Books on the topic 'Genetic algorithms – Statistical methods'

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1

Mitchell, Melanie. An introduction to genetic algorithms. Cambridge, Mass: MIT Press, 1996.

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2

Chen, Ming-Hui, Lynn Kuo, and Paul O. Lewis. Bayesian phylogenetics: Methods, algorithms, and applications. Boca Raton: CRC Press/Taylor & Francis, 2014.

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3

Lange, Kenneth. Mathematical and statistical methods for genetic analysis. 2nd ed. New York: Springer, 2002.

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4

Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5.

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5

Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5.

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6

Lange, Kenneth. Mathematical and statistical methods for genetic analysis. New York: Springer, 1997.

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7

Rattray, Lars Magnus. Modelling the dynamics of genetic algorithms using statistical mechanics. Manchester: University of Manchester, 1997.

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8

Weir, B. S. Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

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9

Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

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10

Barone, Piero, Arnoldo Frigessi, and Mauro Piccioni, eds. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9.

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11

Broman, Karl W. A Guide to QTL Mapping with R/qtl. New York, NY: Springer-Verlag New York, 2009.

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12

Gianola, Daniel, and Keith Hammond, eds. Advances in Statistical Methods for Genetic Improvement of Livestock. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74487-7.

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13

Masoudi-Nejad, Ali. Gene prioritization: Rationale, methodologies and algorithms. Cham: Springer, 2014.

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14

Carter, Jason W. Testing effectiveness of genetic algorithms for exploratory data analysis. Monterey, Calif: Naval Postgraduate School, 1997.

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15

Koopman, S. J. Statistical algorithms for models in state space form: SsfPack 3.0. London: Timberlake Consultants, 2008.

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16

Koopman, S. J. Statistical algorithms for models in state space form: SsfPack 3.0. London: Timberlake Consultants, 2008.

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17

Rekiek, Brahim. Assembly line design: The balancing of mixed-model hybrid assembly lines with genetic algorithms. London: Springer, 2006.

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18

Lo, Andrew W. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Cambridge, MA: National Bureau of Economic Research, 2000.

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19

Lantuej́oul, Christian. Geostatistical simulation: Models and algorithms. New York: Springer, 2002.

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20

Diniz, Paulo Sergio Ramirez. Adaptive filtering: Algorithms and practical implementation. 2nd ed. Boston: Kluwer Academic Publishers, 2002.

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21

Diniz, Paulo Sergio Ramirez. Adaptive filtering: Algorithms and practical implementation. Boston: Kluwer Academic Publishers, 1997.

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22

Lantuéjoul, Christian. Geostatistical simulation: Models and algorithms. Berlin: Springer, 2002.

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23

ACM Workshop on Computational Learning Theory (5th 1992 Pittsburgh, Pennsylvania). Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, July 27-29, 1992, Pittsburgh, Pennsylvania. New York, N.Y: Association for Computing Machinery, 1992.

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24

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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25

Burgess, Stephen. Mendelian randomization: Methods for using genetic variants in causal estimation. Boca Raton: CRC Press, Taylor & Francis Group, 2015.

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26

International Workshop on Randomization and Approximation Techniques in Computer Science (3rd 1999 Berkeley, Calif.). Randomization, approximation, and combinatorial optimization: Algorithms and techniques : Third International Workshop on Randomization and Approximation Techniques in Computer Science, and Second International Workshop on Approximation Algorithms for Combinatorial Optimization Problems RANDOM-APPROX '99, Berkeley, CA, August 8-11, 1999, proceedings. Berlin: Springer, 1999.

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27

Cannings, C., M. J. Bishop, and D. J. Balding. Handbook of statistical genetics. Edited by Balding D. J, Bishop M, and Cannings C. 1942-. 2nd ed. Chichester, West Sussex, England: John Wiley & Sons, 2003.

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28

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (4th 2001 Berkeley, Calif.). Approximation, randomization, and combinatorial optimization: Algorithms and techniques : 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2001 and 5th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2001, Berkeley, CA, USA, August 18-20, 2001, proceedings. New York: Springer, 2001.

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29

Cameron, N. D. Selection indices and prediction of genetic merit in animal breeding. Oxford [England]: CAB International, 1997.

