Paper Info
Title
Boosting Evolutionary Optimization via Fuzzy-Classification-Assisted Selection
Abstract
•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.
Year
DOI
Venue
2020
10.1016/j.ins.2020.01.050
Information Sciences
Keywords
Field
DocType
Evolutionary algorithm,Selection,Fuzzy classification
Fuzzy classification,Fuzzy logic,Fuzzy membership function,Operator (computer programming),Artificial intelligence,Boosting (machine learning),Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
519
0020-0255
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jinyuan Zhang100.34
Xiangji Huang21551159.34
Qinmin Vivian Hu3206.06