation activities (EvE),
which remain as the major challenge in this context. Although the complementary
usage of multiple operators is becoming more popular for managing EvE with
adaptive operator selection schemes, a bespoke adaptive selection system is
still an important topic in research. Reinforcement Learning (RL) has recently
been proposed as a way to customise and shape up a highly effective adaptive
selection system. However, it is still challenging to handle the problem in
terms of scalability. This paper proposes and assesses a RL-based novel
approach to help develop a generalised framework for gaining, processing, and
utilising the experiences for both the immediate and future use. The
experimental results support the proposed approach with a certain level of
success.
