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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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Strategy for Individuals Distribution by Incident Nodes Participation in Star Topology of Distributed Evolutionary Algorithms

References 1. Goldberg, D. E. Genetic and Evolutionary Algorithms Come of Age. – Communications of the ACM, Vol. 37 , 1994, No 3, pp. 113-119. 2. Pappa, G., G. Ochoa, M. Hyde, A. Freitas, J. Woodward, J. Swan. Contrasting Meta-Learning and Hyper-Heuristic Research: The Role of Evolutionary Algorithms. – Genetic Programming and Evolvable Machines, Vol. 15 , 2014, Issue 1, pp. 3-35. 3. Adamidis, P. Review of Parallel Genetic Algorithms Bibliography. Tech. Rep. Version 1. Aristotle University of Thessaloniki, Thessaloniki, Greece, 1994. 4

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Evolving Non-Dominated Parameter Sets for Computational Models from Multiple Experiments

optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2:221-248. Stewart, T., andWest, R. 2010. Testing for equivalence: A methodology for computational cognitive modelling. Journal of Artificial General Intelligence 2:69-87. Tor, K., and Ritter, F. E. 2004. Using a Genetic Algorithm to Optimize the Fit of Cognitive Models. In Proceedings of the Sixth International Conference on Cognitive Modeling, 308-313. Mahwah, NJ: Lawrence Erlbaum.

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Spectrum Allocation of Cognitive Radio Based on Autonomy Evolutionary Algorithm

Access. - In: Proc. of IEEE International Conference on Communications, Vol. 5, 2005, pp. 3132-3136. 13. Anumandla, K. K., S. Kudikala, B. A. Venkata, S. L. Sabat. Spectrum Allocation in Cognitive Radio Networks Using Firefly Algorithm. - Swarm, Evolutionary, and Memetic Computing, Vol. 8297, 2013, pp. 366-376. 14. Li, X. B., L. Lui, A. W. Shi, M. A. Kai, X. P. Guan. Cognitive Radio Spectrum Allocation Based on an Improved Population Adaptive Artificial Bee Colony Algorithm. - Journal of Applied Sciences, Vol. 31, 2013, No 5, pp. 448

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References Adamczewski, Z. (1992). Krzywa łańcuchowa jako linia realna. Przegląd Geodezyjny nr 4 . Warszawa. Arabas, J. (2001). Wykłady z algorytmów ewolucyjnych . Warszawa: Wydawnictwo Naukowo - Techniczne. Civivioglu P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm, Computers and Geosciences, Volume 46, pp. 229-247 . Goldberg, D. E. (2003). Algorytmy genetyczne i ich zastosowanie . Warszawa: Wydawnictwo Naukowo

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Agent-Based Evolutionary Method of Simulation the Co2 Emission Permits Market

References [1] Alkemade F., La Poutre H., Amman H. M. Robust Evolutionary Algorithm Design for Socio-economic Simulation, Computational Economics 28 , 2006, pp. 355-470. [2] Bartoszczuk P., Horabik J. Tradable Permit Systems: Considering Uncertainty in Emission Estimates, Water Air Soil Pollution: Focus (2007) 7 , , Springer Verlag, 2007, pp 573-579. [3] Bonatti M., Ermoliev Y., Gaivoronski A. Modeling of multi-agent market systems in the presence of uncertainty: The case of information economy

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Handling the Multiplicity of Solutions in a Moea Based PDA-THESEUS Framework for Multi-Criteria Sorting

References [1] Butler, J., Jia, J., Dyer, J. (1997): Simulation techniques for the sensitivity analysis of multi-criteria decision models, European Journal of Operational Research 103, 3, 531-546. [2] Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A. (2007): Evolutionary Algorithms for Solving Multi_Objective Problems, Second Edition. Springer, New York. [3] Corrente, S., Doumpos, M., Greco, S., Slowinski, R., Zopounidis, C. (2015): Multiple criteria hierarchy process for sorting problems based on ordinal

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Evolutionary multi-objective optimization for inferring outranking model’s parameters under scarce reference information and effects of reinforced preference

References [1] Butler, J., Jia, J., Dyer, J., Simulation techniques for the sensitivity analysis of multi-criteria decision models, European Journal of Operational Research 103, 1997, 531-546. [2] Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A., Evolutionary Algorithms for SolvingMultiObjective Problems, Second Edition. Springer, New York, 2007. [3] Deb, K., Multi-Objective Optimization using Evolutionary Algorithms, John Wiley & Sons, Chichester-New York-Weinheim-Brisbane-Singapore-Toronto, 2001

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Hybrid MPPT Algorithm for PV Systems Under Partially Shaded Conditions Using a Stochastic Evolutionary Search and a Deterministic Hill Climbing

generation , Ren. Sust. En. Rev., 2015, 41, 284–297. [4] K ot R., S tynski S., M alinowski M., Hardware methods for detecting global maximum power point in a PV power plant , Proc. IEEE Int. Conf. Industrial Technology, ICIT, 2015, 2907–2914. [5] L ogeswarana T., S enthilkumarb A., A review of maximum power point tracking algorithms for photovoltaic systems under uniform and non-uniform irradiances , 4th Int. Conf. Advances in Energy Research, ICAER 2013, India, 2013, 228–235. [6] I shaque K., S alam Z., A mjad M., M ekhilef S., An improved

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Evolutionary Algorithms Approach for Cutting Stock Problem

References [1] C. Nitsche, G. Scheithauer, J. Terno, Tighter relaxations for the cutting stock problem, European Journal of Operational Research, No. 112, pp. 654-663, 1999 [2] T.H. Cormen , C.E. Leiserson, R.L. Rivest, C. Stein, Introduction to Algorithms, Second Edition, The Massachusetts Institute of Technology, 2001 [3] S.J. Russel, P. Norvig, Artificial Intelligence, A Modern Approach, Second Edition, Pearson Education, 2003 [4] M. Melanie, An Introduction to Genetic Algorithms, The

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