A Survey on Evolutionary Computation Approaches to Feature Selection

Bing Xue, Mengjie Zhang, Will Browne, Xin Yao

Research output: Contribution to journalArticlepeer-review

648 Citations (Scopus)
575 Downloads (Pure)


Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on evolutionary computation for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.
Original languageEnglish
Number of pages20
JournalIEEE Transactions on Evolutionary Computation
Issue number99
Early online date30 Nov 2015
Publication statusE-pub ahead of print - 30 Nov 2015


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