[Augustyn, E.; Kozien, M. S. 2014. A Study on Possibility to Apply Piezoelectric Actuators for Active Reduction of Torsional Beams Vibrations. Acta Physica Polonica A, 125, A164-A168.10.12693/APhysPolA.125.A-164]Search in Google Scholar
[El-Mahdy, O., Ahmed, M., Metwalli, S., 2010. Computer aided optimization of natural gas pipe networks using genetic algorithm. Applied Soft Computing, 10, 1141–50. DOI: 10.1016/j.asoc.2010.05.01010.1016/j.asoc.2010.05.010]Search in Google Scholar
[Frith, R., Scott, W., 1996. Comparison of an external gear pump wear model with test data. Wear, 196, 64–71. DOI: 10.1016/0043-1648(95)06845-710.1016/0043-1648(95)06845-7]Open DOISearch in Google Scholar
[Fuh, J., Li, W., 2005. Advances in collaborative CAD: the-state-of the art. Computer Aided Design, 37, 571–81. DOI: 10.1016/j.cad.2004.08.005.10.1016/j.cad.2004.08.005]Open DOISearch in Google Scholar
[Gadek-Moszczak, A., Pietraszek, J., Jasiewicz, B., Sikorska, S., Wojnar, L., 2015. The bootstrap approach to the comparison of two methods applied to the evaluation of the growth index in the analysis of the digital x-ray image of a bone regenerate. New Trends in Comp. Coll. Intell., 572, 127-136. DOI: 10.1007/978-3-319-10774-5_1210.1007/978-3-319-10774-5_12]Open DOISearch in Google Scholar
[Gen, M., Cheng, R., 2000. Genetic algorithms and engineering optimization Vol. 7. Wiley, Hoboken.10.1002/9780470172261]Search in Google Scholar
[Goldberg, D.E., Holland, J.H., 1988. Genetic algorithms and machine learning. Machine Learning, 3, 95–9.10.1023/A:1022602019183]Open DOISearch in Google Scholar
[Grefenstette, J.J., 1986. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 16, 122–8.10.1109/TSMC.1986.289288]Search in Google Scholar
[Hu, Z.H., Ding, Y.S., Zhang, W.B., Yan, Q., 2008. An interactive co-evolutionary CAD system for garment pattern design. Computer Aided Design, 40, 1094–104. DOI: 10.1016/j.cad.2008.10.01010.1016/j.cad.2008.10.010]Open DOISearch in Google Scholar
[Ionel, I.I., 1986. Pumps and pumping. Elsevier, New York.]Search in Google Scholar
[Ivantysyn, J., Ivantysynova, M., 2003. Hydrostatic pumps and motors: principles, design, performance, modelling, analysis, control and testing. Tech Books International.]Search in Google Scholar
[Karpisz, D., Kielbus, A., 2018. Selected problems of designing modern industrial databases. MATEC Web Conf., 183, art. 01017. DOI: 10.1051/matecconf/20181830101710.1051/matecconf/201818301017]Open DOISearch in Google Scholar
[Kielbus, A., Karpisz, D., 2019. Risk management as a process security tool. System Safety: Human-Technical Facility-Environment, 1, 234-239. DOI: 10.2478/czoto-2019-003010.2478/czoto-2019-0030]Open DOISearch in Google Scholar
[Kita, E., Tanie, H., 1997. Shape optimization of continuum structures by genetic algorithm and boundary element method. Engineering Analysis with Boundary Elements, 19, 129–36. DOI: 10.1016/S0955-7997(97)00014-310.1016/S0955-7997(97)00014-3]Open DOISearch in Google Scholar
[Kollek W. Pompy zebate – konstrukcja i eksploatacja. Zakład Narodowy im. Ossolinskich; 1996.]Search in Google Scholar
[Kozien, E., Kozien, M.S., 2017. Academic governance as a determinant of efficient management of a university in Poland - legal and comparative perspective. ESD 2017: Economic and Social Development Conf., Madrid, Varazdin, 38-47.]Search in Google Scholar
[Krenich, S., 2017. Multi-thread evolutionary computation for design optimization. Technical Transactions, 9, 197-20610.4467/2353737XCT.17.159.7171]Search in Google Scholar
[Lampinen J., 2003. CAM shape optimisation by genetic algorithm. Computer Aided Design, 35, 727–37. DOI: 10.1016/S0010-4485(03)00004-610.1016/S0010-4485(03)00004-6]Open DOISearch in Google Scholar
[Ladd, S.R., 1995. Genetic algorithms in C++. Hungry Minds, New York.]Search in Google Scholar
[Langdon, W.B., Poli, R., 2002. Foundations of genetic programming. Springer.10.1007/978-3-662-04726-2]Open DOISearch in Google Scholar
