  The development, assessment, and comparison of randomized search algorithms
heavily rely on benchmarking. Regarding the domain of constrained optimization,
the number of currently available benchmark environments bears no relation to
the number of distinct problem features. The present paper advances a proposal
of a scalable linear constrained optimization problem that is suitable for
benchmarking Evolutionary Algorithms. By comparing two recent EA variants, the
linear benchmarking environment is demonstrated.
