Диссертация (1095062), страница 23
Текст из файла (страница 23)
– М.: МЭСИ, 2013. –С. 90-95.51. Чернодуб, А.Н. Обзор методов нейроуправления / А.Н. Чернодуб, Д.А. Дзюба// Проблемы программирования. – 2011. – № 2. – С. 79 – 94.52. Черноруцкий, И. Г. Методы принятия решений: учебное пособие / И. Г.Черноруцкий. – СПб.: БХВ-Петербург, 2005. – 416 с.53. Щербина, Ю.В. Проектирование систем автоматизации и управления методамитеории нечетких множеств / Ю.В. Щербина, К.В.
Смыкова // Вестник МГУПимени Ивана Федорова. – 2011. – №1. – С. 192-196.54. Энгель,Е.А.Методинтеллектуальныхвычисленийдляуправленияконфигурацией манипуляционного робота / Е.А. Энгель // Вестник СевероВосточного федерального университета им. М.К. Аммосова. – 2015. – № 3 (47). –С. 127-137.55. Angeline, P. An Evolutionary Algorithm that Constructs Recurrent NeuralNetworks / P. Angeline, G. Saunders, J. Pollack // IEEE Transactions on NeuralNetworks and Learning Systems.
– 1994. – Vol 5. – № 1. – pp. 27-54.56. Arbib, M. A. The handbook of brain theory and neural networks / M. A. Arbib. –Cambridge: The MIT Press, 2002. – 1309 p.15157. Ashlock, D. Evolutionary Computation for Modeling and Optimization / D.Ashlock. – Guelph: Springer & Business Media, 2005. – 572 p.58. Baeck, T. Evolutionary Algorithms in Theory and Practice / T. Baeck. – N.Y.:Oxford University Press, 1996. – 328 p.59. Baeck, T. Evolutionary Computation: An Overview / T.
Baeck, H.-P. Schwefel //Nagoya: Conference on Evolutionary Computation (ICEC 1996). – pp. 20-29.60. Barnett, L. Evolutionary search on fitness landscapes with neural networks: PhDthesis / L. Barnett. – Sussex: University of Sussex, 2003. – 199 p.61. Beyer, H.-G. How to analyze evolutionary algorithms / H.-G. Beyer, H.-P.Schwefel, I. Wegener // Theoretical Computer Science. – 2002. – № 1. – pp. 101130.62.
Bhanu, B. Evolutionary Synthesis of Pattern Recognition Systems / B. Bhanu, Y.Lin, K. Krawiec. – N.Y.: Springer, 2005. – 314 p.63. Bramer, M. Artificial Intelligence Applications and Innovations / M. Bramer,V. Devedzic. – N.Y.: Kluwer, 2004. – 499 p.64. Cardamone, L. Evolutionary Learning and Search–Based Content Generation inComputer Games: PhD thesis / L. Cardamone. – Milano: Politecnico di Milano,2012. – 199 p.65. Chen, X. Multi–Facet Survey on Memetic Computation / X. Chen, Y.S.
Ong //IEEE Transactions on Evolutionary Computation. – 2011. – № 5. – pp. 591-607.66. Clune, J. On the performance of indirect encoding across the continuum ofregularity / J. Clune, K. O. Stanley // IEEE Transactions on EvolutionaryComputation. – 2011. – pp. 5–27.67. De Jong, K. A. Generation gaps revisited / K.A. De Jong, J.
Sarma // Whitley:Foundations of Genetic Algorithms. – 1993. – № 2. – pp. 19-28.68. Deb, K. Self–adaptive genetic algorithms with simulated binary crossover / K. Deb,H.-G. Beyer // Evolutionary Computation. – Vol 9. – № 2. – 2001. – pp. 197-221.69. Dolson, E. Applying neural pruning to NEAT / E. Dolson, D.
Park // Lansing:Adaptive Robotics Spring 2012. – 2012. – pp. 305-322.15270. Floreano, D. Neuroevolution: from architectures to learning / D. Floreano, P. Durr,C. Mattiussi // Evolutionary Intelligence. – 2008. – Vol 1. – № 1. – pp. 47-62.71. Forsyth, D. Computer Vision: A Modern Approach / D. Forsyth, J. Ponce. – NewJersey: Prentice Hall. – 2002. – 720 p.72. Fulcher, J.
Advances in applied artificial intelligence / J. Fulcher. – Hershey: IdeaGroup Publishing, 2006. – 309 p.73. Gauci, S. Generating Large–Scale Neural Networks Through DiscoveringGeometric Regularities / S. Gauci // N.Y.: Proceedings of the Genetic andEvolutionary Computation Conference (GECCO–2007), 2007. – 18 p.74. Gomez, F. Efficient Non–Linear Control through Neuroevolution / F. Gomez, J.Schmidhuber, R. Miikkulainen // Berlin: Proceedings of the European Conference onMachine Learning, 2006. – pp. 654-662.75.
Gomez, J. Solving non–markovian control tasks with neuroevolution / J. Gomez, R.Miikkulainen // SanFrancisco: Proc. of the International Joint Conference onArtificial Intelligence, 1999. – pp. 1356-1361.76. Gomez, J. Self adaptation of operator rates in evolutionary algorithms / J. Gomez //Washington: Proc. of Genetic and Evolutionary Computation Conference 2004(GECCO 2004), 2004. – pp. 1162-1173.77. Gruau, F.
