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GENETIC ALGORITHMS FOR DECISIONAL DNA: SOLVING SETS OF EXPERIENCE KNOWLEDGE STRUCTURE

机译:决定性DNA的遗传算法:求解经验知识结构集

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摘要

Set of Experience Knowledge Structure (SOE) has been shown as a tool able to collect and manage explicit knowledge of formal decision events. This structure, after being homogenized and mixed, offers a set of possible solutions that, probably, could be improved. The purpose of this article is to show a search process for improved optimal solutions by implementing Evolutionary Algorithms-EA (Genetic Algorithms-GA). Afterward, according to the user's priorities, a unique optimal solution is chosen. Subsequently, such holistic improved SOE is stored as an experienced decision, feeding a knowledge repository of Decisional DNA that would be a useful technology within many different intelligent systems and platforms, including the Knowledge Supply Chain System (KSCS).
机译:经验知识结构集(SOE)已显示为能够收集和管理正式决策事件的明确知识的工具。经过均质化和混合后,此结构提供了一组可能的解决方案,可能会进行改进。本文的目的是通过实现进化算法-EA(Genetic Algorithms-GA)来展示一种针对改进的最优解的搜索过程。之后,根据用户的优先级,选择唯一的最佳解决方案。随后,将这种经过全面改进的SOE作为经验丰富的决策进行存储,为决策DNA的知识库提供信息,这将是许多不同的智能系统和平台(包括知识供应链系统(KSCS))中的有用技术。

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