Auswahl der wissenschaftlichen Literatur zum Thema „Genetic algorithms“
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Zeitschriftenartikel zum Thema "Genetic algorithms":
Sumida, Brian. „Genetics for genetic algorithms“. ACM SIGBIO Newsletter 12, Nr. 2 (Juni 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.
Raol, Jitendra R., und Abhijit Jalisatgi. „From genetics to genetic algorithms“. Resonance 1, Nr. 8 (August 1996): 43–54. http://dx.doi.org/10.1007/bf02837022.
Babu, M. Nishidhar, Y. Kiran und A. Ramesh V. Rajendra. „Tackling Real-Coded Genetic Algorithms“. International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (31.12.2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.
Abbas, Basim K. „Genetic Algorithms for Quadratic Equations“. Aug-Sept 2023, Nr. 35 (26.08.2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.
Nico, Nico, Novrido Charibaldi und Yuli Fauziah. „Comparison of Memetic Algorithm and Genetic Algorithm on Nurse Picket Scheduling at Public Health Center“. International Journal of Artificial Intelligence & Robotics (IJAIR) 4, Nr. 1 (30.05.2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.
Carnahan, J., und R. Sinha. „Nature's algorithms [genetic algorithms]“. IEEE Potentials 20, Nr. 2 (2001): 21–24. http://dx.doi.org/10.1109/45.954644.
Frenzel, J. F. „Genetic algorithms“. IEEE Potentials 12, Nr. 3 (Oktober 1993): 21–24. http://dx.doi.org/10.1109/45.282292.
Fulkerson, William F. „Genetic Algorithms“. Journal of the American Statistical Association 97, Nr. 457 (März 2002): 366. http://dx.doi.org/10.1198/jasa.2002.s468.
Forrest, Stephanie. „Genetic algorithms“. ACM Computing Surveys 28, Nr. 1 (März 1996): 77–80. http://dx.doi.org/10.1145/234313.234350.
Holland, John H. „Genetic Algorithms“. Scientific American 267, Nr. 1 (Juli 1992): 66–72. http://dx.doi.org/10.1038/scientificamerican0792-66.
Dissertationen zum Thema "Genetic algorithms":
Bland, Ian Michael. „Generic systolic arrays for genetic algorithms“. Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312529.
Aguiar, Marilton Sanchotene de. „Análise formal da complexidade de algoritmos genéticos“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1998. http://hdl.handle.net/10183/25941.
The objective of the work is to study the viability of treating optimization problems, considered intractable, through Genetic Algorithms, developing approaches for the qualitative evaluation of a Genetic Algorithm. Inside this theme, approached areas: complexity, classes of problems, analysis and development of algorithms and Genetic Algorithms, this last one being central object of the study. As product of the study of this theme, a development method of Genetic Algorithms is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The fact that a problem theoretically solvable isn’t enough to mean that it is solvable in pratice. A problem is denominated easy if in the worst case it possesses an algorithm reasonably efficient. And an algorithm is said reasonably efficient when a polynomial p exists such that for any entrance size n the algorithm terminates at maximum of p(n) steps [SZW 84]. Since a polynomial can be of very high order, then an algorithm of polynomial complexity can be very inefficient. The premise of the Genetic Algorithms is that one can find approximate solutions of problems of great computational complexity by means of a process of simulated evolution [LAG 96]. As product of the study of this theme, a method of development of Genetic Algorithms with the quality conscience is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The axiom set has the purpose of giving the semantics of the algorithm, in other words, it defines formally the operation of the algorithm, more specifically of the functions and procedures of the algorithm. And this, facilitates the planner of algorithms a larger safety in the development, because in order to prove the correction of a Genetic Algorithm that satisfies that model it is only necessary to prove that the procedures satisfy the axioms. To have conscience of the quality of an approximate algorithm, two factors are important: the accuracy and the complexity. This work lifts the important points for the study of the complexity of a Genetic Algorithm. Unhappily, they are conflicting factors, because as larger the accuracy, worse (higher) it is the complexity, and vice-versa. Thus, a study of the quality of a Genetic Algorithm, considered an approximate algorithm, would be only complete with the consideration of these two factors. But, this work provides a great step in direction of the study of the viability of the treatment of optimization problems through Genetic Algorithms.
