期刊
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
卷 41, 期 10, 页码 1001-1018出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSE.2015.2432024
关键词
Multi-objective optimization; search based software engineering; active learning
资金
- US National Science Foundation (NSF) [CCF:1017330]
- Qatar/West Virginia University research grant [NPRP 09-12-5-2-470]
- NASA Ames Research Center
Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.
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