1. Akandwanaho, S.M., Viriri, S.: A spy search mechanism for memetic algorithm in dynamic environments. Appl. Soft Comput. 75, 203–214 (2019)
2. Baluja, S.: Population-based incremental learning. A method for integrating genetic search based function optimization and competitive learning. Technical report, Carnegie-Mellon Univ. Pittsburgh, PA, Dept. of Computer Science (1994)
3. Baykasoğlu, A., Ozsoydan, F.B.: Dynamic optimization in binary search spaces via weighted superposition attraction algorithm. Expert Syst. Appl. 96, 157–174 (2018)
4. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol. 3, pp. 1875–1882. IEEE (1999)
5. Branke, J.: Optimization in dynamic environments. In: Branke, J. (ed.) Evolutionary Optimization in Dynamic Environments, vol. 3, pp. 13–29. Springer, Boston (2002).
https://doi.org/10.1007/978-1-4615-0911-0_2