基于遗传算法的范畴化风险决策知识重组方法

GAs based categorical knowledge reorganization for risk decision-making

知识重组是风险管理中辅助决策者获得决策信息的重要环节,它将与应急问题情境具有强关联的文本性决策知识提供给决策者.在范畴化结构的基础上,提出一种基于遗传算法面向语义关联的知识重组方法,为风险决策知识快速获取所需的高维语义信息处理提供了新的途径.该算法从遗传编码、算子、收敛控制和参数选择4个侧面来实现进化过程.仿真实验结果表明,在强制收敛的条件下,该方法可获得较好的重组可靠性.

Knowledge reorganization is one of the key links in supporting decision-makers to acquire the information of decision-making in risk management. This process provides decision makers with textual knowledge which has strong correlation with decision-making problem situations. This paper proposes a knowledge reorganization method based on categorical structure semantic correlations based on genetic algorithm. This method provides a new way for the high- dimensional semantic information processing which are necessary for the quick knowledge acquisition of risk decision- making. The process of genetic evolution is achieved through four aspects including genetic coding, operators, convergence control and parameter selection. Simulation experiment results show that this method can obtain better reliability of reorganization under forced convergence conditions.