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Hybridisation of Evolutionary Algorithms for Solving Multi-Objective Simulation Optimisation Problems

References Gosavi A. Simulation Optimisation: Parametric Optimisation Techniques and Reinforcement Learning. - Norvell: Kluwer Academic Publishers, 2003, 551 p. Deb K. Evolutionary algorithms for multi-criterion optimisation in engineering design. Proceedings of Evolutionary Algorithms in Engineering and Computer Science. - London: John Wiley and Sons, Ltd., 1999. 135-161 p. Jourdan L., Basseur M., Talbi E.G. Hybridizing exact methods and metaheuristics: A taxonomy. European

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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: 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] 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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Use of Linear Genetic Programming and Artificial Neural Network Methods to Solve Classification Task

References Takahaši A. Neural Networks in Fingerprint Classification Problem // Scientific Proceedings of Riga Technical University, Series 5, Vol. 36 (2008), pp. 83.-92. Brameier M., Banzhaf W. Linear Genetic Programming - USA: Springer, 2007. Brameier M., Banzhaf W. A Comparison of Linear Genetic Programming and Neural Networks in Medical Data Mining // Evolutionary Computation, Issue 1, Vol. 5 (2001), pp. 17-26. Brameier, M. and W. Banzhaf. Linear

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Optimal Pixel-to-Shift Standard Deviation Ratio for Training 2-Layer Perceptron on Shifted 60 × 80 Images with Pixel Distortion in Classifying Shifting-Distorted Objects

R eferences [1] K. Fukushima and S. Miyake, “Neocognitron: A new algorithm for pattern recognition tolerant of deformations and shifts in position,” Pattern Recognition , vol. 15, iss. 6, 1982, pp. 455–469. [2] K. Fukushima, “Self-organization of shift-invariant receptive fields,” Neural Networks , vol. 12, iss. 6, 1999, pp. 791–801. [3] V. V. Romanuke, “An attempt for 2-layer perceptron high performance in classifying shifted monochrome 60-by-80-images via training with pixel-distorted shifted

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Approximation of Isomorphic Infinite Two-Person Non-Cooperative Games by Variously Sampling the Players’ Payoff Functions and Reshaping Payoff Matrices into Bimatrix Game

.03.004 [7] X. Chen and X. Deng, “Recent development in computational complexity characterization of Nash equilibrium,” Computer Science Review , vol. 1, iss. 2, pp. 88–99, Dec. 2007. [8] D. Friedman, “On economic applications of evolutionary game theory,” Journal of Evolutionary Economics , vol. 8, iss. 1, pp. 15–43, March 1998. [9] S. Brusco, “Perfect Bayesian implementation in economic environments,” Journal of Economic Theory , vol. 129, iss. 1, pp. 1–30, July 2006

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