A variable population size opposition-based learning for differential evolution algorithm and its applications on feature selection
Author:
Funder
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05179-y.pdf
Reference62 articles.
1. Gao D, Wang G-G, Pedrycz W (2020) Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 28(12):3265–3275. https://doi.org/10.1109/tfuzz.2020.3003506
2. Tirkolaee EB, Goli A, Weber G-W (2020) Fuzzy mathematical programming and self-adaptive artificial fish swarm algorithm for just-in-time energy-aware flow shop scheduling problem with outsourcing option. IEEE Trans Fuzzy Syst 28(11):2772–2783
3. Tirkolaee EB, Alinaghian M, Hosseinabadi AAR, Sasi MB, Sangaiah AK (2019) An improved ant colony optimization for the multi-trip capacitated arc routing problem. Comput Electr Eng 77:457–470
4. Alswaitti M, Albughdadi M, Isa NAM (2019) Variance-based differential evolution algorithm with an optional crossover for data clustering. Appl Soft Comput 80:1–17. https://doi.org/10.1016/j.asoc.2019.03.013
5. Zhang Y, Gong D-w, Gao X-z, Tian T, Sun X-y (2020) Binary differential evolution with self-learning for multi-objective feature selection. Inf Sci 507:67–85. https://doi.org/10.1016/j.ins.2019.08.040
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