  Self-adaptive systems frequently use tactics to perform adaptations. Tactic
examples include the implementation of additional security measures when an
intrusion is detected, or activating a cooling mechanism when temperature
thresholds are surpassed. Tactic volatility occurs in real-world systems and is
defined as variable behavior in the attributes of a tactic, such as its latency
or cost. A system's inability to effectively account for tactic volatility
adversely impacts its efficiency and resiliency against the dynamics of
real-world environments. To enable systems' efficiency against tactic
volatility, we propose a Tactic Volatility Aware (TVA-E) process utilizing
evolved Recurrent Neural Networks (eRNN) to provide accurate tactic
predictions. TVA-E is also the first known process to take advantage of
uncertainty reduction tactics to provide additional information to the
decision-making process and reduce uncertainty. TVA-E easily integrates into
popular adaptation processes enabling it to immediately benefit a large number
of existing self-adaptive systems. Simulations using 52,106 tactic records
demonstrate that: I) eRNN is an effective prediction mechanism, II) TVA-E
represents an improvement over existing state-of-the-art processes in
accounting for tactic volatility, and III) Uncertainty reduction tactics are
beneficial in accounting for tactic volatility. The developed dataset and tool
can be found at https://tacticvolatility.github.io/
