Эволюционные вычисления

: Литература по курсу
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Московский государственный университет путей сообщения
Опубликован: 10.10.2014 | Доступ: свободный | Студентов: 869 / 193 | Длительность: 22:10:00
Тема: Алгоритмы и дискретные структуры
Специальности: Программист, Архитектор программного обеспечения
  • 1.
    Fogel L.J., Owens A.J., Walsh M.J.
    Artificial Intelligence throw Simulated Evolution.
    Wiley,New York,1966.
  • 2.
    Holland J.P.
    Adaptation in Natural and Artificial Systems.An Introductionary Analysis With Application to Biology? Control and Artificial Intelligence.
    Univercity of Michigan,1975,210 p.
  • 3.
    Goldberg D.E.
    Genetic Algorithms in Search, Optimization and Machine Learning.
    Addison-Wesley, reading,MA.-1989.
  • 4.
    Rechenberg I.
    Evolutionstrategie: Optimierung technisher Systems nach Prinzipien der biologischen Evolution.
    Stuttgart:Fromman-Holzboog,1973.
  • 5.
    Koza J.R.
    Genetic Programming.
    Cambridge:MA:MIT Press,1992.
  • 6.
    Ивахненко А.Г.
    Самообучающиеся системы распознавания и автоматического управления.
    Киев:Техника,1969.
  • 7.
    Цыпкин Я.З.
    Основы теории обучающихся систем.
    М.:Наука.-1970.
  • 8.
    Расстригин Л.А.
    Статистические методы поиска.
    М.:Наука.-1968.
  • 9.
    Букатова И.Л.
    Эволюционное моделирование и его приложения.
    М.:Наука,1979.
  • 10.
    Букатова И.Л., Михеев Ю.И., Шаров А.М.
    Эвоинформатика: теория и практика эволюционного моделирования.
    М.:Наука.-1991.-206с.
  • 11.
    В.М.Курейчик.
    Генетические алгоритмы.
    Тагарог:Изд-во ТРТУ,1998.
  • 12.
    Гладков Л.А., Курейчик В.В., Курейчик В.М
    Генетические алгоритмы.
    М.: Физматлит,2006.
  • 13.
    Курейчик В.В., Курейчик В.М., Родзин С.И.
    Теория эволюционных вычислений.
    М.: Физматлит,2013.
  • 14.
    Скобцов Ю.А.
    Основы эволюционных вычислений.
    Донецк: ДонНТУ, 2008.- 326с.
  • 15.
    Michalevich Z.
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 16.
    Пилинский М., Рутковская Д., Рутковский Л.
    Нейронные сети, генетические алгоритмы и нечеткие системы.
    М: Горячая линия, 2004.- 452с.
  • 17.
    Deepa S.N., Sivanandam S.N.
    Introduction to genetic algorithms.
    Berlin, Heidelberg: Springer- Verlag, 2008.
  • 18.
    Macready W.G., Wolpert D.H.
    No free lunch theorems for search / Operations research
    Santa Fe Institute, 1995
  • 19.
    Macready W.G., Wolpert D. H.
    No Free Lunch Theorems for Search Technical
    1996. Report SFI-TR-95-02-010.- Sania Fe Instituie.
  • 20.
    Holland J.H.
    Adaptation in Natural and Artificial Systems. An Introductory Analysis with Application to Biology - Control and Artificial Intelligence.
    University of Michigan, -1975.-210 p.
  • 21.
    Goldberg D.E.
    Genetic Algorithms in Search, Optimization and Machine Learning.
    Addison- Wesley, MA.-1989.
  • 22.
    Гладков Л.А., Курейчик В.В, Курейчик В.М.
    Генетические алгоритмы.
    М.:Физматлит.-2006.-319с.
  • 23.
    Скобцов Ю.А.
    Основы эволюционных вычислений.
    Донецк: ДонНТУ, -2008.- 326 с.
  • 24.
    Michalevich Z .
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 25.
    Тестовые наборы для задачи коммивояжера [Электронный ресурс].
  • 26.
    Тестовые наборы для задачи коммивояжера [Электронный ресурс].
  • 27.
    Барашко А.С., Скобцов Ю.А., Сперанский Д.В.
    Моделирование и тестирование дискретных устройств.
