Beyond evolutionary algorithms for search-based software engineering

作者：Chen Jianfeng; Nair Vivek^*; Menzies Tim

来源：Information and Software Technology, 2018, 95: 281-294.

DOI：10.1016/j.infsof.2017.08.007

Context: Evolutionary algorithms typically require 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.

• 出版日期2018-3