  Augmenting a base constraint model with additional constraints can strengthen
the inferences made by a solver and therefore reduce search effort. We focus on
the automatic addition of streamliner constraints, derived from the types
present in an abstract Essence specification of a problem class of interest,
which trade completeness for potentially very significant reduction in search.
The refinement of streamlined Essence specifications into constraint models
suitable for input to constraint solvers gives rise to a large number of
modelling choices in addition to those required for the base Essence
specification. Previous automated streamlining approaches have been limited in
evaluating only a single default model for each streamlined specification. In
this paper we explore the effect of model selection in the context of
streamlined specifications. We propose a new best-first search method that
generates a portfolio of Pareto Optimal streamliner-model combinations by
evaluating for each streamliner a portfolio of models to search and explore the
variability in performance and find the optimal model. Various forms of racing
are utilised to constrain the computational cost of training.