    Киев: Наукова думка, 1992.
  • 28.
    Миронов С.В., Сперанский Д.В.
    Генетические алгоритмы для сокращения диагностической информации //Автоматика и телемеханика.
    2008.-№7.- С.146-156.
  • 29.
    Brayan D., Brgles F., Kazminski K.
    Combinational profiles of sequential benchmark circuits // Proceed. of 1989 Intern. Symposium on Sequential Circuits
    Portland, OR, USA, 1989.- P.1929-1934.
  • 30.
    Скобцов В.Ю., Скобцов Ю.А., Сперанский Д.В.
    Моделирование, тестирование и диагностика цифровых устройств.
    М.: Национальный Открытый Университет <ИНТУИТ>, 2012.
  • 31.
    Скобцов В.Ю., Скобцов Ю.А.
    Современные модификации и обобщения генетических алгоритмов // Таврический вестник компьютерных наук и математики
    Симферополь: 2004, №1. - с.60-71.
  • 32.
    Скобцов Ю.А.
    Основы эволюционных вычислений.
    Донецк: ДонНТУ, 2008.- 326с.
  • 33.
    Mitchel М.
    An introduction to genetic algorithms.
    The MIT Press, Cambridge, Massachusetts.-1998.-210 p.
  • 34.
    Пилинский М., Рутковская Д., Рутковский Л.
    Нейронные сети, генетические алгоритмы и нечеткие системы.
    М:Горячая линия, 2004.- 452с
  • 35.
    Michalevich Z.
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 36.
    Mandavilli S., Patnaik L. M.
    Adaptation in genetic algorithms. In: Genetic algorithms for pattern recognition.
    CRC Press.-1996.-P.45-64.
  • 37.
    Mahfoud S.W.
    A comparison of parallel and sequential niching methods.//Proceedings of VI International conference on genetic algorithms.
    1995.-P.136-143.
  • 38.
    Gordon V., Mathias K., Whitley D.
    Lamarkian evolution, the Baldwin effect & function optimization, in Davidor Y., Schwefel H., Manner R., editors, Parallel problem solving from nature: PPSN III
    Berlin, Springer-Verlag: 1994.- P.6-15.
  • 39.
    Grefenstette J.J.
    Lamarkian learning in multi-agent environment//Proceedings of the 4th international conference on genetic algorithms
    San Francisko, Morgan Kaufman Publishers:1991.
  • 40.
    Davidor Y.
    A genetic algorithm applied to robot trajectory generation. Davis L.editor, Handbook of genetic algorithms
    New York, Van Nostrand Reinhold, 1991.
  • 41.
    Moscato P., Norman M.
    A memetic approach for the traveling salesman problem: implementation of computational ecology for combinatorial optimization on message-passing systems//Proceedings of international conference on parallel computing & transporter application
    Amsterdam,1992.
  • 42.
    Radcliffe N., Surry P.
    Formal memetic algorithm//Proceeding - Selected Papers from AISB Workshop on Evolutionary Computing.
    P. 1-16 Springer-Verlag London, UK c1994.
  • 43.
    Dawkins R.
    The selfish gene.
    Oxford: Oxford University press,1976.
  • 44.
    Carlos Cotta, Pablo Moscato
    Hand book of memetic algorithms.
    Springer- Verlag Heidelberg, 2012.
  • 45.
    Herera F., Lozano M.
    Adaptation of genetic algorithm parametres based on fuzzy logic controllers// In F.Herera & J.Verdegay, editors. Genetic algorithms and soft computing, Physica-Verlag.
    P.95-125.
  • 46.
    Lin L., Mitsuo G., Runwei C.
    Network models and optimization.
    Springer-Verlag London Limited,2008.-701p.
  • 47.
    Eiben A., Hinterding R., Michalevicz Z.
    Adaptation in evolutionary computation: a survey//Proceedings of IEEE international conference on evolutionary computation
    Piscataway, NJ:1997.-P.65-69.
  • 48.
    Davis L.
    editor. Handbook of genetic algorithms.
    New York.- Van Nostrand Rein-hold, 1991
  • 49.
    Juldstrom B.
    What have you done for me lately adapting operator probabilities in a steady-state genetic algorithm//Proceedings 6th international conference on Gas, San Francisco
    1995.-P.81-87.
