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REFERENCES Arabas, J. (2004). Wykłady z algorytmów ewolucyjnych ( Lectures on evolutionary algorithms ), Warszawa: Wydawnictwo Naukowo-Techniczne. Barnes, M.J., Chen, J. Y. C., Hill, S. (2017). Humans and Autonomy: Implications of Shared Decision-Making for Military Operations. Retrieved January 29, 2020 from: https://apps.dtic.mil/dtic/tr/fulltext/u2/1024840.pdf . Brookshear, J. Glenn (2007). Computer Science: An Overview . Boston: Pearson. Bondecka-Krzykowska, I. (2016). Z zagadnień ontologicznych informatyki ( Ontological issues in computer science

FPGA in WSNs. – Journal of Software Engineering, Vol. 9 , 2015, No 1, pp. 87-95. 5. Arivubrakan, P., V. R. S. Dhulipala. Sentry Based Intruder Detection Technique for Wireless Sensor Networks. – Journal of Artificial Intelligence, Vol. 6 , 2013, No 2, pp. 175-180. 6. Huang, Y., C.-Z., Zang, H.-B. Yu. Localization Method Based on Modified Particle Swarm Optimization for Wireless Sensor Networks. – Control and Decision, Vol. 27 , 2012, No 1, pp. 156-160. 7. Zhang, W., Q. Song. An Improved DV-Hop Algorithm Based on Genetic Algorithm. – Journal of Chongqing University

References [1] R. Dechter, J. Pearl, Generalized best-first search strategies and optimality of A ∗, Journal ACM, 32, 3, (1985) 505-536. ⇒192, 199, 204 [2] I. Fekete, T. Gregorics, L. Zs. Varga, Corrections to graph-search algorithms. Proc. of the Fourth Conference of Program Designers, ELTE, Budapest, June 1-3, 1988. ⇒191 [3] T. Gregorics, Which of graphsearch versions is the best? Annales Univ. Sci. Budapest., Sect. Comput. 15 (1995) 93-108. ⇒191, 202 [4] A. Martelli, On the complexity of admissible search algorithms. Artificial Intelligence, 8, 1 (1977) 1

Polish). Moore, A. and Atkeson, C. (1993). Prioritized sweeping: Reinforcement learning with less data and less time, Machine Learning 13 (1): 103-130, DOI: 10.1007/BF00993104. Moriarty, D., Schultz, A. and Grefenstette, J. (1999). Evolutionary algorithms for reinforcement learning, Journal of Artificial Intelligence Research 11 : 241-276. Peng, J. and Williams, R. (1993). Efficient learning and planning within the Dyna framework, Adaptive Behavior 1 (4): 437-454. Reynolds, S. (2002). Experience stack reinforcement learning for off-policy control, Technical

Classification of Very High Spatial Resolution Images, IEEE Transaction on Geoscience and Remote Sensing (2006). FELENZWALB, P. F.—HUTTENLOCHER, D. P.: Efficient Graph-Based Image Segmentation, International Journal of Computer Vision 59 No. 2 (2004). KANUNGO, T.—MOUNT, D.—NETANYAHU, N.—PIATKO, C.—SILVERMAN, R.—WU, A. Y.: An Efficient k -means Clustering Algorithm: Analysis and Implementation, IEEE Transactions and Pattern Analysis and Machine Intelligence 24 No. 7 (2002). MAT-ISA, N. A.—MASHOR, M. Y.—OTHMAN, N. H.: Comparison of Segmentation Performance of Clustering

References [1] V.E. Alekseev, On the ocal restrictions effect on the complexity of finding the graph independence number , in: Combinatorial-Algebraic Methods in Applied Mathematics, A. Markov (Ed(s)), (Gorkiy University, 1983) 3-13, in Russian. [2] V.E. Alekseev, A polynomial algorithm for finding maximum independent sets in fork-free graphs, Discrete Anal. Operation Research Serie 1 6(4) (1999) 3-19, in Russian. [3] V.E. Alekseev, Augmenting graphs for independent sets, Discrete Appl. Math. 145 (2004) 3-10. doi:10.1016/j.dam.2003.09.003 [4] A. Billionnet

Computing, 7, 3, 1978. 5. Bayer R., Moore J.: A fast matching algorithm. ACM, 20, 10, 1977. 6. Bird R.: Two dimensional pattern matching. Information Processing Letters, 6, 5, 1977. 7. Cáceres M., Puglisi S.J., Zhukova B.: Fast Indexes for Gapped Pattern Matching. In: Chatzigeorgiou A. et al. (eds.) SOFSEM 2020: Theory and Practice of Computer Science. SOFSEM 2020. Lecture Notes in Computer Science, Vol. 12011. Springer, Cham, 2020, DOI 10.1007/978-3-030-38919-2_40. 8. Charalampopoulos P., Kociumaka T., Pissis S.P., Radoszewski J., Rytter W., Straszyński J., Waleń T

References [1] S.P. Adam, S.A.N. Alexandropoulos, P.M. Pardalos, M.N. Vrahatis, No free lunch theorem: a review, Approximation and Optimization, Springer, 57-82, 2019. [2] E.S. Ali, S.M. Abd-Elazim, Bacteria foraging optimization algorithm based load frequency controller for interconnected power system, Int. J. of Electrical Power & Energy Systems, 33(3), 633-638, 2011. [3] T. de Fátima Araújo, W. Uturbey, Performance assessment of PSO, DE and hybrid PSO–DE algorithms when applied to the dispatch of generation and demand, Int. J. of Electrical Power & Energy

Cybernetics, Vol. SSC-4, 1968, No 2, pp. 100-107. 5. Li, L., Y. Tao, C. Langton. Current Situation and Future of Researching Move Robot Technology. - Robot, Vol. 24, 2002, No 5, pp. 475-480. 6. Zhou, W. The Path Planning of Rescuing and Probing Robot in the Coal Mine and Trajectory Tracking Control Studying. Shanxi: Taiyuan University of Technology, 2011. 7. Xia, L. AFSA and Its Application. Guangxi, Guangxi University for Nationalities, 2009. 8. Yu, H., J. Wei, J. Li. Transformer Fault Diagnosis Based on Improved Artificial Fish Swarm Optimization Algorithm and BP Network

, Binnur, et al. 2006. Active appearance model-based facial composite generation with interactive nature-inspired heuristics. In: Multimedia Content Representation, Classification and Security. Springer Berlin Heidelberg, p. 183-190. 5. GIBSON, Stuart J.; SOLOMON, Christopher J.; BEJARANO, Alvaro. 2003. Pallares Synthesis of Photographic Quality Facial Composites using Evolutionary Algorithms. In: BMVC, pp. 1-10. 6. SOLOMON, Christopher J., GIBSON, Stuart J., MIST, Joseph J. 2013. Interactive evolutionary generation of facial composites for locating suspects in criminal