  Construct, Merge, Solve and Adapt (CMSA) is a general hybrid metaheuristic
for solving combinatorial optimization problems. At each iteration, CMSA (1)
constructs feasible solutions to the tackled problem instance in a
probabilistic way and (2) solves a reduced problem instance (if possible) to
optimality. The construction of feasible solutions is hereby problem-specific,
usually involving a fast greedy heuristic. The goal of this paper is to design
a problem-agnostic CMSA variant whose exclusive input is an integer linear
program (ILP). In order to reduce the complexity of this task, the current
study is restricted to binary ILPs. In addition to a basic problem-agnostic
CMSA variant, we also present an extended version that makes use of a
constraint propagation engine for constructing solutions. The results show that
our technique is able to match the upper bounds of the standalone application
of CPLEX in the context of rather easy-to-solve instances, while it generally
outperforms the standalone application of CPLEX in the context of hard
instances. Moreover, the results indicate that the support of the constraint
propagation engine is useful in the context of problems for which finding
feasible solutions is rather difficult.