  • 50.
    Katsaras C.P., Koumousis V.K.
    Asow-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance//IEEE Transactions on Evolutionary Computation, 2006, 10(1)
    P.19-28.
  • 51.
    L. Lin and M. Gen
    "Auto-tuning strategy for evolutionary algorithms:Balancing between exploration and exploitation," Soft Computing,vol. 13
    pp. 157-168, 2009.
  • 52.
    Kelly R.B., Moel M.C., Stewart C.V.
    Reducing the search time of a steady state genetic algorithm using the immigration operator//Proceedings IEEE International Conference Toolsfor AI.- 1991
    P.500-501.
  • 53.
    Скобцов Ю.А.
    Основы эволюционных вычислений.
    Донецк: ДонНТУ, 2008.- 326с.
  • 54.
    А.И.Эль-Хатиб., Скобцов Ю.А.
    Параллельные генетические алгоритмы// Науков? прац? Донецького национального техн?чного ун?верситету, сер?я "Обчислювальна техн?ка та автоматизац?я",
    Донецьк : ДонНТУ. - 2005.-с.137-144.
  • 55.
    Alba E., Troya J.M.
    A survey of parallel distributed genetic algorithms//Complexity.
    1999.-vol.4.-N4.-pp.31-52.
  • 56.
    E.Alba, M.Tomassini.
    Parallelism and evolutionary algorithms//IEEE Trans. On evolutionary computation.
    2002.-vol.6.-N5.-pp.443-462.
  • 57.
    Engelbrecht A.P.
    Computional intelligence: introduction. John Wiley&Sons Ltd.
    2007.-597p.
  • 58.
    Holland J.H.
    ECHO: Explorations of Evolution in a Miniature World. In J.D. Farmer and J. Doyne, editors, Proceedings of the Second Conference on Artificial Life
    1990
  • 59.
    Fukuda T. and Kubota R.K.
    Learning, adaptation and evolution of intelligent robotic system. In proceedi.H.Holland. ECHO: Explorations of evolution in miniature world. In J.D.Farmer an ngs of the IEEE international symposium on intelligent control, 1988
    pp.2-7,.
  • 60.
    Rosin C.D. and Belew R.K.
    New methods for competitive coevolution. Evolutionary computation, 1997.- 5(1).
    pp.1-29,.
  • 61.
    De Jong K.A. and Potter M.A.
    Evolving complex structures via cooperative coevolution. In proceedings of the fourth annual conference on evolutionary programming, 1995.
    pp.307-317.- Cambridge, MA, MIT press.
  • 62.
    Potter M.A. and de Jong K.A.
    A cooperative coevolutionary approach to function optimization. In T.Davidor, H-P. Schwefel and R.Manner, editors, Proceedings of the paralltl problem solving from nature, 1994
    pp.249-257, Berlin, Springer.
  • 63.
    Potter M.A. and de Jong K.A.
    Evolving neural networks with collaborative species. In proceedings of the summer computer simulation conference, 1995
    pp.340-345.
  • 64.
    Potter M.A. de Jong K.A. and Grefenstette J.J.
    A coevolutionary approach to learning sequential desicision rules. In L.Eshelman, editor, Proceedings of the sixth international conference on genetic algorithms, 1995
    pp.366-372, San Francisco, CA, Morgan Kaufmann.
  • 65.
    Pareto V.
    Manual di Economica Polittica, Societa Editrice Libraia, Milan, Italy,1906; translated into English by A.S.Schwier, as Manual of Political Economy.
    1971.- New York: Macmillan.
  • 66.
    Lin Lin., Mitsuo Gen, Runwei Cheng
    Network models and optimization.
    Springer-Verlag London Limited: 2008.-701p.
  • 67.
    Michalevich Z.
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 68.
    Пилинский М., Рутковская Д., Рутковский Л.
    Нейронные сети, генетические алгоритмы и нечеткие системы.
    М: Горячая линия, 2004.- 452с.
  • 69.
    J. D., Schaffer
    Multiple objective optimization with vector evaluated genetic algorithms, Proceeding 1st International Conference on Gas
    рр. 93-100
  • 70.
    Fleming, Fonseca C., P.
    Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization, Proceeding 5th International Conference on Gas
    1993. -pp. 416-423.
  • 71.
    Ishibuchi H., Murata T.
    A multiobjective genetic local search algorithm and its applications to flowshop scheduling// IEEE Transactions on Systems, Man and Cybernetics,1998.- 28(3),
    p.392-403.
  • 72.
    Cgeng R., Gen M.
    Genetic algorithms and engineering optimization
    2000.- New York; John Wiley&Sons.
  • 73.
    Agarwal S., Deb, K., Meyarivan T., Pratep A.
    A fast and elitist multiobjective genetic algorithm: NSGA-II// IEEE Transactions on Evolutionary Computation, 2002.- 6(2)
    p.182-197.
  • 74.
    Gen M., Lin L.
    Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation, Soft Computing, Vol. 13
    pp. 157-168.
  • 75.
    Friedberg R.M.
    A learning machine: part1// IBM J. research and development.-1958
    Volume 2 Issue 1.-pp. 2-13.
  • 76.
    Koza J.R.
    Genetic Programming.
    Cambridge:MA:MIT Press,1992.
  • 77.
    Ю.А. Скобцов.
    Основы эволюционных вычислений
    Донецк: ДонНТУ, 2008.- 326с.
  • 78.
    Кнут Д.
    Искусство программировния.Т.1
    М.:Мир,1976.
  • 79.
    F.D.Francone., P.Nordin, R.E.Keller, W.Banzhaf
    Genetic programming: introduction.
    1998. Morgan Kaufman, Inc. Sun Francisko, USA.
  • 80.
    Banzhaf W., Brameier M.
    A comparison of linear genetic programming and neural networks in medical data mining// IEEE Transactions on Evolutionary Computation.
    2001.- 5 .- pp. 17 - 26.
  • 81.
    Banzhaf W., Kantschik W.
    Linear-graph GP - a new GP structure// Proceedings of the 4th European Conference on Genetic Programming.
    2002.-Berlin: Springer.-pp. 83-92.
  • 82.
    Скобцов В.Ю., Скобцов Ю.А.
    Модификации генетического программирования//Труды конференций "Интеллектуальные системы" и "Интеллектуальные САПР"
    Москва: Физматлит.- 2004.- с.76-81.
  • 83.
    de Garis H., Iba H.
    Extened genetic programming with recombinative guidance.
    In P.J. Angeline&L.E.Kinnear, Jr.(Eds) Advances in Genetic Programming 2,Cambridge,MA:MIT Press.- pp.69-88.
  • 84.
    Gruau F.
    Neural Network Synthesis using Cellular Encoding and the Genetic Algorithm. PhD thesis, Laboratoire del'Informatique du Parallelisme, Ecole Normale Superieure de Lyon, France.
    1994.-151p.
  • 85.
    Pruainkevicz P.
    Lindermayer A. The Algorithmic Beaty of Plants
    1990.- Springer-Verlag, New York.
  • 86.
    Whigham P.A.
    Grammaticaly-based genetic programming // In Roska J.P.,editor, Proceedings of the workshop on Genetic Programming:From Theory to Real-World Application
    Tahoe City,CA.-1994.-pp.64-75.
  • 87.
    Holland J.P.
    Adaptation in Natural and Artificial Systems. An Introductory Analysis with Application to Biology. Control and Artificial Intelligence.
    University of Michigan.1975.-210 p.
  • 88.
    De Jong K.A., Gordon D.F., Spears W.H.
    Using genetic algorithms for concepts learning// Machine Learning.
    1993.-13.-P.161-188.
  • 89.
    Goldberg D.E.
    Genetic Algorithms in Search, Optimization and Machine Learning.
    Addison-Wesley, reading, MA.-1989.
  • 90.
    Mitchel T.
    Machine Learning
    McGraw-Hill, New York.-1996.
  • 91.
    Michalevich Z.
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 92.
    Wilson S.W.
    Classifier fitness based on accuracy//Evolutionary Computation, №3(2)
    1995.-P.149-175.
  • 93.
    Wilson S.W.
    Generalization in the XCS classifier system// Genetic Programming 1998: Proceedings of the Third Annual Conference
    P.665-674.
  • 94.
