References
- Bäck, T., F. Hoffmeister, and H.-P. Schwefel. 1991. A survey of evolution strategies. In Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, USA, 2–9. Morgan Kaufmanns.
- Beyer, H.-G., and H.-P. Schwefel. 2002. Evolution strategies: A comprehensive introduction. Journal Natural Computing 1 (1):3–52. doi:10.1023/A:1015059928466.
- Goldberg, D. E. 1989. Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.
- Grosan, C., and A. Abraham. 2007. Hybrid evolutionary algorithms: methodologies, architectures, and reviews. In Abraham A., Grosan C., Ishibuchi H. (eds), Hybrid evolutionary algorithms. Studies in computational intelligence, vol. 75, 1-17, Berlin, Heidelberg: Springer. doi:10.1007/978-3-540-73297-6_1
- Jensi, R., and J. G. Wiselin. 2016. An improved krill herd algorithm with global exploration capability for solving numerical function optimization problems and its application to data clustering. Applied Soft Computing 46:230–45. doi:10.1016/j.asoc.2016.04.026.
- Karaboga, D., and B. Basturk. 2007. A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization 39 (3):459–71. doi:10.1007/s10898-007-9149-x.
- Kwasnicka, H. 1999. Obliczenia ewolucyjne w sztucznej inteligencji, (in Polish). Wroclaw: Oficyna Wydawnicza Politechniki Wroclawskiej.
- Michalewicz, Z. 1992. Genetic algorithms + data structures = evolution programs. Berlin: Springer Verlag.
- Potter, M. A., and K. A. De Jong. 1994. A cooperative coevolutionary approach to function optimization. In Davidor Y., Schwefel HP., Männer R. (eds.), Parallel problem solving from nature - PPSN III. PPSN 1994. Lecture NOTES IN COMPUTER SCIENCE, vol. 866, 249-257, Berlin, Heidelberg: Springer. doi:10.1007/3-540-58484-6_269
- Pytel, K. 2011. The fuzzy genetic strategy for multiobjective optimization. In Proceedings of the federated conference on computer science and information systems, Szczecin.
- Pytel, K. 2016. Hybrid fuzzy-genetic algorithm applied to clustering problem. In Proceedings of the 2016 federated conference on computer science and information systems, Gdansk.
- Pytel, K. 2017. Hybrid multievolutionary system to solve function optimization problems. In Proceedings of the federated conference on computer science and information systems, Prague.
- Pytel, K., and T. Nawarycz. 2010. Analysis of the distribution of individuals in modified genetic algorithms. In Rutkowski L., Scherer R., Tadeusiewicz R., Zadeh L. A., Zurada J. M. (eds.), Artificial intelligence and soft computing, ICAISC 2010. Lecture Notes in Computer Science, vol 6114, 197-204, Springer-Verlag Berlin Heidelberg. doi:10.1007/978-3-642-13232-2_24
- Pytel, K., and T. Nawarycz. 2012. The fuzzy-genetic system for multiobjective optimization. In Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L.A., Zurada J.M. (eds.), Swarm and evolutionary computation, Lecture Notes in Computer Science, vol 7269, 325-332, Springer-Verlag Berlin Heidelberg. doi:10.1007/978-3-642-29353-5_38
- Pytel, K., and T. Nawarycz. 2013. A fuzzy-genetic system for ConFLP problem. In Andrzej M. J. Skulimowski (ed.), Advances in decision sciences and future studies, vol. 2, 575-586, Krakow: Progress & Business Publishers.
- Qian, X., Wang X., Su Y., and He L. 2018. An effective hybrid evolutionary algorithm for solving the numerical optimization problems. Journal of Physics: Conference Series 1004 (1): article id. 012020. doi:10.1088/1742-6596/1004/1/012020.