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Books on the topic 'System of genetic algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Abu-alola, Abdulmohsin. Genetic algorithms for intelligent control system design. Wolverhampton: University of Wolverhampton, 1995.

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2

Bloch, Tobias A. Storage and pumping system optimization using genetic algorithms. Ottawa: National Library of Canada, 1999.

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3

Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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4

Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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5

Kramer, Oliver. Genetic Algorithm Essentials. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52156-5.

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6

Ajlouni, Naim. Genetic algorithms for control systems design. Salford: University of Salford, 1995.

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7

Fleming, P. J. Genetic algorithms in control systems engineering. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1993.

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8

Islam, Rafiqul. Genetic algorithms, fuzzy systems, and website classification. Hauppauge, N.Y: Nova Science Publishers, 2011.

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9

Tao, Jili, Ridong Zhang, and Yong Zhu. DNA Computing Based Genetic Algorithm. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2.

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10

Sara, Silva, Krawiec Krzysztof, Machado Penousal, Cotta Carlos, and SpringerLink (Online service), eds. Genetic Programming: 15th European Conference, EuroGP 2012, Málaga, Spain, April 11-13, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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11

Genetic algorithms and fuzzy multiobjective optimization. Boston: Kluwer Academic Publishers, 2002.

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12

Mayer, David G. Evolutionary algorithms and agricultural systems. Boston: Kluwer Academic Publishers, 2002.

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13

Adeli, Hojjat. Machine learning: Neural networks, genetic algorithms, and fuzzy systems. New York: Wiley, 1995.

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14

Genetic algorithms and robotics: A heuristic strategy for optimization. Singapore: World Scientific, 1991.

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15

Mayer, David G. Evolutionary Algorithms and Agricultural Systems. Boston, MA: Springer US, 2002.

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16

French, Alan Paul. A genetic algorithm for the satisfiability problem. Loughborough, Leics: Loughborough University Business School, 1995.

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17

K, Kokula Krishna Hari, ed. Energy Efficient Node Placement Using Genetic Algorithm. Pondicherry, India: Association of Scientists, Developers and Faculties, 2014.

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18

The simple genetic algorithm: Foundations and theory. Cambridge, Mass: MIT Press, 1999.

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19

Ruan, Da. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms. Boston, MA: Springer US, 1997.

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20

Pawar, Prashant M. Structural health monitoring using genetic fuzzy systems. London: Springer, 2011.

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21

Gillam, Peter Terrence. The use of genetic algorithms in discovering profitable stock trading systems. Birmingham: University of Birmingham, 1998.

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22

Gen, Mitsuo. Network models and optimization: Multiobjective genetic algorithm approach. London: Springer, 2008.

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23

Karr, C. L. Genetic algorithm applied to least squares curve fitting. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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24

Graves, Paul J. J. A genetic algorithm for routing printed circuit boards. Manchester: University of Manchester, Department of Computer Science, 1995.

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25

Timmerman, Michael Jay. A genetic algorithm based anti-submarine warfare simulator. Monterey, Calif: Naval Postgraduate School, 1993.

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26

Kothari, Ravi. A competitive genetic algorithm for single row facility layout. Ahmedabad: Indian Institute of Management, 2012.

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27

Choy, Tommy King Lun. On the optimisation of manufacturing systems through fuzzied genetic algorithms based simulation. [S.l.]: typescript, 1998.

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28

Suman, Vitisha. A genetic algorithm based method for neutron spectrum unfolding. Mumbai: Scientific Information Resource Division, Bhabha Atomic Research Centre, 2013.

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29

McClintock, Shaunna. Soft computing: A fuzzy logic controlled genetic algorithm environment. [S.l: The Author], 1999.

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30

Moerder, Daniel D. Constrained minimization of smooth functions using a genetic algorithm. Hampton: National Aeronautics and Space Administration, Langley Research Center, 1994.

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31

J, Grefenstette John, American Association for Artificial Intelligence., Naval Research Laboratory (U.S.), and Bolt, Beranek, and Newman, inc., eds. Genetic algorithms and their applications: Proceedings of the second International Conference on Genetic Algorithms : July 28-31, 1987 at the Massachusetts Institute of Technology, Cambridge, MA. Hillsdale, N.J: L. Erlbaum Associates, 1987.

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32

Rogers, David. G/SPINES: A hybird of friedman's multivariate adaptive regression splines (MARS) algorithm with Holland's genetic algorithm. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1991.