    Martin V.Buts.Rule
    Based Evolutionary Online Learning
    Springer-Verlag Berlin Heidelberg.-2006.- 266p.
  • 95.
    Скобцов Ю.А., Хмелевой С.В.
    Генетический подход к задачам прогнозирования// Науков? прац? Донецького нац?онального техн?чного ун?верситету: сер?я"Обчислювальна техн?ка та автоматизац?я".- Випуск 90
    Донецьк : ДонНТУ. - 2005.-с.127-136
  • 96.
    Alistair Sinclair., Ari Juels, Shumeet Baluja
    The Equilibrium Genetic Algorithm and the Role of Crossover.
    1993.
  • 97.
    Shumeet Baluja.
    Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning (Tech. Rep. No. CMU-CS-94-163).
    Pittsburgh, PA: Carnegie Mellon University. - 1994.
  • 98.
    Goldberg D. E., Harik G., Lobo F. G.
    The Compact Genetic Algorithm// IEEE Trans. Evolutionary Computation. - 1999. - vol. 3.
    pp. 287-297.
  • 99.
    Corno F., Sonza Reorda M., Squillero G.
    The Selfish Gene Algorithm: a new Evolutionary Optimization Strategy. Proceedings of the ACM Symposium on Applied Computation.
    1998.
  • 100.
    Дубов Я.В., Скобцов Ю.А.
    Вероятностные и компактные генетические алгоритмы// Искусственный интеллект
    2006.-№2.-с.108-116.
  • 101.
    Скобцов Ю.А.
    Основы эволюционных вычислений.
    Донецк: ДонНТУ, 2008.- 326с.
  • 102.
    Скобцов Ю.А., Федоров Е.Е.
    Метаэвристики.
    Донецк, ДонНТУ:изд-во <Ноулидж>, 2013.-426с.
  • 103.
    Rechenberg I.
    Evolutionstrategie: Optimierung technisher Systems nach Prinzipien der biologischen Evolution.
    Stuttgart:Fromman-Holzboog,1973.
  • 104.
    Michalevich Z.
    Genetic Algorithms + data structures=Evolution Programs.
    Springer.-1999.
  • 105.
    Rechenberg I.
    Evolution strategy. In J.Zurada,R.MarksI, C.Robinson. Computational Intelligence - Imitating Life, Piscatway,NJ: IEEE Press.-1994.
    P.147-159.
  • 106.
    Schwefel H.P.
    Numerical Optimization of Computer Models.
    John Wiley, Chichester, U.K., 1981.
  • 107.
    H-Byung Jun, K-B. Sim., S-W. Lee
    Performance Improvement of Evolution Strategies using Reinforcement Learning. In Proceedings of the IEEE International Fuzzy Systems Conference, volume 2
    P.639-644, 1999.
  • 108.
    Koumoutsakos P.D., Muller S.D., Schraudolph N.N.
    Step Size Adaption in Evolution Strategies using Reinforcement Learning. In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1
    P.151-156, 2002.
  • 109.
    Hansen N., Ostermeier A.
    An Evolution Strategy with Coordinate System Invariant Adaption of Arbitrary Normal Mutation Distributions within The Concept of Mutative Strategy Parameter Control. In Proceedings of the Genetic and Evolutionary Computation Conference
    P.902-909, Sanfrancisco, 1999. Morgan Kaufmann.
  • 110.
    Kursawe F.
    Towards Self-Adapting Evolution Strategies// Proceedings of the Second IEEE Conference on Evolutionary Computation
    1995.-P.283-288.
  • 111.
    Hashem M.A., Izumi K., Watanabe K.
    An Evolution Strategy with Competing Subpopulations// Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, 1997
    P.306-311.
  • 112.
    Fathi M., Hildebrand L., Reusch B.
    Directed Mutation - A New Self-Adaption for Evolution Strategies. In Proceedings of the IEEE Congress on Evolutionary Computationю.-1999
    pp.1550-1557.
  • 113.
    Engelbrecht A.P.
    Computional intelligence: introduction
    John Wiley&Sons Ltd.-2007.-597p.
  • 114.
    Пилинский М., Рутковская Д., Рутковский Л.
    Нейронные сети, генетические алгоритмы и нечеткие системы.
