Single and Multi Objective Genetic Programming for software development effort estimation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @InProceedings{conf/sac/SarroFG12,
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author = "Federica Sarro and Filomena Ferrucci and
Carmine Gravino",
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title = "Single and Multi Objective Genetic Programming for
software development effort estimation",
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booktitle = "Proceedings of the 27th Annual ACM Symposium on
Applied Computing",
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year = "2012",
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editor = "Sascha Ossowski and Paola Lecca",
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pages = "1221--1226",
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address = "Trento, Italy",
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publisher = "ACM",
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keywords = "genetic algorithms, genetic programming, SBSE, effort
estimation, empirical study, multi objective search",
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isbn13 = "978-1-4503-0857-1",
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URL = "http://dl.acm.org/citation.cfm?id=2245276",
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DOI = "doi:10.1145/2245276.2231968",
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abstract = "The idea of exploiting Genetic Programming (GP) to
estimate software development effort is based on the
observation that the effort estimation problem can be
formulated as an optimisation problem. Indeed, among
the possible models, we have to identify the one
providing the most accurate estimates. To this end a
suitable measure to evaluate and compare different
models is needed. However, in the context of effort
estimation there does not exist a unique measure that
allows us to compare different models but several
different criteria (e.g., MMRE, Pred(25), MdMRE) have
been proposed. Aiming at getting an insight on the
effects of using different measures as fitness
function, in this paper we analysed the performance of
GP using each of the five most used evaluation
criteria. Moreover, we designed a Multi-Objective
Genetic Programming (MOGP) based on Pareto optimality
to simultaneously optimise the five evaluation measures
and analysed whether MOGP is able to build estimation
models more accurate than those obtained using GP. The
results of the empirical analysis, carried out using
three publicly available datasets, showed that the
choice of the fitness function significantly affects
the estimation accuracy of the models built with GP and
the use of some fitness functions allowed GP to get
estimation accuracy comparable with the ones provided
by MOGP.",
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acmid = "2231968",
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bibdate = "2012-06-08",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sac/sac2012.html#SarroFG12",
- }
Genetic Programming entries for
Federica Sarro
Filomena Ferrucci
Carmine Gravino
Citations