【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。更多还原

【Abstract】 Current workflow mining algorithm using local strategy couldn’t ensure that a globally optimal process modelwas mined.The algorithm was also sensitive to noise.To solve the problems,a hybrid adaptive genetic algorithm (HAGA) 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 designed according to evolution stage and parents’ similarity.The simulation testing results demonstrate that the new algorithm has noise immunity and is more robust than α algorithm,and it can find better solution and converge faster than the simple genetic algorithm (SGA) employing general genetic strategy.更多还原