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Journal of Artificial Intelligence and Soft Computing Research
The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology
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Enhancing Island Model Genetic Programming by Controlling Frequent Trees

, T. Uno, S. ichi Nakano, in Proc. of the 6th Intl. Conf. on Discovery Science (Springer-Verlag, 2003), pp. 47–61 [24] C. Fillon, A. Bartoli, in Proceedings of the 9th European conference on Genetic Programming EuroGP’ 06 (2006), pp. 13–23 [25] S. Luke, G.C. Balan, L. Panait, in Proceedings of the 2003 international conference on Genetic and evolutionary computation GECCO’03: Part II (2003), pp. 1729–1739 [26] K. Yanai, H. Iba, in Proceedings of the 2003 Congress on Evolutionary Computation CEC’03, vol. 3 (2003), vol. 3, pp. 1618–1625 [27] W

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Electrostatic Force Microscopy Measurement System for Micro-topography of Non-conductive Devices

] Sun, Z., Wang, X., Han, B. (2013). Dielectric property of binary phase composite and its interface investigated by electric force microscope. Acta Physica Sinica , 62 (03), 95-100. (in Chinese) [13] Villeneuvefaure, C., Boudou, L., Makasheva, K., Teyssedre, G. (2017). Methodology for extraction of space charge density profiles at nanoscale from Kelvin probe force microscopy measurements. Nanotechnology , 28 (50), 505701. [14] Boularas, A., Baudoin, F., Villeneuvefaure, C., Clain, S., Teyssedre, G. (2014). Multi-dimensional modelling of electrostatic

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Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning

neural network, in: Hybrid neural systems, Springer, 2000, pp. 240-254 [4] G. Bologna, A study on rule extraction from several combined neural networks, International journal of neural systems 11, 03, 2001, 247-255 [5] G. Bologn, Is it worth generating rules from neural network ensembles?, Journal of Applied Logic 2, 3, 2004, 325-348 [6] A. A. Freitas, Comprehensible classification models: a position paper, ACM SIGKDD explorations newsletter 15, 1, 2014, 1-10 [7] J. Chorowski, J. M. Zurada, Learning

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Detection of Gearbox lubrication Using PSO-Based WKNN

, M. (2007). A modified probabilistic neural network for partial volume segmentation in brain MR image. IEEE Transactions on Neural Network , 18 (5), 1424-1432. [22] Chen, B., Liu, H., Chia, J., Bao, Z. (2009). Large margin feature weighting method via linear programming. IEEE Transactions on Knowledge and Data Engineering , 21 (10), 1475-1488. [23] Middlemiss, M.J., Dick, G. (2003). Weighted feature extraction using genetic algorithms for intrusion detection. In The 2003 Congress on Evolutionary Computation (CEC ‘03) , 8

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Information-measuring System to Study the Thermocouple with Controlled Temperature Field

Committee of Legal Metrology . . [9] Pohrebennyk, V., Mitryasova, O., Dzhumelia, E., Kochanek, A. (2017). Evaluation of surface water quality in mining and chemical industry. In Proceedings of the 17th International Multidisciplinary Scientific GeoConference (SGEM 2017). SGEM, Vol. 17 (51), 425-432. [10] Zhang, Y., Chen, B., Pan, G., Zhao, Y. (2019). A novel hybrid model based on VMD-WT and PCA-BPRBF neural network for short-term wind speed forecasting. Energy Conversion and Management , 195, 180

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Common Driving Notification Protocol Based on Classified Driving Behavior for Cooperation Intelligent Autonomous Vehicle Using Vehicular Ad-Hoc Network Technology

, P. Medici, P. Versari, Extensive Tests of Autonomous Driving Technologies, IEEE Transactions on Intelligent Transportation Systems, 14, 2013, pp. 1403-1415. [4] J. Choi, J. Lee, D. kim, G. Soprani, P. Cerri, A. Broggi, K Yi, Environment-Detection-and- Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment, IEEE Transactions on Intelligent Transportation Systems, 13, 2012, pp. 974-982. [5] J. Hardy, M. Campbell, Contingency Planning Over Probabilistic Obstacle Predictions for Autonomous Road Vehicles, IEEE

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Metrological Software Test for Simulating the Method of Determining the Thermocouple Error in Situ During Operation

-779. [4] Krolczyk, G.M., Maruda, R.W., Nieslony, P., Wieczorowski, M. (2016). Surface morphology analysis of Duplex Stainless Steel (DSS) in Clean Production using the Power Spectral Density. Measurement, 94, 464-470. [5] Kočí, V., Maděra, J., Jerman, M., Trník, A., Černý, R. (2014). Determination of the equivalent thermal conductivity of complex material systems with large-scale heterogeneities. International Journal of Thermal Sciences , 86, 365-373. [6] Novák, M., Náprstková, N., Józwik, J. (2015). Analysis of the surface profile and its material share

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Impact of Learners’ Quality and Diversity in Collaborative Clustering

. Perner and A. Imiya, Eds. Springer Berlin Heidelberg, 2005, vol. 3587, pp. 610–619. [Online]. Available: [7] W. Pedrycz, Collaborative fuzzy clustering, Pattern Recognition Letters, vol. 23, no. 14, pp. 1675–1686, 2002 [8] N. Grozavu, M. Ghassany, and Y. Bennani, Learning confidence exchange in collaborative clustering, in IJCNN, 2011, pp. 872–879 [9] W. Pedrycz and K. Hirota, A consensus-driven fuzzy clustering, Pattern Recogn. Lett., vol. 29, no. 9, pp. 1333–1343, 2008 [10] N. Grozavu, G. Cabanes, and Y

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Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation

References [1] A. E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 3 (2), 124–141, 1999. [2] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotic Systems, in: Proceedings of the NATO Advanced Workshop on Robots and Biological Systems. Tuscany, Italy, 1989. [3] P.J. Angeline, Adaptive and self-adaptive evolutionary computation, in: M. Palaniswami, Y. Attikiouzel, R.J. Marks, D.B. Fogel, T. Fukuda (Eds.), Computational Intelligence: A Dynamic System

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