Association rule mining based parameter adaptive strategy for differential evolution algorithms. (1st June 2019)

• Record Type: Journal Article
• Authors: Wang, Chuan; Liu, Yancheng; Zhang, Qinjin; Guo, Haohao; Liang, Xiaoling; Chen, Yang; Xu, Minyi; Wei, Yi
• Is Part Of: Expert systems with applications. Volume 123(2019)
• Page Start: 54
• Page End: 69
• Publication Date: 2019-06-01
• Subjects: Differential evolution -- Association Rule Mining -- Parameter adaption; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.01.035
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The proposed strategy extracts associations automatically from successful records. The proposed strategy incorporate the associations to generate new F and Cr values. The process of adaption for F and Cr introduces no extra control parameters. We propose a novel method to adapt F and Cr for Differential Evolution algorithms. Experiments show that the strategy could enhance performances to some extent. Abstract: It is a very challenging and important task to adaptively adjust the scale factor F and the crossover rate Cr for Differential Evolutionary (DE) algorithms. Most recent adaptive techniques were designed to generate parameters randomly based on successful trial values during the previous evolving process, lacking explicit guidelines to generate appropriate values. This paper proposes a novel parameter adaption strategy, which could incorporate promising F and Cr pairs extracted by using Association Rule Mining (ARM) into DE algorithms. First, all successful F and Cr values generated by their original methods are recorded during the whole evolution, resulting in an increasing dataset. Second, we discretize the dataset and extract the most frequent itemset of parameters by using a modified version of the widely used Apriori algorithm. Third, a greedy operator is developed to generate new parameters in the next generation by comparing the presented ARM-based and original-method-based fitness values. The presented technique provides an additional pair of F and Cr values to be evaluated, without replacing existing strategies for the control parameters. The main contribution of this paper is that we propose a novel way, which utilizes information generated during the evolutionary process, to enhance exploration capabilities by adjusting control parameters. Experimental results demonstrate that the proposed ARM-based parameter adaptive strategy is able to enhance performances of some state-of-the-art DE variants. Further, this methodology might be helpful for other control parameters of Evolutionary Algorithms (EA).