Beyond Evolutionary Algorithms for Search-based Software Engineering
This addresses the efficiency issue for software engineers performing optimization tasks, though it is incremental as it improves on existing methods rather than introducing a new paradigm.
The paper tackled the problem of evolutionary algorithms requiring many evaluations in search-based software engineering by proposing a method that builds a large initial population and culls it with recursive bi-clustering, achieving comparable results to state-of-the-art evolutionary algorithms using under 100 evaluations.
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.