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Applying genetic algorithm for the development of the components-based embedded system

机译:遗传算法在基于组件的嵌入式系统开发中的应用

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The embedded system is primarily designed for a particular piece of equipment and it varies on a case-by-case basis. The functionality is required to be specific to the equipment and consequently the application domain is limited. The software embedded in the system also faces problem due to the limitation of the hardware capacity. It is necessary for the designers to consider the hardware capacity and software specification simultaneously while an embedded system is developed. If hardware and software are taken into account concurrently, the design applicability and efficiency are decreased. The evolutionary computing (EC), which comprises techniques of evolutionary programming, evolution strategies, genetic algorithms, and genetic programming has been widely used to solve optimization problems for large scale and complex systems. It is capable to escape not only from local optima due to population based approach, but also from unbiased nature, which enables it to perform well in a situation with little domain knowledge. Therefore, this study proposes an evolutionary approach that applies the characteristics of software reuse, the metrics for the object-oriented concept, and the genetic algorithm to effectively manage and optimize the embedded system. This approach is implemented in the World Wide Web environment. Numerous results associated with performance enhancements of the algorithm are presented in this paper.
机译:嵌入式系统主要是为特定设备设计的,并且会根据具体情况而有所不同。该功能要求特定于设备,因此应用领域受到限制。由于硬件容量的限制,系统中嵌入的软件也面临问题。设计人员在开发嵌入式系统时必须同时考虑硬件容量和软件规格。如果同时考虑硬件和软件,则会降低设计的适用性和效率。进化计算(EC)包含进化编程,进化策略,遗传算法和遗传编程技术,已广泛用于解决大型复杂系统的优化问题。它不仅能够避免由于基于种群的方法而导致的局部最优,而且能够摆脱无偏见的性质,这使其能够在几乎没有领域知识的情况下表现良好。因此,本研究提出了一种演化方法,该方法利用软件重用的特性,面向对象概念的度量以及遗传算法来有效地管理和优化嵌入式系统。此方法在万维网环境中实现。本文介绍了与算法性能增强相关的大量结果。

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