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Issue No. 10 - Oct. (2015 vol. 41)
ISSN: 0098-5589
pp: 1001-1018
Tim Menzies , , North Carolina State University
ABSTRACT
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.
INDEX TERMS
Optimization, Software, Computational modeling, Approximation methods, Standards, Biological system modeling, Sociology,Active Learning, Multi-objective optimization, Search based software engineering
CITATION
Joseph Krall, Tim Menzies, Misty Davies, "GALE: Geometric Active Learning for Search-Based Software Engineering", IEEE Transactions on Software Engineering, vol. 41, no. , pp. 1001-1018, Oct. 2015, doi:10.1109/TSE.2015.2432024
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