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.
7th International Conference on Hybrid Intelligent
Systems, HIS 2007
Jahr
2007
Monat
17-19 September
Seiten
332--335
Verlag
IEEE
contents
* Introduction\\* Genetic Programming and MDD\\-
Model-Driven Development\\- Combining MDD and GP\\*
Evolving Distributed Algorithms\\- Evolving an Election
Algorithm\\* Creating a PIM\\- Control Flow Model\\-
Data Model\\- Modeling the Primitive Operations\\*
Transforming the UML Models\\* Conclusion
isbn13
978-0-7695-2946-2
language
en
notes
also known as WZKG2007DGPFg
Library of Congress Number 2007936727, Product Number
E2946. see http://his07.hybridsystem.com/
%0 Conference Paper
%1 Weise:2007:HIS
%A Weise, Thomas
%A Zapf, Michael
%A Khan, Mohammad Ullah
%A Geihs, Kurt
%B 7th International Conference on Hybrid Intelligent
Systems, HIS 2007
%C Kaiserslautern, Germany
%D 2007
%E König, Andreas
%E Köppen, Mario
%E Abraham, Ajith
%E Igel, Christian
%E Kasabov, Nikola
%I IEEE
%K Algorithms Architecture, Development, Distributed Driven GP, MDA, MDD, MOF-Skript, Model UML, XMI, algorithms, genetic programming,
%P 332--335
%R doi:10.1109/ICHIS.2007.4344073
%T Genetic Programming meets Model-Driven Development
%U http://www.it-weise.de/documents/files/WZKG2007DGPFg.pdf
%X 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.
@inproceedings{Weise:2007:HIS,
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.},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Kaiserslautern, Germany},
author = {Weise, Thomas and Zapf, Michael and Khan, Mohammad Ullah and Geihs, Kurt},
biburl = {https://www.bibsonomy.org/bibtex/2a8810ade180f1b7af3312cc219287cc9/brazovayeye},
booktitle = {7th International Conference on Hybrid Intelligent
Systems, HIS 2007},
contents = {* Introduction\\* Genetic Programming and MDD\\-
Model-Driven Development\\- Combining MDD and GP\\*
Evolving Distributed Algorithms\\- Evolving an Election
Algorithm\\* Creating a PIM\\- Control Flow Model\\-
Data Model\\- Modeling the Primitive Operations\\*
Transforming the UML Models\\* Conclusion},
doi = {doi:10.1109/ICHIS.2007.4344073},
editor = {K{\"o}nig, Andreas and K{\"o}ppen, Mario and Abraham, Ajith and Igel, Christian and Kasabov, Nikola},
interhash = {9236a157ef81806f54bdb9245877f185},
intrahash = {a8810ade180f1b7af3312cc219287cc9},
isbn13 = {978-0-7695-2946-2},
keywords = {Algorithms Architecture, Development, Distributed Driven GP, MDA, MDD, MOF-Skript, Model UML, XMI, algorithms, genetic programming,},
language = {en},
month = {17-19 September},
notes = {also known as \cite{WZKG2007DGPFg}
Library of Congress Number 2007936727, Product Number
E2946. see http://his07.hybridsystem.com/},
pages = {332--335},
publisher = {IEEE},
timestamp = {2008-06-19T17:54:00.000+0200},
title = {Genetic Programming meets Model-Driven Development},
url = {http://www.it-weise.de/documents/files/WZKG2007DGPFg.pdf},
year = 2007
}