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Specifically, the CBDA innovatively introduces three improved factors, namely the chaotic map, evolutionary population dynamics (EPD) mechanism, and binarization strategy on the basis of conventional DA to balance the exploitation and exploration capabilities of the algorithm and make it more suitable to handle the formulated problem. We conduct experiments on 24 well-known data sets from the UCI repository with three ablated versions of CBDA targeting different components of the algorithm in order to explain their contributions in CBDA and also with five established comparative algorithms in terms of fitness value, classification accuracy, CPU running time, and number of selected features. The results show that the proposed CBDA has remarkable advantages in most of the tested data sets.<\/jats:p>","DOI":"10.3233\/ida-230540","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T11:33:49Z","timestamp":1712921629000},"page":"1491-1526","source":"Crossref","is-referenced-by-count":2,"title":["CBDA: Chaos-based binary dragonfly algorithm for evolutionary feature selection"],"prefix":"10.1177","volume":"28","author":[{"given":"Zhao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aimin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiming","family":"Bao","sequence":"additional","affiliation":[{"name":"Chang Guang 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