1. 1. L. G. Caldas and L. K. Norford, “A design optimization tool based on a genetic algorithm,” Autom. Constr., 2002, doi: 10.1016/S0926- 5805(00)00096-0.
   2.  M. Jaderberg et al., “Human-level performance in 3D multiplayer games with population-based reinforcement learning,” Science (80-. )., 2019, doi: 10.1126/science.aau6249.
   3. I. Inza, P. Larrañaga, R. Etxeberria, and B. Sierra, “Feature Subset Selection by Bayesian network-based optimization,” Artif. Intell., 2000, doi: 10.1016/S0004-3702(00)00052-7.
   4. B. A. Jensen, B. Joseph, and B. G. Lipták, “Expert systems,” in Instrument Engineers Handbook, Fourth Edition: Process Control and Optimization, 2005.
   5. A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective intelligent energy management for a microgrid,” IEEE Trans. Ind. Electron., 2013, doi: 10.1109/TIE.2012.2188873.
   6. F. Herrera, M. Lozano, and J. L. Verdegay, “Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis,” Artif. Intell. Rev., 1998, doi: 10.1023/A:1006504901164.
   7. P. Parandoush and A. Hossain, “A review of modeling and simulation of laser beam machining,” International Journal of Machine Tools and Manufacture. 2014, doi: 10.1016/j.ijmachtools.2014.05.008.
   8. T. Khatib, A. Mohamed, and K. Sopian, “A review of photovoltaic systems size optimization techniques,” Renewable and Sustainable Energy Reviews. 2013, doi: 10.1016/j.rser.2013.02.023.
   9. M. Bahram, A. Wolf, M. Aeberhard, and D. Wollherr, “A prediction-based reactive driving strategy for highly automated driving function on freeways,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2014, doi: 10.1109/IVS.2014.6856503.
   10. R. Quiza Sardiñas, M. Rivas Santana, and E. Alfonso Brindis, “Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes,” Eng. Appl. Artif. Intell., 2006, doi: 10.1016/j.engappai.2005.06.007.
   
   
2. 1. L. G. Caldas and L. K. Norford, “A design optimization tool based on a genetic algorithm,” Autom. Constr., 2002, doi: 10.1016/S0926- 5805(00)00096-0.
   2.  M. Jaderberg et al., “Human-level performance in 3D multiplayer games with population-based reinforcement learning,” Science (80-. )., 2019, doi: 10.1126/science.aau6249.
   3. I. Inza, P. Larrañaga, R. Etxeberria, and B. Sierra, “Feature Subset Selection by Bayesian network-based optimization,” Artif. Intell., 2000, doi: 10.1016/S0004-3702(00)00052-7.
   4. B. A. Jensen, B. Joseph, and B. G. Lipták, “Expert systems,” in Instrument Engineers Handbook, Fourth Edition: Process Control and Optimization, 2005.
   5. A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective intelligent energy management for a microgrid,” IEEE Trans. Ind. Electron., 2013, doi: 10.1109/TIE.2012.2188873.
   6. F. Herrera, M. Lozano, and J. L. Verdegay, “Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis,” Artif. Intell. Rev., 1998, doi: 10.1023/A:1006504901164.
   7. P. Parandoush and A. Hossain, “A review of modeling and simulation of laser beam machining,” International Journal of Machine Tools and Manufacture. 2014, doi: 10.1016/j.ijmachtools.2014.05.008.
   8. T. Khatib, A. Mohamed, and K. Sopian, “A review of photovoltaic systems size optimization techniques,” Renewable and Sustainable Energy Reviews. 2013, doi: 10.1016/j.rser.2013.02.023.
   9. M. Bahram, A. Wolf, M. Aeberhard, and D. Wollherr, “A prediction-based reactive driving strategy for highly automated driving function on freeways,” in IEEE Intelligent Vehicles Symposium, Proceedings, 2014, doi: 10.1109/IVS.2014.6856503.
   10. R. Quiza Sardiñas, M. Rivas Santana, and E. Alfonso Brindis, “Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes,” Eng. Appl. Artif. Intell., 2006, doi: 10.1016/j.engappai.2005.06.007.