Systems research, genetic algorithms and information systems
Systems research, genetic algorithms and information systems
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DOI：
10.1002/(sici)1099-1743(200003/04)17:2
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发表时间：
2000-03
影响因子：
2.7
通讯作者：
S. Chaudhry;M. Varano;Lida Xu
中科院分区：
文献类型：
--
作者：
S. Chaudhry;M. Varano;Lida Xu
达尔文进化论和遗传学催生了一类被称为进化算法的计算方法，特别是遗传算法。这些演进战略为不断增加的工业应用提供了新的机遇和挑战。在这篇文章中，我们提出，对于正在出现的关于生物进化和进化算法之间的异同的辩论，基本统一的进化论的合适背景是系统科学。最近技术的变化，加上人工智能领域的发展，促进了使能技术的增长，例如智能系统，我们在其中集成了遗传算法。遗传算法与其他人工智能工具集成在一起，使用一个协作的智能子系统，该子系统被集成到本组织的信息系统中。一系列例子说明了遗传算法的发展和扩展应用，以及我们的计算经验与几个商业上可用的遗传算法软件。版权所有©2000 John Wiley&Sons，Ltd.
Darwinian evolution and genetics have spawned a class of computational methods called evolutionary algorithms, and in particular, genetic algorithms. These evolutionary strategies provide new opportunities and challenges with ever-increasing applications in industry. In this paper, we propose that the proper context for a basic unifying theory of evolution for the emerging debate on the similarities and differences between biotic evolution and evolutionary algorithms is systems science. Recent changes in technology, coupled with developments in the field of artificial intelligence, promote the growth of enabling technologies, such as intelligent systems, in which we integrate genetic algorithms. Genetic algorithms are integrated with other artificial intelligence tools using a cooperating intelligent subsystem, which is integrated into the information systems of the organization. A portfolio of examples illustrating the evolving and expanding applications of genetic algorithms is included, as well as our computational experience with several commercially available genetic algorithm software. Copyright © 2000 John Wiley & Sons, Ltd.