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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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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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Is Brain Emulation Dangerous?

. Hansen, M.; Köhntopp, K.; Pfitzmann, A. 2002. The Open Source approach - opportunities and limitations with respect to security and privacy, Computers & Security, 21(5):461-471. Hanson, R. 1998. Burning the Cosmic Commons: Evolutionary Strategies for Interstellar Colonization. Available at http://hanson.gmu.edu/filluniv.pdf Hanson, R. 2008. Economics of the singularity. IEEE Spectrum, June 2008:37-42. Humphreys, M. 2002. Economics and Violent Conflict. Working paper UNICEF. Kurzweil, R. 2012. How to Create a Mind: The

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Artificial General Intelligence: Concept, State of the Art, and Future Prospects

-Simon Scale. Number 11. Williams & Wilkins Company. Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford University Press. Brooks, R. A. 2002. Flesh and machines: How robots will change us. Pantheon Books New York Cassimatis, N. 2007. Adaptive algorithmic hybrids for human-level Artificial Intelligence. In Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 94-112. Damer, B.; Newman, P.; Gordon, R.; and Barbalet, T. 2010. The EvoGrid: simulating pre

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