A Short Survey of Evolutionary Algorithms for Solving Many Objective Optimization Problems
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0665-5_12
Reference11 articles.
1. Cheng, R., Jin, Y., Olhofer, M., Sendhoff, B.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20, 773–791 (2016)
2. Pan, L., He, C., Tian, Y., et al.: A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23(1), 74–88 (2018)
3. Guo, X.: A survey of decomposition based evolutionary algorithms for many-objective optimization problems. IEEE Access 10, 72825–72838 (2022)
4. Fritzke, B.: A growing neural gas network learns topologies. Advances in Neural Information Processing Systems 7 (1994)
5. Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired co-evolutionary algorithms using weight vectors. Eur. J. Oper. Res. 243(2), 423–441 (2015)
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