A comparative study of many-objective evolutionary algorithms for the discovery of software architectures
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Ramirez:2016:ESR,
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author = "Aurora Ramirez and Jose Raul Romero and
Sebastian Ventura",
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title = "A comparative study of many-objective evolutionary
algorithms for the discovery of software
architectures",
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journal = "Empirical Software Engineering",
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year = "2016",
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volume = "21",
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number = "6",
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pages = "2546--2600",
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month = dec,
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keywords = "genetic algorithms, genetic programming, SBSE,
Software architecture discovery, Search based software
engineering, Many-objective evolutionary algorithms,
Multi-objective evolutionary algorithms",
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ISSN = "1573-7616",
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DOI = "doi:10.1007/s10664-015-9399-z",
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size = "55 pages",
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abstract = "During the design of complex systems, software
architects have to deal with a tangle of abstract
artefacts, measures and ideas to discover the most
fitting underlying architecture. A common way to
structure such complex systems is in terms of their
interacting software components, whose composition and
connections need to be properly adjusted. Along with
the expected functionality, non-functional requirements
are key at this stage to guide the many design
alternatives to be evaluated by software architects.
The appearance of Search Based Software Engineering
(SBSE) brings an approach that supports the software
engineer along the design process. Evolutionary
algorithms can be applied to deal with the abstract and
highly combinatorial optimisation problem of
architecture discovery from a multiple objective
perspective. The definition and resolution of
many-objective optimisation problems is currently
becoming an emerging challenge in SBSE, where the
application of sophisticated techniques within the
evolutionary computation field needs to be considered.
In this paper, diverse non-functional requirements are
selected to guide the evolutionary search, leading to
the definition of several optimisation problems with up
to 9 metrics concerning the architectural
maintainability. An empirical study of the behaviour of
8 multi- and many-objective evolutionary algorithms is
presented, where the quality and type of the returned
solutions are analysed and discussed from the
perspective of both the evolutionary performance and
those aspects of interest to the expert. Results show
how some many-objective evolutionary algorithms provide
useful mechanisms to effectively explore design
alternatives on highly dimensional objective spaces.",
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notes = "Communicated by: Marouane Kessentini and Guenther
Ruhe",
- }
Genetic Programming entries for
Aurora Ramirez Quesada
Jose Raul Romero Salguero
Sebastian Ventura
Citations