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Browser-based Harnessing of Voluntary Computational Power

-162 [57] Meeds E., Hendriks R., Al Faraby S., Bruntink M., Welling M., MlitB:Machine Learning in the Browser, PeerJ Computer Science , 1 , July 2015, e11 [58] Merelo-Guervos J.J., P. A. Castillo, J. L. J. Laredo, A. Mora Garcia and A. Prieto, Asynchronous distributed genetic algorithms with Javascript and JSON. 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) , Hong Kong, 2008, pp. 1372-1379 [59] Merelo-Guervos J.J, Garcia-Sanchez P., Designing and Modeling a Browser-Based Distributed Evolutionary

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Universes and simulations: Civilizational development in nested embedding

Technology, 7(1), 2001. [29] Harrison E. R. The natural selection of universes containing intelligent life. Quarterly Journal of the Royal Astronomical Society, 36:193, 1995. [30] Heudin J. Virtual Worlds: Synthetic Universes, Digital Life, and Complexity. New England Complex Systems Institute Series on Complexity. Westview Press, 2004. [31] Hornby G. S. Measuring complexity by measuring structure and organization. In IEEE Congress on Evolutionary Computation, pages 2017-2024. IEEE, 2007. [32] Jenkins P

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Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Computer Science, pp.388-401, (2001). [16] International Software Testing Qualifications Board: IEEE Standard glossary of terms used in Software Engineering, (2011). [17] Jain A., Tarwani S., Chug A.: An Empirical Investigation of Evolutionary Algorithm for Software Maintainability Prediction. In Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), pp.1-6, IEEE, India, (2016). [18] Jin C., Liu. J.A.: Applications of Support Vector Machine and Unsupervised Learning for Predicting Maintainability using Object-Oriented Metrics. In

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Analysis of facial expression movements and the phenomenon of frontal asymmetry as the basis for automation of the research of hidden cognitive attitudes. Some basic remarks

. Georg Misch w poszukiwaniu postmetafizycznej metafizyki , Warszawa, 2007. [25] Soon Ch.S., Braas M, Heinze H.J., Haynes J.D., Unconscious determinants of free decisions in the human brain, in: Nature Neuroscience 5, 2008, 543-545. [26] Stacewicz P., Evolutionary Schema of Modeling Based on Genetic Algorithms, in: Studies in Logic, Grammar and Rhetoric 40 (53), 2015, 219-238. [27] Stacewicz P., Wlodarczyk A., Modelling in the context of computer science – a methodological approach, in: Studies in Logic, Grammar and Rhetoric 20 (33), 2010, 155

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Structural Analysis of Benchmarking Fitness Landscapes

References T. Jones. Evolutionary Algorithms, Fitness Landscapes and Search. Albuquerque: The University of New Mexico, 1995. C. R. Reeves, J. E. Rowe. Genetic Algorithms - Principles and Perspectives. A Guide to GA Theory. Springer, 2002. V. K. Vassilev, T. C. Fogarty, J. F. Miller. Information Characteristics and the Structure of Landscapes. Evolutionary Computation , 8(1), Mar. 2000, pp. 31-60. P. Merz, B. Freisleben. Fitness Landscape Analysis and

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Simulation-Based Analysis of Fitness Landscape in Optimisation

References Jones T. Evolutionary Algorithms, Fitness Landscapes and Search. - Albuquerque: The University of New Mexico, 1995, 224 p. Langdon W.B., Poli R. Foundations of Genetic Programming. - Berlin, Heidelberg: Springer-Vorlag, 2002, 260 p. Reeves C.R., Rowe J.E. Genetic Algorithms - Principles and Perspectives. A Guide to GA Theory. - Springer, 2002, 344 p. Vassilev V.K., Fogarty T.C., Miller J.F. Information Characteristics and the Structure of

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An Integrated Approach to Product Delivery Planning and Scheduling

. L. Kaufman and P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. Hoboken, NJ: John Wiley & Sons, Inc., 1990. K. Deb et al., "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II", IEEE Transactions on Evolutionary Computation , 6(2), pp. 182-197, 2002. S. Wagner, "Heuristic Optimization Software Systems - Modeling of Heuristic Optimization Algorithms in the HeuristicLab Software Environment", PhD Thesis, Institute for Formal Models and Verification, Johannes Kepler

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Mining Online Store Client Assessment Classification Rules with Genetic Algorithms

. Sivanandam, S. N. Deepa, Introduction to Genetic Algorithms. Berlin: Springer, 2008, pp. 39-129. R. Sarker, K-H. Liang, C. Newton, A new multiobjective evolutionary algorithm. Eur J Oper Res 2002, pp. 12-23. J. Horn, N. Nafpliotis and E. Goldberg, A niched Pareto genetic algorithm for multiobjective optimization. Orlando, USA: IEEE; 1994, pp. 82-87. R. Kohavi, R. Quinlan, Decision Tree Descovery: Handbook of Data Mining and Knowledge Discovery. USA: University Press, 1999, pp. 267

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Practice of Web Data Mining Methods Application

References Araya S., Silva M., Weber R. Identifying web usage behavior of bank customers // Berlin: Springer, October 2003. - P 951-958. Ajith A., Vitorino R. Web Usage Mining Artificial Ant Colony Clustering and Linear Genetic Programming // CEC'03 - Congress on Evolutionary Computation / IEEE Press, ISBN 078-0378-04-0, 8-12 December 2003. - Canberra, Australia P. 1384-1391. Software Discipulus™ Web Site URL: http://www.rmltech.com/ Ye S., Wen J., Ma W. A

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Mapping Implementation for Multi-robot System with Glyph Localisation

References [1] S. Thrun, Probabilistic robotics. The MIT Press, 2005 [2] http://www.irobot.com/us/robots/home/roomba.aspx cited: 16.08.2012. [3] H. Durrant-Whyte, T. Bailey, Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms, Robotics and Automation Magazine, 2006 [4] S. Thrun. Robotic mapping: A survey. Exploring artificial intelligence in the new millennium , 2003 [5] J.-G. Kang, S.-Y. An, S. Kim, S.-Y. Oh, Sonar-Based Simultaneous

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