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30

Ziegler, Andreas. A statistical approach to genetic epidemiology: Concepts and applications. Weinheim, GE: Wiley-VCH, 2007.

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31

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (6th 2003 Princeton, N.J.). Approximation, randomization, and combinatorial optimization: Algorithms and techniques : 6th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2003, and 7th International Workshop on Randomization and Approximation Techniques in Computer Science, RANDOM 2003, Princeton, NJ, USA, August 24-26, 2003 : proceedings. Berlin: Springer, 2003.

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32

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (8th 2005 Berkeley, Calif.). Approximation, randomization, and combinatorial optimization: Algorithms and techniques : 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005, and 9th International Workshop on Randomization and Computation, RANDOM 2005, Berkeley, CA, USA, August 22-24, 2005 : proceedings. Berlin: Springer, 2005.

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33

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (10th 2007 Princeton, N.J.). Approximation, randomization, and combinatorial optimization: Algorithms and techniques ; 10th international workshop, APPROX 2007, and 11th international workshop, RANDOM 2007, Princeton, NJ, USA, August 20-22, 2007 ; proceedings. Berlin: Springer, 2007.

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34

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (7th 2004 Cambridge, Mass.). Approximation, randomization, and combinatorial optimization : algorithms and techniques: 7th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2004, and 8th International Workshop on Randomization and Computation, RANDOM 2004, Cambridge, MA, USA, August 22-24, 2004 : proceedings. Berlin: Springer, 2004.

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35

International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (9th 2006 Barcelona, Spain). Approximation, randomization and combinatorial optimization: Algorithms and techniques : 9th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2006, and 10th International Workshop on Randomization and Computation, RANDOM 2006, Barcelona, Spain, August 28-30, 2006 : proceedings. Berlin: Springer, 2006.

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36

Cannings, C., M. J. Bishop, and D. J. Balding. Handbook of statistical genetics. Chichester: Wiley, 2001.

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37

National Research Council (U.S.). Committee on Validation of Toxicogenomic Technologies: A Focus on Chemical Classification Strategies. Validation of toxicogenomic technologies: A workshop summary. Washington, D.C: National Academies Press, 2007.

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38

Nikiforov, V. S. Ot͡s︡enka sily st͡s︡eplenii͡a︡ genov maloplodnykh selʹskokhozi͡a︡ĭstvennykh zhivotnykh na osnove populi͡a︡t͡s︡ionno-genealogicheskikh dannykh. Syktyvkar: Akademii͡a︡ nauk SSSR, Komi filial, In-t biologii, 1987.

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39

National Research Council (U.S.). Committee on Validation of Toxicogenomic Technologies: A Focus on Chemical Classification Strategies. Validation of toxicogenomic technologies: A workshop summary. Washington, D.C: National Academies Press, 2007.

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40

Wu, Rongling. Statistical and computational pharmacogenomics. Boca Raton: Chapman & Hall/CRC, 2008.

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41

Smith, Stephen L. Genetic and evolutionary computation: Medical applications. Chichester, U.K: Wiley, 2010.

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42

Campbell, Robert Kenneth. Genetic variation and seed zones of douglas-fir in the Siskiyou National Forest. Portland, Or. (333 S.W. First Ave., P.O. Box 3890, Portland 97208): U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1993.

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43

Kundu, Debasis. Statistical Signal Processing: Frequency Estimation. India: Springer India, 2012.

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44

Lavine, Barry K., and Barry K. Levine. Pattern Recognition Analysis Via Genetic Algorithms & Multivariate Statistical Methods. CRC, 2001.

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45

Thomas, Duncan C. Statistical Methods in Genetic Epidemiology. Oxford University Press, USA, 2004.

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46

Wang, Jason T. L., Bruce A. Shapiro, and Dennis Shasha, eds. Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.001.0001.

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Abstract:
Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
47

P, Dhawan Atam, and United States. National Aeronautics and Space Administration., eds. Genetic algorithms as global random search methods. [Washington, DC: National Aeronautics and Space Administration, 1995.

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48

SPSS 7.5, statistical algorithms. Chicago, IL: SPSS, Inc., 1997.

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49

Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. Springer, 2012.

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50

Barban, Nicola, Felix C. Tropf, and Melinda C. Mills. Introduction to Statistical Genetic Data Analysis. MIT Press, 2020.

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