Applications of Soft Computing
Applications of Soft Computing
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软计算的应用
DOI：
10.1007/978-3-540-88079-0_20
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Turner C
中科院分区：
文献类型：
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作者：
Turner C
本文描述了使用遗传算法进行业务流程挖掘的实践。本文旨在确定流程挖掘遗传算法中两个适应度函数参数的最佳值；第一个参数降低了选择额外行为的流程模型的可能性，类似地，第二个参数限制了包含重复任务的模型的选择。本研究中进行的实验还包括使用突变算子的衰减率，以提高挖掘过程模型的准确性。提供了所采用的适应度和变异算法的详细信息。本文的结论是，适应度函数参数的最佳设置实际上会根据每个过程模型中的构造而变化。本文发现，适应度函数参数之一的值较高，可以更准确地挖掘简单的流程结构。还发现使用突变衰减率有利于简单过程的正确挖掘。
In this paper the practice of business process mining using genetic algorithms is described. This paper aims to ascertain the optimum values for two fitness function parameters within a process mining genetic algorithm; the first parameter reduces the likelihood of process models with extra behaviour being selected and similarly the second parameter restricts the selection of models containing duplicate tasks. The experiments conducted in this research also include the use of a decaying rate for the mutation operator in order to promote greater accuracy in the mined process models. Details are provided on the fitness and mutation algorithms employed. The paper concludes that the optimum setting of the fitness function parameters will in fact vary depending on the constructs found in each process model. This paper finds that a higher value for one of the fitness function parameters allows for simple process constructs to be mined with greater accuracy. The use of a decaying rate of mutation is also found to be beneficial in the correct mining of simple processes.