Combining Genetic Programming and Model-Driven Development

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Thomas Weise and Michael Zapf and 
                 Mohammad Ullah Khan and Kurt Geihs",
  title =        "Combining Genetic Programming and Model-Driven
  journal =      "International Journal of Computational Intelligence
                 and Applications (IJCIA)",
  year =         "2009",
  volume =       "8",
  number =       "1",
  month =        mar,
  pages =        "37--52",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 algorithms, model-driven development, model-driven
                 architecture, UML, XMI, MOFScript, distributed systems,
                 distributed algorithms, sensor networks",
  URL =          "",
  DOI =          "doi:10.1142/S1469026809002436",
  URL =          "",
  abstract =     "Genetic Programming is known to provide good solutions
                 for many problems like the evolution of network
                 protocols and distributed algorithms. In most cases it
                 is a hardwired module of a design framework assisting
                 the engineer in optimising specific aspects in system
                 development. In this article we show how the utility of
                 Genetic Programming can be increased remarkably by
                 isolating it as a component and integrating it into the
                 model-driven software development process. Our Genetic
                 Programming framework produces XMI-encoded UML models
                 that can easily be loaded into widely available
                 modeling tools, which in turn offer code generation as
                 well as additional analysis and test capabilities. We
                 use the evolution of a distributed election algorithm
                 as an example to illustrate how Genetic Programming can
                 be combined with model-driven development.",

Genetic Programming entries for Thomas Weise Michael Zapf Mohammad Ullah Khan Kurt Geihs