Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery

Abstract

Evolutionary algorithm is an effective way to solve process discovery problem which aims to mine process models from event logs which are consistent with the real business processes. However, current evolutionary algorithms, such as GeneticMiner, ETM, and ProDiGen, converge slowly and in difficultly because all of them employ genetic crossover and mutation which have strong randomness. This paper proposes a hybrid evolutionary algorithm for automated process discovery, which consists of a set-based differential evolution algorithm and guided local exploration. There are three major innovations in this work. First of all, a hybrid evolutionary strategy is proposed, in which a differential evolution algorithm is employed to search the solution space and rapidly approximate the optimal solution firstly, and then a specific local exploration method joins to help the algorithm skip out the local optimum. Secondly, two novel set-based differential evolution operators are proposed, which can efficiently perform differential mutation and crossover on the causal matrix. Thirdly, a fine-grained evaluation technique is designed to assign score to each node in a process model, which is employed to guide the local exploration and improve the efficiency of the algorithm. Experiments were performed on 68 different event logs, including 22 artificial event logs, 44 noisy event logs, and two real event logs. Moreover, the proposed algorithm was compared with three popular algorithms of process discovery. Experimental results show that the proposed algorithm can achieve good performance and its converge speed is fast.