A comparative study of many-objective evolutionary algorithms for the discovery of software architectures

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Ramirez:2016:ESR,
  author =       "Aurora Ramirez and Jose Raul Romero and 
                 Sebastian Ventura",
  title =        "A comparative study of many-objective evolutionary
                 algorithms for the discovery of software
                 architectures",
  journal =      "Empirical Software Engineering",
  year =         "2016",
  volume =       "21",
  number =       "6",
  pages =        "2546--2600",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, SBSE,
                 Software architecture discovery, Search based software
                 engineering, Many-objective evolutionary algorithms,
                 Multi-objective evolutionary algorithms",
  ISSN =         "1573-7616",
  DOI =          "doi:10.1007/s10664-015-9399-z",
  size =         "55 pages",
  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.",
  notes =        "Communicated by: Marouane Kessentini and Guenther
                 Ruhe",
}

Genetic Programming entries for Aurora Ramirez Quesada Jose Raul Romero Salguero Sebastian Ventura

Citations