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33

Ballard, John Michael. Generic computational algorithms for extracting 3D machinability data from a wireframe CAD system. [Derby: University of Derby], 1999.

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34

Rattray, Magnus. An analysis of a genetic algorithm training the binary perception. Manchester: University of Manchester, 1994.

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35

Thomas, Mark W. A Pareto frontier for full stern submarines via genetic algorithm. Monterey, Calif: Naval Postgraduate School, 1998.

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36

Vasiljević, Darko. Classical and evolutionary algorithms in the optimization of optical systems. Boston: Kluwer Academic Publishers, 2002.

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37

Tyukavin, Aleksandr. Fundamentals of pathology. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1242551.

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The textbook reveals the concepts of health and disease, presents modern ideas about the causes and conditions of the occurrence of actual types of pathology. The importance of molecular genetic factors of heredity and reactivity in the formation of typical pathological processes is shown. The main regularities and features of manifestations of organ and system dysfunction in various types of pathology are described. Special attention is paid to the causes and mechanisms of development of socially significant diseases of the heart and blood vessels, brain, respiratory organs and other vital systems of the body. A separate section of the textbook provides up-to-date information on first aid. The criteria of safe conditions for first aid are described; the main clinical manifestations of emergency conditions in accidents, injuries, poisoning and diseases are described. A list of first aid measures for life-threatening conditions is presented. The cloud service contains a video of the basic algorithm for conducting cardiopulmonary resuscitation in a pharmacy or office. It is written in accordance with the program of the academic discipline "Fundamentals of Pathology" in the specialty 33.02.01 "Pharmacy" and refers to the educational and methodological publications of the cycle of general professional disciplines for students of pharmaceutical technical schools.
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38

Alpay, Daniel, and Victor Vinnikov, eds. System Theory, the Schur Algorithm and Multidimensional Analysis. Basel: Birkhäuser Basel, 2007. http://dx.doi.org/10.1007/978-3-7643-8137-0.

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39

Shao, Yingxia, Bin Cui, and Lei Chen. Large-scale Graph Analysis: System, Algorithm and Optimization. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3928-2.

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40

Koukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. Manchester: UMIST, 1996.

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41

Elisha, Karl Justin Edward. A K-seed genetic clustering algorithm with applications to cellular manufacturing. Birmingham: University of Birmingham, 1997.

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42

OmeGA: A competent genetic algorithm for solving permutation and scheduling problems. Boston: Kluwer Academic Publishers, 2002.

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43

Knjazew, Dimitri. OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems. Boston, MA: Springer US, 2002.

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44

Jonathan, Raper, ed. Spatial evolutionary modeling. New York: Oxford University Press, 2000.

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45

Bierwirth, Christian. Adaptive search and the management of logistics systems: Base models for learning agents. Boston: Kluwer Academic, 2000.

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46

Modeling Bluetooth Radio Technology Simulation Using Multi-Agent Based System and Genetic Algorithm Design Paradigm. Storming Media, 2001.

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47

Ramli, Razamin, Haslinda Ibrahim, and Tze Shung Lim. Solving a bus driver scheduling problem: A genetic algorithm approach. UUM Press, 2013. http://dx.doi.org/10.32890/9789670474311.

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Many transport companies face problems in regulating their transport services due to various challenges and issues. These problems affect the quality of the services provided especially in a university campus environment, where students heavily depend on the university transport services for their daily commuting.What are the problems faced by the management of the campus transport company? What are the issues raised by the drivers operating the on-campus buses?Hence, in assisting the management of the transport company the authors have identified the inefficiency of their bus driver scheduling system as one of the main problems, which needed to be tackled.For that reason, the authors developed an efficient bus driver scheduling model based on the Genetic Algorithm (GA) approach.The GA model is able to provide some resolutions and insight in relation to these inquiries: What are the constraints being considered in this bus driver scheduling problem?How were the drivers break times being distributed in this GA approach?How was the time taken to generate an efficient schedule?
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48

Zaspel, Joachim C. Automating pilot function performance assesssment using fuzzy systems and a genetic algorithm. 1997.

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49

J, Gentry E., and Stanley D. A, eds. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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50

Rolf, Drechsler, and Drechsler Nicole 1970-, eds. Evolutionary algorithms for embedded system design. Boston: Kluwer Academic Publishers, 2003.

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