基于混合自适应遗传算法的工作流挖掘优化  

Workflow Mining Optimization Based on Hybrid Adaptive Genetic Algorithm

作　　者：顾春琴[1,2] 陶乾[2,3] 吴家培[1] 常会友[2] 姚卿达[2] 衣杨[2] 

机构地区：[1]仲恺农业工程学院计算机科学与工程学院,广州510225 [2]中山大学信息科学与技术学院,广州510275 [3]广州大学松田学院,广州511370

出　　处：《计算机科学》2010年第3期234-238,共5页Computer Science

基　　金：国家自然科学基金(60573159)资助

摘　　要：针对目前工作流挖掘算法采用局部策略而无法保证最优挖掘以及算法对噪声敏感的情况,提出了基于混合自适应遗传算法的工作流挖掘优化算法。首先定义了基本工作流网以及变迁的使能和点火规则,描述了过程模型;然后提出了过程模型转换成基本工作流网的算法,给出了衡量事件日志与过程模型的符合性的适应值评价函数;最后根据进化阶段以及个体相似度设计了混合自适应的交叉率和变异率。仿真试验结果表明,该算法与α算法相比具有更高的鲁棒性和对噪声的抗干扰性;与基本遗传算法相比,该算法能显著提高解的质量和收敛速度。Current workflow mining algorithm using local strategy couldn＇t ensure that a globally optimal process mode was mined. The algorithm was also sensitive to noise. To solve the problems,a hybrid adaptive genetic algorithm （HA-GA） was proposed. Firstly, Elementary Workflow net （EW-net） was defined. The enabling and firing rules of EW-net were given, and the process model was described. Secondly, a converting algorithm proposed was used to convert the process model to EW-net, and an evaluating function of the individual fitness was presented in order to measure the compliance between event log and mined process model. Lastly, hybrid adaptive crossover and mutation rates were do signed according to evolution stage and parents＇ similarity. The simulation testing results demonstrate that the new algorithm has noise immunity and is more robust than a algorithm,and it can find better solution and converge faster thar the simple genetic algorithm （SGA） employing general genetic strategy.

关 键 词：工作流挖掘 过程挖掘 混合自适应遗传算法 基本工作流网 关联矩阵 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]