    М: Горячая линия, 2004.- 452с.
  • 115.
    Fogel L.J., Owens A.J., Walsh M.J.
    Artificial Intelligence throw Simulated Evolution.
    1966.- Wiley,New York,
  • 116.
    Chellaila K., Fogel D.B.
    Revisiting Evolutionary Programming
  • 117.
    Fogel D.B.
    Evolutional computation: toward a new philosophy of machine entelligence. New York:IEEE Press.
    1995.
  • 118.
    Beantley P.J.
    Evolutionary Design be Computers.
    San Francisco: Morgan Kaufmann.-1999.
  • 119.
    Engelbrecht A.P.
    Computional intelligence: introduction.
    John Wiley&Sons Ltd.-2007.-597p.
  • 120.
    Fogel D.B.
    System Identification throught Simulated Evolution: A Machine Learning Approach to Modeling.
    Glinn Press,Needham Heights, MA, 1991.
  • 121.
    Chellapilla K.
    Combining Mutation Operators in Evolutionary Programming/ IEEE Transactions on Evolutionary Computation.-1998.-2(3)
    pp.91-96.
  • 122.
    Engelbrat A.P., Messerschmidt L.
    Learning to play games using a PSO-based competitive learning approach//IEEE Transactions on Evolutionary Computations.-8(3).
    2004.-P.280-288.
  • 123.
    Fogel D.B.
    Evolving Artificial Intelligence.
    PhD thesis, University of California, 1992
  • 124.
    Lee C-Y, Yao X.
    Evolutionary programming using mutations based on the Levy probability distribution//IEEE Transactions on Evolutionary Computations.-8(2)
    2004.-P.1-13.
  • 125.
    Chellapilla K.
    Combining mutation operatots in evolutionary programming// IEEE Transactions on Evolutionary Computations.-2(3)
    1998.-P.91-96.
  • 126.
    Wong K.P., Yurievich J.
    Evolutionary-Programming-Based Algorithms from Environmentally-Constrained Economic Dispatch// IEEE Transactions on Power Systems.-13(2).
    1997.-P.301-306.
  • 127.
    Atmar J.W., D.B., Fogel, Fogel L.J.
    Meta-Evolutionary Programming.In Proceedings of the Twenty-Fifth Conference on Signals,Systems and Computers.- 1991.-volume 1
    pp.540-545.
  • 128.
    T.Back and H.-P.Schwefel.
    An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation,1(1)
    P.1-23, 1993
  • 129.
    Angeline P.J., Pollack J.P., Saunders G.M.
    An Evolutionary That Constructs Recurrent Neural Networkd.//IEEE Transactions on Neural Networks, 5(1).
    1994.-pp.54-65.
  • 130.
    Lai L.L., Ma J.T.
    Determination of Operational Parameters of Electrical Machines using Evolutionary Programming// Proceedings of the Seventh International Conference on Electrical Machines and Drives.-1995
    pp.116-120.
  • 131.
    Wong K.P., Yurievich J.
    Evolutionary programming based optimal power flow algorithm//IEEE Transactions on Power Systems.-14(4)
    1999.-pp.1245-1250.
  • 132.
    Morris A.S., Swain A.K.
    A Novel Hybrid Evolutionary Programming Method for Function Optimization// Proceedings of the IEEE Congress on Evolutionary Computation.-2000.- volume 1
    pp.699-705.
  • 133.
    Gao W.
    Fast Immunized Evolutionary Programming. In Proceedings of the IEEE Congress on Evolutionary Computation.-2004.-volume 1
    pp.666-670.
  • 134.
    Atmar J.W., Fogel, Fogel D.B., L.J.
    Meta-Evolutionary Programming// Proceedings of the Twenty-Fifth Conference on Signals, Systems and Computers.- 1991.- volume 1
    pp. 540-545.
  • 135.
    Angeline P.J., Fogel D.B., Fogel L.J.
    An Evolutionary Programming Approach to Self-Adaptation on Finite State Machines. In J. McDonnell, R. Reynolds, and D.B. Fogel,editors// Proceedings of the Fourth Annual Conference on Evolutionary Programming.
    1995.- pp. 355-365. MIT Press.
  • 136.
    Matsumara Y., Ohkura K., Ueda K.
