Beyond evolutionary algorithms for search-based software engineering

Abstract

ContextEvolutionary algorithms typically require large number of evaluations (of solutions) to converge – which can be very slow and expensive to evaluate. ObjectiveTo solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods. MethodInstead 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. ResultsUsing just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms. ConclusionJust 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.