  The generalization abilities of heuristic optimizers may deteriorate with the
increment of the search space dimensionality. To achieve generalized
performance across Large Scale Blackbox Optimization (LSBO) tasks, it
ispossible to ensemble several heuristics and devise a meta-heuristic to
control their initiation. This paper first proposes a methodology of
transforming LSBO problems into online decision processes to maximize
efficiency of resource utilization. Then, using the perspective of multi-armed
bandits with non-stationary reward distributions, we propose a meta-heuristic
based on Temporal Estimation of Rewards (TER) to address such decision process.
TER uses a window for temporal credit assignment and Boltzmann exploration to
balance the exploration-exploitation tradeoff. The prior-free TER generalizes
across LSBO tasks with flexibility for different types of limited computational
resources (e.g. time, money, etc.) and is easy to be adapted to new tasks for
its simplicity and easy interface for heuristic articulation. Tests on the
benchmarks validate the problem formulation and suggest significant
effectiveness: when TER is articulated with three heuristics, competitive
performance is reported across different sets of benchmark problems with search
dimensions up to 10000.
