1. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Evolutionary computation 1: Basic algorithms and operators. CRC Press, Bristol (2018)
2. Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Pearson Education Limited, Malaysia (2016)
3. Pinedo, M., Hadavi, K.: Scheduling: theory, algorithms and systems development. In: Operations Research Proceedings 1991, pp. 35–42. Springer, Berlin, Heidelberg (1992)
4. Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., Clune, J.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint arXiv:1712.06567. (2017)
5. Mirjalili, S.Z., Saremi, S., Mirjalili, S.M.: Designing evolutionary feedforward neural networks using social spider optimization algorithm. Neural Comput. Appl. 26(8), 1919–1928 (2015)