基于混合自适应的遗传工作流挖掘算法

Workflow Mining Optimization Based on Hybrid Adaptive Genetic Algorithm

针对遗传算法工作流挖掘容易过早收敛且局部寻优能力较差,导致得到的解不理想的情况,提出了一种基于混合遗传方法的工作流挖掘算法。该算法采用因果矩阵映射流程实例作为工作流模型的编码,在遗传算法的选择操作阶段采用锦标赛策略与精英保留策略相结合,在交叉变异阶段运用混合自适应方法,并结合模拟退火思想,使解的质量有了明显的提高。仿真实验表明,该算法与基于简单遗传方法的工作流挖掘算法相比效率更高。

Current workflow mining algorithm using local strategy couldn ’ t ensure that a globally optimal process modelwas mined .noise .To solve the problems ,a hybrid adaptive genetic algorithm was proposed .In this paper ,we define activity causal ma trix as a representation for individuals .In This paper algorithm elite retention strategy and adopts tournament to carry on select , then it using hybrid adaptive strategy carry on mutationr and rossove ,introducing the idea of simulated annealing into mutation and crossover ,the reconstruction method can effectively deal with noise and incompleteness and correctly discover the process mode1 .The simulation testing results demonstrate that the new algorithm has noise immunity and and it can find better solution and converge faster than the simple genetic algorithm employing general genetic strategy .