swMATH ID: 4801
Software Authors: Weise, Thomas; Zapf, Michael; Khan, Mohammad Ullah; Geihs, Kurt
Description: Combining genetic programming and model-driven development Genetic programming (GP) 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 optimizing specific aspects in system development. In this article, we show how the utility of GP can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our GP 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 GP can be combined with model-driven development (MDD).
Homepage: http://dgpf.sourceforge.net/
Keywords: model-driven architecture; distributed algorithms; sensor networks
Related Software: MSOPS-II; SIGOA; Genocop; CIXL2; pCMALib; MOCell; CEC 05; DREAM; PMF; OpenCL; ECJ; EASEA; OPT4J; HeuristicLab; JDeal; MOEA/D; MPI; OR-Library; CUDA; PALS
Cited in: 3 Publications
Further Publications: http://dgpf.sourceforge.net/documents/index.php

Citations by Year