Abu-Bakar, Nordin. „Adaptive genetic algorithms“. Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343268.
Hayes, Christina Savannah Maria. „Generic properties of the infinite population genetic algorithm“. Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/hayes/HayesC0806.pdf.
Wagner, Stefan. „Looking inside genetic algorithms /“. Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00116856.pdf.
Cole, Rowena Marie. „Clustering with genetic algorithms“. University of Western Australia. Dept. of Computer Science, 1998. http://theses.library.uwa.edu.au/adt-WU2003.0008.
Lapthorn, Barry Thomas. „Helioseismology and genetic algorithms“. Thesis, Queen Mary, University of London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271261.
Delman, Bethany. „Genetic algorithms in cryptography /“. Link to online version, 2003. https://ritdml.rit.edu/dspace/handle/1850/263.
Krüger, Franz David, und Mohamad Nabeel. „Hyperparameter Tuning Using Genetic Algorithms : A study of genetic algorithms impact and performance for optimization of ML algorithms“. Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-42404.
As machine learning (ML) is being more and more frequent in the business world, information gathering through Data mining (DM) is on the rise, and DM-practitioners are generally using several thumb rules to avoid having to spend a decent amount of time to tune the hyperparameters (parameters that control the learning process) of an ML algorithm to gain a high accuracy score. The proposal in this report is to conduct an approach that systematically optimizes the ML algorithms using genetic algorithms (GA) and to evaluate if and how the model should be constructed to find global solutions for a specific data set. By implementing a GA approach on two ML-algorithms, K-nearest neighbors, and Random Forest, on two numerical data sets, Iris data set and Wisconsin breast cancer data set, the model is evaluated by its accuracy scores as well as the computational time which then is compared towards a search method, specifically exhaustive search. The results have shown that it is assumed that GA works well in finding great accuracy scores in a reasonable amount of time. There are some limitations as the parameter’s significance towards an ML algorithm may vary.
Yan, Ping. „Theory of simple genetic algorithms“. Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446649.
Bücher zum Thema "Genetic algorithms":
Man, K. F., K. S. Tang und S. Kwong. Genetic Algorithms. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0577-0.
Anup, Kumar, und Gupta Yash P, Hrsg. Genetic algorithms. Oxford: Pergamon, 1995.
1942-, Buckles Bill P., und Petry Fred, Hrsg. Genetic algorithms. Los Alamitos, Calif: IEEE Computer Society Press, 1986.
Luque, Gabriel, und Enrique Alba. Parallel Genetic Algorithms. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22084-5.
Mutingi, Michael, und Charles Mbohwa. Grouping Genetic Algorithms. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44394-2.
Haupt, Randy L. Practical genetic algorithms. 2. Aufl. Hoboken, N.J: John Wiley, 2004.
Haupt, Randy L. Practical genetic algorithms. New York: Wiley, 1998.
Dr, Herrera Francisco, und Verdegay José-Luis, Hrsg. Genetic algorithms and soft computing. Heidelberg: Physica-Verlag, 1996.
Lance, Chambers, Hrsg. The practical handbook of genetic algorithms: Applications. 2. Aufl. Boca Raton, Fla: Chapman & Hall/CRC, 2001.
Lance, Chambers, Hrsg. Practical handbook of genetic algorithms. Boca Raton: CRC Press, 1995.
Buchteile zum Thema "Genetic algorithms":
Hardy, Yorick, und Willi-Hans Steeb. „Genetic Algorithms“. In Classical and Quantum Computing, 313–400. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8366-5_15.
Du, Ke-Lin, und M. N. S. Swamy. „Genetic Algorithms“. In Search and Optimization by Metaheuristics, 37–69. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_3.
Sastry, Kumara, David E. Goldberg und Graham Kendall. „Genetic Algorithms“. In Search Methodologies, 93–117. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_4.
Rathore, Heena. „Genetic Algorithms“. In Mapping Biological Systems to Network Systems, 97–106. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29782-8_8.
