@inproceedings{Weise:2007:HIS,
        title = {Genetic Programming meets Model-Driven Development},
        address = {Kaiserslautern, Germany},
        author = {Thomas Weise and Michael Zapf and Mohammad Ullah Khan and Kurt Geihs},
        booktitle = {7th International Conference on Hybrid Intelligent
                 Systems, HIS 2007},
        editor = {Andreas K{\"o}nig and Mario K{\"o}ppen and Ajith Abraham and Christian Igel and Nikola Kasabov},
        month = {17-19 September},
        pages = {332--335},
        publisher = {IEEE},
        year = 2007,
        url = {http://www.it-weise.de/documents/files/WZKG2007DGPFg.pdf},
        language = {en},
	notes = {also known as \cite{WZKG2007DGPFg},
	doi = {doi:10.1109/ICHIS.2007.4344073},
	abstract = {Genetic programming is known to provide good solutions
                 for many problems like the evolution of network
                 protocols and distributed algorithms. In such cases it
                 is most likely a hardwired module of a design framework
                 that assists the engineer to optimise specific aspects
                 of the system to be developed. It provides its results
                 in a fixed format through an internal interface. In
                 this paper 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 modelling tools
                 which in turn posses 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. This example
                 clearly illustrates the advantages of our approach -
                 the generation of source code in different programming
                 languages.},
	biburl = {http://www.bibsonomy.org/bibtex/2a8810ade180f1b7af3312cc219287cc9/brazovayeye},
	keywords = {Architecture, MOF-Skript, UML, Model Algorithms MDD, MDA, programming, Distributed genetic Development, XMI, algorithms, Driven GP,}
}

@techreport{WZKG2007DGPFd,
        title = {Genetic Programming meets Model-Driven Development},
        address = {University of Kassel, FB-16, Distributed Systems
                 Group, Wilhelmsh{\"o}her Allee 73, 34121 Kassel,
                 Germany},
        author = {Thomas Weise and Michael Zapf and Mohammad Ullah Khan and Kurt Geihs},
        institution = {University of Kassel},
        month = {July~2,},
        number = {2007, 2},
        organization = {University of Kassel},
        pages = {1--8},
        type = {Kasseler Informatikschriften (KIS)},
        year = 2007,
        url = {http://kobra.bibliothek.uni-kassel.de/handle/urn:nbn:de:hebis:34-2007070218
                 786},
        language = {en},
	copyright = {unrestricted},
	abstract = {Genetic programming is known to provide good solutions
                 for many problems like the evolution of network
                 protocols and distributed algorithms. In such cases it
                 is most likely a hardwired module of a design framework
                 that assists the engineer to optimise specific aspects
                 of the system to be developed. It provides its results
                 in a fixed format through an internal interface. In
                 this paper 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 modelling tools
                 which in turn posses 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. This example
                 clearly illustrates the advantages of our approach -
                 the generation of source code in different programming
                 languages.},
	biburl = {http://www.bibsonomy.org/bibtex/2e2af94503a38b8b097aebd153f2ae8ac/brazovayeye},
	keywords = {Architecture, MOF-Skript, UML, Model Algorithms MDD, MDA, programming, Distributed genetic Development, XMI, algorithms, Driven GP,}
}