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

页码：1001 / 1018

页数：18

共 63 条

[21]

Fortin FA, 2012, J MACH LEARN RES, V13, P2171

[22]

Hao Pan, 2008, 2008 International Conference on Computer Science and Software Engineering (CSSE 2008), P78, DOI 10.1109/CSSE.2008.1057

[23] Search based software engineering for software product line engineering: a survey and directions for future work [J].

Harman, M. ;

Jia, Y. ;

Krinke, J. ;

Langdon, W. B. ;

Petke, J. ;

Zhang, Y. .

18TH INTERNATIONAL SOFTWARE PRODUCT LINE CONFERENCE (SPLC 2014), VOL 1, 2014, :5-18

[24]

Harman M., 2013, COMMUNICATION

[25] Search-Based Software Engineering: Trends, Techniques and Applications [J].

Harman, Mark ;

Mansouri, S. Afshin ;

Zhang, Yuanyuan .

ACM COMPUTING SURVEYS, 2012, 45 (01)

[26]

Hoare C. A., 1961, Commun. ACM, V4, P321, DOI [DOI 10.1145/366622.366644, 10.1145/366622.366647, DOI 10.1145/366622.366647, DOI 10.1145/366622.366642]

[27]

Hoos HH, 2002, EIGHTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-02)/FOURTEENTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE (IAAI-02), PROCEEDINGS, P661

[28] Covariance matrix adaptation for multi-objective optimization [J].

Igel, Christian ;

Hansen, Nikolaus ;

Roth, Stefan .

EVOLUTIONARY COMPUTATION, 2007, 15 (01) :1-28

[29]

Joseph Krall M. D., 2015, IEEE T HUMA IN PRESS

[30]

Kamvar S. D., 2003, IJCAI'03, P561

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