Ansari, Nirwan, und Edwin Hou. „Genetic Algorithms“. In Computational Intelligence for Optimization, 83–97. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_6.
Rowe, Jonathan E. „Genetic Algorithms“. In Springer Handbook of Computational Intelligence, 825–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_42.
Reeves, Colin R. „Genetic Algorithms“. In Handbook of Metaheuristics, 109–39. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1665-5_5.
Dracopoulos, Dimitris C. „Genetic Algorithms“. In Perspectives in Neural Computing, 111–31. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0903-7_7.
Kingdon, Jason. „Genetic Algorithms“. In Perspectives in Neural Computing, 55–80. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_4.
Dawid, Herbert. „Genetic Algorithms“. In Lecture Notes in Economics and Mathematical Systems, 37–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-00211-7_3.
Konferenzberichte zum Thema "Genetic algorithms":
Skomorokhov, Alexander O. „Genetic algorithms“. In the conference. New York, New York, USA: ACM Press, 1996. http://dx.doi.org/10.1145/253341.253399.
Alfonseca, Manuel. „Genetic algorithms“. In the international conference. New York, New York, USA: ACM Press, 1991. http://dx.doi.org/10.1145/114054.114056.
Butt, Fouad, und Abdolreza Abhari. „Genetic algorithms“. In the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878779.
Pandey, Hari Mohan, Anurag Dixit und Deepti Mehrotra. „Genetic algorithms“. In the CUBE International Information Technology Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381766.
Chapman, Colin D., Kazuhiro Saitou und Mark J. Jakiela. „Genetic Algorithms As an Approach to Configuration and Topology Design“. In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0338.
Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel und Luís Paquete. „Algorithm selection of anytime algorithms“. In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377930.3390185.
Carlson, Susan E., Michael Ingrim und Ronald Shonkwiler. „Component Selection Using Genetic Algorithms“. In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0336.
Ladkany, George S., und Mohamed B. Trabia. „Incorporating Twinkling in Genetic Algorithms for Global Optimization“. In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49256.
Misir, Mustafa, Stephanus Daniel Handoko und Hoong Chuin Lau. „Building algorithm portfolios for memetic algorithms“. In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2598455.
Alba, Enrique. „Cellular genetic algorithms“. In GECCO '14: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2598394.2605356.
Berichte der Organisationen zum Thema "Genetic algorithms":
Arthur, Jennifer Ann. Genetic Algorithms. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1375151.
Sharp, David H., John Reinitz und Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Fort Belvoir, VA: Defense Technical Information Center, Januar 1991. http://dx.doi.org/10.21236/ada256223.
Kargupta, H. Messy genetic algorithms: Recent developments. Office of Scientific and Technical Information (OSTI), September 1996. http://dx.doi.org/10.2172/378868.
Messa, K., und M. Lybanon. Curve Fitting Using Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, Oktober 1991. http://dx.doi.org/10.21236/ada247206.
Thomas, E. V. Frequency selection using genetic algorithms. Office of Scientific and Technical Information (OSTI), Mai 1993. http://dx.doi.org/10.2172/10177075.
Vlek, R. J., und D. J. M. Willems. Recipe reconstruction with genetic algorithms. Wageningen: Wageningen Food & Biobased Research, 2021. http://dx.doi.org/10.18174/540621.
Cobb, Helen G., und John J. Grefenstette. Genetic Algorithms for Tracking Changing Environments. Fort Belvoir, VA: Defense Technical Information Center, Januar 1993. http://dx.doi.org/10.21236/ada294075.
Pittman, Jennifer, und C. A. Murthy. Optimal Line Fitting Using Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, Juli 1997. http://dx.doi.org/10.21236/ada328266.
Goldberg, David. Competent Probabilistic Model Building Genetic Algorithms. Fort Belvoir, VA: Defense Technical Information Center, Juli 2003. http://dx.doi.org/10.21236/ada416564.
Vemuri, V. R. Genetic algorithms at UC Davis/LLNL. Office of Scientific and Technical Information (OSTI), Dezember 1993. http://dx.doi.org/10.2172/10122640.