Boosting evolutionary optimization via fuzzy-classification-assisted selection

• Published in: Information sciences 2020-05, Vol.519, p.423-438
• Main Authors: Zhang, Jinyuan, Huang, Jimmy Xiangji, Hu, Qinmin Vivian
• Format: Article
• Language: English
• Subjects: Evolutionary algorithm; Fuzzy classification; Selection
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2020.01.050

•We treat the solution selection procedure in EAs as a fuzzy classification problem to reduce the number of FEs. Selected solutions belong to the ’promising’ class, while discarded solutions belong to the ’unpromising’ class•We propose a fuzzy-classification-assisted selection (FCAS) strategy to decide solutions for FE. Different from the existing classification-based strategies that decide solution evaluations according to the predicted labels, we use fuzzy membership degrees, which is more reliable than only using the labels.•The proposed FCAS strategy is a general algorithm framework, where different kinds of fuzzy classification models can be applied, and the FCAS can be applied to different kinds of EAs. We integrate FCAS into two state-of-the-art algorithms on three classical test suites. The experimental results show that the number of FEs can be significantly reduced by our proposed FCAS when the same fitness values are achieved. In evolutionary optimization, solution selection is an important operator since it will be normally used to decide the optimization direction via determining new solutions. Most selection methods are objective fitness-based approaches which will lead to a waste of fitness evaluations. This is because some evaluated but unpromising solutions are discarded without contributing useful search information. We are, thus, motivated to treat the solution selection as a classification procedure, where the selected solutions and discarded solutions belong to different classes. However, another problem is that the difference between ‘promising’ and ‘unpromising’ solutions becomes fuzzy when iterations go on. Therefore, we employ fuzzy classification to predict the categories of solutions by the fuzzy membership function. And then the predicted results are used to assist solution selection to reduce the number of fitness evaluations. Finally, we propose a fuzzy-classification-assisted selection (FCAS) strategy to boost evolutionary optimization. FCAS is experimentally integrated into two state-of-the-art algorithms and studied on three test suites. The results reveal the efficiency of FCAS for boosting evolutionary optimization.