Complex Application Architecture Dynamic Reconfiguration Based on Multi-criteria Decision Making

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Vincent Talbot and Ilham Benyahia",
  title =        "Complex Application Architecture Dynamic
                 Reconfiguration Based on Multi-criteria Decision
  journal =      "International journal of Software Engineering \&
  year =         "2010",
  volume =       "1",
  number =       "4",
  pages =        "19--37",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Complex
                 applications, architecture performance optimization,
                 architecture reconfiguration, multi-criterion",
  ISSN =         "0976-2221",
  URL =          "",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.5121/ijsea.2010.1402",
  size =         "19 pages",
  abstract =     "Intelligent Transportation Systems (ITS) are
                 increasingly important since they aim to bring
                 solutions to crucial problems related to transportation
                 networks such as congestion and various road incidents.
                 Management of ITS, as other complex and distributed
                 applications, has to cope with unforeseeable events and
                 incomplete data while guaranteeing a quality of service
                 (QoS) defined by multiple criteria reflecting real-life
                 needs. To enable applications to adapt to changing
                 environments, we define a methodology of dynamic
                 architecture reconfiguration based on multi-criteria
                 decision making (MCDM) using evolutionary computing
                 (EC) to find the best combination of architecture
                 components. We use the Pareto Evolutionary Algorithm
                 Adapting the Penalty (PEAP), a category of EC, selected
                 in this paper to deal with time consuming online
                 processing required by basic EC such as genetic
                 algorithms. Our simulation results relating to road
                 safety highlight the benefits of MCDM prior to such
                 reconfiguration. We also address the problem of
                 destabilization which can result from repeated
                 reconfigurations in response to ongoing environment
  notes =        "Very little mention of GP but does say follows

                 Universite du Quebec en Outaouais, Canada",

Genetic Programming entries for Vincent Talbot Ilham Benyahia