  Context: Evolutionary algorithms typically require a large number of
evaluations (of solutions) to converge - which can be very slow and expensive
to evaluate.Objective: To solve search-based software engineering (SE)
problems, using fewer evaluations than evolutionary methods.Method: Instead of
mutating a small population, we build a very large initial population which is
then culled using a recursive bi-clustering chop approach. We evaluate this
approach on multiple SE models, unconstrained as well as constrained, and
compare its performance with standard evolutionary algorithms. Results: Using
just a few evaluations (under 100), we can obtain comparable results to
state-of-the-art evolutionary algorithms.Conclusion: Just because something
works, and is widespread use, does not necessarily mean that there is no value
in seeking methods to improve that method. Before undertaking search-based SE
optimization tasks using traditional EAs, it is recommended to try other
techniques, like those explored here, to obtain the same results with fewer
evaluations.
