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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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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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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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Primal–Dual Type Evolutionary Multiobjective Optimization

References Coello Coello C. A., Van Veldhuizen D.A., and Lamont G.B. (2002), Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York. Deb K. (2001), Multi-objective Optimization Using Evolutionary Algorithms. JohnWiley and Sons, Chichester. Hanne T. (2007), A multiobjective evolutionary algorithm for approximating the efficient set. European Journal of Operational Research, 176, 1723-1734. Kaliszewski I., Miroforidis J. (2012), Real and virtual

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Bias and No Free Lunch in Formal Measures of Intelligence

References Auger, A. and O. Teytaud. Continuous lunches are free! Proceedings of the 9th annual conference on Genetic and evolutionary computation (ACM SIGEVO 1997) , pages 916-922. Hutter, M. Universal Artificial Intelligence: Sequential Decisions based on Algorithmic Probability . Springer, Berlin. 2004. 300 pages. Legg, S. and M. Hutter. Proc. A Formal Measure of Machine Intelligence. 15th Annual Machine Learning Conference of Belgium and The Netherlands (Benelearn 2006

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Feature Reinforcement Learning: Part I. Unstructured MDPs

Problems: Sequential Allocation of Experiments . London: Chapman and Hall. Brafman, R. I., and Tennenholtz, M. 2002. R-max - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning. Journal of Machine Learning Research 3:213-231. Cover, T. M., and Thomas, J. A. 2006. Elements of Information Theory . Wiley-Intersience, 2nd edition. Dearden, R.; Friedman, N.; and Andre, D. 1999. Model based Bayesian Exploration. In Proc. 15th Conference on Uncertainty in Artificial

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Learning and decision-making in artificial animals

International joint conference on artificial intelligence (IJCAI), 4246. Jonsson, A., and Barto, A. G. 2001. Automated state abstraction for options using the U-tree algorithm. In Advances in neural information processing systems, 1054-1060. Keramati, M., and Gutkin, B. S. 2011. A reinforcement learning theory for homeostatic regulation. In Advances in neural information processing systems, 82-90. Langton, C. G. 1997. Artificial life: An overview. MIT Press. LeCun, Y.; Bengio, Y.; and Hinton, G. 2015. Deep

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