A Comparison between Cellular Encoding and Direct Encoding forGenetic Neural Networks / F. Gruau, D. Whitley, L. Pyeatt // San Francisco:Proceedings of the First Annual Conference, 1996. – pp. 81-89.78. Hausknecht, M. Neuroevolution Approach to General Atari Game Playing / M.Hausknecht, J. Lehman // Nottingham: Computational Intelligence and AI in Games,IEEE, 2014. – № 4.
– pp. 355-366.79. Hinterding, R. Adaptation in evolutionary computation: A survey / R. Hinterding, Z.Michalewicz, A. Eiben // Indianapolis: Proc. of the 4th IEEE InternationalConference on Evolutionary Computation, 1997. – pp. 65-69.15380. Hinterding, R. Gaussian mutation and self–adaptation in numeric genetic algorithms/ R. Hinterding // Cambridge: IEEE Press: IEEE International Conference onEvolutionary Computation, 1995. – pp. 384-389.81. Hofland, M.
Combining Manual Training and Enforced Sub–Populations to ControlForest Fires: Master thesis / M. Hofland. – Utrecht: Utrecht University, 2007. –163 p.82. Holland, J.H. Building blocks, cohort genetic algorithms and hyperplane–definedfunctions / J.H. Holland // Massachusetts: Massachusetts Institute of Technology:Evolutionary computation. – 2000. – № 4. – pp. 373-391.83.
Igel, C. Evolutionary optimization of neural systems: The use of strategy adaptation/ C. Igel, S. Wiegand, F. Friedrichs // Bochnm: Birkhäuser Basel, Trends andApplications in Constructive Approximation. International Series of NumericalMathematics, 2005 – 23 p.84.
Igel, C. Neuroevolution for reinforcement learning using evolution strategies / C.Igel // Bochum: Proc. of Congress on Evolutionary Computation (CEC 2003), 2003.– pp. 2588-2595.85. James, D. A comparative analysis of simplification and complexification in theevolution of neural network topologies / D. James, P. Tucker // Genetic andEvolutionary Computation Conference (GECCO–2004), 2004. – pp. 441-459.86. Janghel, R.R. Breast cancer diagnostic system using Symbiotic Adaptive Neuro–evolution / R.R. Janghel, A. Shukla, R. Tiwari, R. Kala // Paris: Soft Computing andPattern Recognition. – 2010.
– pp. 326-329.87. Jorgensen, T. D. Pruning artificial neural networks using neural complexitymeasures / T. D. Jorgensen, B. P. Haynes, C. Norlund // International journal ofneural systems. – 2008. – Vol 18. – № 5. – pp. 389-403.88. Karkavitsas, G. Automatic Music Genre Classification Using Hybrid GeneticAlgorithms // G. Karkavitsas, G. Tsihrintzis // Intelligent Interactive MultimediaSystems and Services, 2011 – 335 p.15489. Kassahun, Y.
Efficient reinforcement learning through evolutionary acquisition ofneural topologies / Y. Kassahun, G. Sommer // 13th European Symposium onArtificial Neural Networks, 2005. – pp. 259-266.90. Kenneth, O. Evolving Neural Networks through Augmenting Topologies/ O.Kenneth, R. Miikkulainen // Evolutionary Computation, 2002. – Vol 10. – № 2. – pp.99-127.91. Khlopkova, O.A. Application of combinations of genetic algorithms and neuralnetworks in online learning services / O.A. Khlopkova // 5th International ScientificConference Science and Society.
– London: SCIEURO, 2013. – pp. 78-83.92. Kita, H. A comparison study of self–adaptation in evolution strategies and real–coded genetic algorithms / H.A. Kita // Evolutionary Computation, 2000. – Vol 9. –№ 2. – pp. 223-241.93. Kitano, H. Designing neural network using genetic algorithm with graph generationsystem / H. Kitano // Pittsburgh: Complex Systems. – 1990. – № 4. – pp. 461-476.94. Komleva, N. Development of business models of open education based on newintellect technologies / N. Komleva, N. Tikhomirova // Zermatt: The EADTU's annualconference 2010,Proceedingsof Strategies and Business Models for LifelongLearning/Networking Conference, 2010. – pp. 409-415.95.
Koza, J. Genetic programming: a paradigm for genetically breeding computerpopulation of computer programs to solve problems / J. Koza. – Cambridge: MITPress, 1992. – 315 p.96. Liles, W. Introduction to Schema Theory: a survey lecture of pessimistic & exactschema theory / W/ Liles, P. Wiegand. – Virginia: George Mason University EC labActivities, 2002 – 114 p.97. Lubberts, A. Co–evolving a go–playing neural network / A. Lubberts, R.Miikkulainen // San Francisco: Process of Coevolution: Turning AdaptiveAlgorithms upon Themselves, Birds–of–a–Feather Workshop, Genetic andEvolutionary Computation Conference (GECCO–2001), 2001.
– pp. 14-19.15598. Luger, G. F. Artificial intelligence: structures and strategies for complex problemsolving. Reading / G.F. Luger. – Wesley: Pearson, 1998. – 824 p.99. Maniezzo, V. Genetic evolution of the topology and weight distribution of neuralnetworks / V. Maniezzo // Transactions of on Neural Networks. – 1994. – Vol 5. –№ 1. – pp. 39-53.100.
![](https://s.studizba.com/z.php?f=/templates/si/i/noavatar.png&w=100&h=100&t=1"){kind=avatar}
