  We address a family of hard benchmark instances for the Simple Plant Location
Problem (also known as the Uncapacitated Facility Location Problem). The recent
attempt by Fischetti et al. to tackle the K\"orkel-Ghosh instances resulted in
seven new optimal solutions and 22 improved upper bounds. We use automated
generation of heuristics to obtain a new algorithm for the Simple Plant
Location Problem. In our experiments, our new algorithm matched all the
previous best known and optimal solutions, and further improved 12 upper
bounds, all within shorter time budgets compared to the previous efforts.
  Our algorithm design process is split into two phases: (i) development of
algorithmic components such as local search procedures and mutation operators,
and (ii) composition of a metaheuristic from the available components. Phase
(i) requires human expertise and often can be completed by implementing several
simple domain-specific routines known from the literature. Phase (ii) is
entirely automated by employing the Conditional Markov Chain Search (CMCS)
framework. In CMCS, a metaheuristic is flexibly defined by a set of parameters,
called configuration. Then the process of composition of a metaheuristic from
the algorithmic components is reduced to an optimisation problem seeking the
best performing CMCS configuration.
  We discuss the problem of comparing configurations, and propose a new
efficient technique to select the best performing configuration from a large
set. To employ this method, we restrict the original CMCS to a simple
deterministic case that leaves us with a finite and manageable number of
meaningful configurations.
