  Automating the design of heuristic search methods is an active research field
within computer science, artificial intelligence and operational research. In
order to make these methods more generally applicable, it is important to
eliminate or reduce the role of the human expert in the process of designing an
effective methodology to solve a given computational search problem.
Researchers developing such methodologies are often constrained on the number
of problem domains on which to test their adaptive, self-configuring
algorithms; which can be explained by the inherent difficulty of implementing
their corresponding domain specific software components.
  This paper presents HyFlex, a software framework for the development of
cross-domain search methodologies. The framework features a common software
interface for dealing with different combinatorial optimisation problems, and
provides the algorithm components that are problem specific. In this way, the
algorithm designer does not require a detailed knowledge the problem domains,
and thus can concentrate his/her efforts in designing adaptive general-purpose
heuristic search algorithms. Four hard combinatorial problems are fully
implemented (maximum satisfiability, one dimensional bin packing, permutation
flow shop and personnel scheduling), each containing a varied set of instance
data (including real-world industrial applications) and an extensive set of
problem specific heuristics and search operators. The framework forms the basis
for the first International Cross-domain Heuristic Search Challenge (CHeSC),
and it is currently in use by the international research community. In summary,
HyFlex represents a valuable new benchmark of heuristic search generality, with
which adaptive cross-domain algorithms are being easily developed, and reliably
compared.

Artificial Intelligence