[Melanie, M., 1999. An introduction to genetic algorithms. Cambridge, Massachusetts.]Search in Google Scholar
[Opydo, M., Kobylecki, R., Dudek, A., Bis, Z. 2016. The effect of biomass co-combustion in a CFB boiler on solids accumulation on surfaces of P91 steel tube samples. Biomass & Bioenergy, 85, 61-68. DOI: 10.1016/j.biombioe.2015.12.01110.1016/j.biombioe.2015.12.011]Open DOISearch in Google Scholar
[Osmera, P., Lacko, B., Peter M., 2003. Parallel Evolutionary Algorithms, 2003 IEEE Int. Symposium Computational Intelligence in Robotics and Automation, Kobe, IEEE, 1348-1353.]Search in Google Scholar
[Pacana, J., Pacana, A., 2018. Analysis of Possibilities of Using Polymeric Materials for Testing Prototypes of Harmonic Drive. Materials Research Proceedings, 5, 61-66. DOI: 10.21741/9781945291814-1110.21741/9781945291814-11]Open DOISearch in Google Scholar
[Pal, P., Tigga, A., Kumar, A., 2005. Feature extraction from large cad databases using genetic algorithm. Computer Aided Design, 37, 545–58. DOI: 10.1016/j.cad.2004.08.00210.1016/j.cad.2004.08.002]Open DOISearch in Google Scholar
[Park, H.S., Dang, X.P., 2010. Structural optimization based on CAD–CAE integration and metamodeling techniques. Computer-Aided Design, 42, 889-902. DOI: 10.1016/j.cad.2010.06.003.10.1016/j.cad.2010.06.003]Open DOISearch in Google Scholar
[Pietraszek, J., Dwornicka, R., Krawczyk, M., Kołomycki, M., 2017. The non-parametric approach to the quantification of the uncertainty in the design of experiments modelling. UNCECOMP 2017: 2nd Int. Conf. Uncertainty Quantification in Computational Sciences and Engineering, Rhodes, NTU of Athens, 598-604. DOI: 10.7712/120217.5395.1722510.7712/120217.5395.17225]Open DOISearch in Google Scholar
[Pietraszek, J., Goroshko, A., 2014. The heuristic approach to the selection of experimental design, model and valid pre-processing transformation of DoE outcome. Advanced Materials Research-Switzerland, 874, 145-149. DOI: 10.4028/www.scientific.net/AMR.874.14510.4028/www.scientific.net/AMR.874.145]Open DOISearch in Google Scholar
[Radek, N., Pasieczynski, L., Makrenek, M., Dudek, A., 2018. Mechanical Properties of Anti-Graffiti Coating Systems used in the Railway Industry. Materials Research Proceedings, 5, 243-247. DOI: 10.21741/9781945291814-4310.21741/9781945291814-43]Open DOISearch in Google Scholar
[Radek, N., Pietraszek, J., Antoszewski, B., 2014. The Average Friction Coefficient of Laser Textured Surfaces of Silicon Carbide Identified by RSM Methodology. Adv. Mat. Res.-Switz., 874, 29-34. DOI: 10.4028/www.scientific.net/AMR.874.2910.4028/www.scientific.net/AMR.874.29]Open DOISearch in Google Scholar
[Shi, X., 2011. Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, 36, 1659–67. DOI: 10.1016/j.energy.2010.12.06410.1016/j.energy.2010.12.064]Open DOISearch in Google Scholar
[Stroustrup, B., 2000. The C++ Programming Language. The C++ Programming Language (Special Edition). Addison-Wesley, Reading.]Search in Google Scholar
[Szczotok, A., Radek, N., Dwornicka, R., 2018. Effect of the induction hardening on microstructures of the selected steels. METAL 2018: 27th Int. Conf. Metallurgy and Materials. Ostrava, Tanger, 1264-1269.]Search in Google Scholar
[Wang, N., Tai, K., 2010. Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms. Computers and Structures, 88, 1064–73. DOI: 10.1016/j.compstruc.2010.06.00210.1016/j.compstruc.2010.06.002]Open DOISearch in Google Scholar
[Wang, D., Zhang, W., Yang, J., Wang, Z., 2012. A virtual punching method for shape optimization of openings on curved panels using CAD-based boolean operations. Computer Aided Design, 44, 388–99. DOI: 10.1016/j.cad.2012.01.00310.1016/j.cad.2012.01.003]Open DOISearch in Google Scholar
[Wang, N.F., Tai, K., 2010. Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms. Computers and Structures, 88, 1064-1073. DOI: 10.1016/j.compstruc.2010.06.00210.1016/j.compstruc.2010.06.002]Open DOISearch in Google Scholar