HW/SW codesign of protocols based on performance optimization using genetic algorithms

Miranda, Maximiliano; Lima, R.N.B.; Pedroza, A.C.P.; Filho, A.C. Mesquita

doi:10.1016/s1388-3437(01)80127-x

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2007

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Cited by 3 publications

(2 citation statements)

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“…Among many other applications, it has successfully been used for creating protocols with minimum communication costs [9,31,6], software testing [8], and evolving hardware/software codesigns [7].…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming meets Model-Driven Development

Weise

Zapf

Ullah

et al. 2007

7th International Conference on Hybrid Intelligent Systems (HIS 2007)

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Section: Introductionmentioning

confidence: 99%

Genetic Programming meets Model-Driven Development

Weise

Zapf

Ullah

et al. 2007

7th International Conference on Hybrid Intelligent Systems (HIS 2007)

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“…Genetic programming is the automated generation of computer programs by artificial evolution. Among many other applications, it has successfully been used for creating protocols with minimum communication costs [9,31,6], software testing [8], and evolving hardware/software codesigns [7].…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming meets Model-Driven Development

Weise

Zapf

Khan

et al. 2007

7th International Conference on Hybrid Intelligent Systems (HIS 2007)

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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.

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Genetic Programming for Proactive Aggregation Protocols

Weise

Geihs

Baer

Adaptive and Natural Computing Algorithms

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HW/SW codesign of protocols based on performance optimization using genetic algorithms

Cited by 3 publications

References 10 publications

Genetic Programming meets Model-Driven Development

Genetic Programming meets Model-Driven Development

Genetic Programming meets Model-Driven Development

Genetic Programming for Proactive Aggregation Protocols

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