Technischer Report
Genetic Programming meets Model-Driven Development
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 optimize 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 modeling 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.
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 modeling 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.
Citation
@techreport{urn:nbn:de:hebis:34-2007070218786,
author={Weise, Thomas and Zapf, Michael and Khan, Mohammad Ullah and Geihs, Kurt},
title={Genetic Programming meets Model-Driven Development},
year={2007}
}
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3000 Weise, Thomas
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3010 Khan, Mohammad Ullah
3010 Geihs, Kurt
4000 Genetic Programming meets Model-Driven Development / Weise, Thomas
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4170 Kasseler Informatikschriften ;; 2007, 2
5550 {{Genetische Programmierung}}
5550 {{Modellgetriebene Entwicklung}}
7136 ##0##urn:nbn:de:hebis:34-2007070218786
<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2007-07-02T12:01:36Z 2007-07-02T12:01:36Z 2007-07-02T12:01:36Z urn:nbn:de:hebis:34-2007070218786 http://hdl.handle.net/123456789/2007070218786 95119 bytes application/pdf eng Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ Genetische Programmierung Evolutionäre Algorithmen Genetic Programming Evolutionary Algorithms GP EA Model Driven Development Model Driven Architecture MDD MDA UML XMI MOFScript Election Algorithm Distributed Algorithms Sensor Networks 004 Genetic Programming meets Model-Driven Development Technischer Report 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 optimize 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 modeling 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. open access Weise, Thomas Zapf, Michael Khan, Mohammad Ullah Geihs, Kurt Kasseler Informatikschriften ;; 2007, 2 C.2.4 D.1.2 D.2.10 D.2.12 I.2.2 I.2.6 Genetische Programmierung Modellgetriebene Entwicklung Kasseler Informatikschriften 2007, 2 </resource>
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