    Evolutionary Programming with Noncoding Segments for Realvalued Function Optimization// Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.-1999.-volume 4
    pp.242-247.
  • 137.
    Fogel D.B., Fogel G.B.
    Multiple-vector self adaptation in evolutionary algorithms//BioSystems-2001.-61(2-3)
    pp.155-162.
  • 138.
    Eberhart.C., KennedyJ.
    Particleswarmintelligence//ProceedingsoftheIEEEInternationalJoint Conference on Neural Networks.-1995
    pp. 1942-1948.
  • 139.
    Engelbrecht A.P.
    Computional intelligence: introduction.
    JohnWiley&Sons Ltd.-2007.-597p.
  • 140.
    Deepa S.N., Sivanandam S.N.
    Introduction to genetic algorithms.
    Springer-Verlag Berlin Heidelberg.-2008.-p.442.
  • 141.
    Скобцов Ю.А., Федоров Е.Е.
    Метаэвристики.
    Донецк,ДонНТУ:изд-во <Ноулидж>.- 2013.-426с.
  • 142.
    Dorigo M.
    Optimization, learning and natural algoriothms.PhD. thesis.
    Politechnico di Milano, 1992.
  • 143.
    Deepa S.N., Sivanandam S.N.
    Introduction to genetic algorithms.
    Springer-Verlag Berlin Heidelberg.-2008..-442p.
  • 144.
    Engelbrecht A.P.
    Computional intelligence: introduction.
    JohnWiley&Sons Ltd.-2007.-597p.
  • 145.
    CaroG.Di., Dorigo M.
    The ant colony optimization meta-heuristic.InD.Corne, M.Dorigo, F.Glover, editors.// New ideas in optimization.
    McGraw-Hill.-P.11-32.
  • 146.
    Colorni A., Maniezzo V.
    The ant system applied to the quadratic assignment problem//IEEE transactions on knowledge and data engineering, № 11(5)
    1999.-P.769-778.
  • 147.
    ColorniA., DorigoM., Maniezzo V.
    Ant system: optimization by a colony of cooperative agents//IEEE transactions on systems, man and cybernetics-part B,№ 26(1).
    1996.-P.29-41.
  • 148.
    Dorigo M., Gambardella M.
    Ant colony systems: a cooperative leaning approach to the traveling salesman problem//IEEE transactions on evolutionary computation.-№ 1(1).
    1997.-P.53-66.
  • 149.
    Hoos H., T.StutzleT.
    MAX-MIN ant system and local search for the traveling salesman problem// Proceedings of the IEEE international conference on evolutionary computation.-1997
    P.309-314.
  • 150.
    Dorigo M., Gambardella L.M.
    Ant-Q: a reinforcement learning approach to TSP// Proceedings of twelfth international conference on machine learning.-1995.
    P.252-260.
  • 151.
    Taillard E.D.
    FANT: fast ant system. Technical report IDSIA 46-98
    IDSIA, Lugano, Switzeland, 1998.
  • 152.
    Bullnheimer B., Kotsis G., StrauseC.
    Parallelization strategies for the ant systems//In G.Toraldo, A.Murli,P.Pardalos, editor.Kluwer. Series on applied optimization
    1997.-P.87-100.
  • 153.
    Carbonaro A., Maniezzo V.
    An ANTS heuristic for the frequency assignment problem//Future generation computer systems, №16(9)
    2000.-P.927-935.
  • 154.
    Guntsch M., Middlendorf M.
    Applying population base ACO to dynamic optimization problems// Proceedings of third international workshop on ant colony optimization and swarm intelligence.-2003
    P.111-122.
  • 155.
    Gong S., Li Y.
    Dynamic ant colony optimization for TSP//International Journal of advanced manufacturing technology.№ 22(7-8)
    2003.-P.528-533
  • 156.
    Guntsch M., Middendorf M.
    Pheromone modification strategies for ant algorithms applied to dynamic TSP// Proceedings of the workshop on applications of evolutionary computing.-2001
    P.213-222.
  • 157.
    Fonlupt C., Roux O., Talbi E-G.
    ANTabu. Technical report LIL-98-04
    Laboratoired'informatique du Littoral, Universite du Littral, Calais, Franse.-1998.

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