GALE: Geometric Active Learning for Search-Based Software Engineering

被引：25
作者：
Krall, Joseph [1 ]
Menzies, Tim [1 ]
Davies, Misty [2 ]
机构：
[1] N Carolina State Univ, Comp Sci, Raleigh, NC 27695 USA
[2] NASA, Ames Res Ctr, Intelligent Syst Div, Los Angeles, CA USA
来源：
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | 2015年 / 41卷 / 10期
基金：
美国国家科学基金会;
关键词：
Multi-objective optimization; search based software engineering; active learning; MULTIOBJECTIVE EVOLUTIONARY ALGORITHM; DIFFERENTIAL EVOLUTION; OPTIMIZATION; SYSTEMS; MOEA/D;
D O I：
10.1109/TSE.2015.2432024
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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.
引用
收藏
页码：1001 / 1018
页数：18
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