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Bibliografias temáticas / Genetic algorithms

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Literatura científica selecionada sobre o tema "Genetic algorithms"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 25 de julho de 2025

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Artigos de revistas sobre o assunto "Genetic algorithms"

1

Sumida, Brian. "Genetics for genetic algorithms." ACM SIGBIO Newsletter 12, no. 2 (1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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Raol, Jitendra R., and Abhijit Jalisatgi. "From genetics to genetic algorithms." Resonance 1, no. 8 (1996): 43–54. http://dx.doi.org/10.1007/bf02837022.

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Nico, Nico, Novrido Charibaldi, and 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, no. 1 (2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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 One of the most significant aspects of the working world is the concept of a picket schedule. It is difficult for the scheduler to make an archive since there are frequently many issues with the picket schedule. These issues include schedule clashes, requests for leave, and trading schedules. Evolutionary algorithms have been successful in solving a wide variety of scheduling issues. Evolutionary algorithms are very susceptible to data convergence. But no one has discussed where to start from, where the data converges from making schedules using evolutionary algorithms. The best algorit

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4

Babu, M. Nishidhar, Y. Kiran, and A. Ramesh V. Rajendra. "Tackling Real-Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development Volume-2, Issue-1 (2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.

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Srinath Murthy, Ahana, and Dattatreya P Mankame. "Genetic Algorithms - A Brief Study." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1195–200. http://dx.doi.org/10.21275/sr24721004409.

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Abbas, Basim K. "Genetic Algorithms for Quadratic Equations." Aug-Sept 2023, no. 35 (August 26, 2023): 36–42. http://dx.doi.org/10.55529/jecnam.35.36.42.

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A common technique for finding accurate solutions to quadratic equations is to employ genetic algorithms. The authors propose using a genetic algorithm to find the complex roots of a quadratic problem. The technique begins by generating a collection of viable solutions, then proceeds to assess the suitability of each solution, choose parents for the next generation, and apply crossover and mutation to the offspring. For a predetermined number of generations, the process is repeated. Comparing the evolutionary algorithm's output to the quadratic formula proves its validity and uniqueness. Furth

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Rizky Fatih Syahputra and Yahfizham Yahfizham. "Menganalisis Konsep Dasar Algoritma Genetika." Bhinneka: Jurnal Bintang Pendidikan dan Bahasa 2, no. 1 (2023): 120–32. http://dx.doi.org/10.59024/bhinneka.v2i1.643.

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Genetic algorithms are computer techniques inspired by the theory of evolution and genetics. Individual definition, chromosome initialization, chromosome testing, selection (crossover) and mutation are fundamental elements of genetic algorithms. Genetic algorithms are used to solve optimization problems, such as lesson planning, community services and traffic light adjustment. By producing the best combination of chromosomes, the genetic algorithm can achieve ideal results. The genetic algorithm produces appropriate planning data to avoid delays. This research uses the methods of data collecti

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Carnahan, J., and R. Sinha. "Nature's algorithms [genetic algorithms]." IEEE Potentials 20, no. 2 (2001): 21–24. http://dx.doi.org/10.1109/45.954644.

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M., Nishidhar Babu, Kiran Y., and Ramesh |. V. Rajendra A. "Tackling Real Coded Genetic Algorithms." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 217–23. https://doi.org/10.31142/ijtsrd5905.

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Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with t

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Kim, Yong-Hyuk, Zong Woo Geem, and Yourim Yoon. "Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization." Mathematics 13, no. 9 (2025): 1409. https://doi.org/10.3390/math13091409.

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The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solution

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Teses / dissertações sobre o assunto "Genetic algorithms"

1

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.

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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.

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O objetivo do trabalho é estudar a viabilidade de tratar problemas de otimização, considerados intratáveis, através de Algoritmos Genéticos, desenvolvendo critérios para a avaliação qualitativa de um Algoritmo Genético. Dentro deste tema, abordam-se estudos sobre complexidade, classes de problemas, análise e desenvolvimento de algoritmos e Algoritmos Genéticos, este ultimo sendo objeto central do estudo. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproxim

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3

Abu-Bakar, Nordin. "Adaptive genetic algorithms." Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343268.

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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.

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Wagner, Stefan. "Looking inside genetic algorithms /." Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00116856.pdf.

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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.

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Clustering is the search for those partitions that reflect the structure of an object set. Traditional clustering algorithms search only a small sub-set of all possible clusterings (the solution space) and consequently, there is no guarantee that the solution found will be optimal. We report here on the application of Genetic Algorithms (GAs) -- stochastic search algorithms touted as effective search methods for large and complex spaces -- to the problem of clustering. GAs which have been made applicable to the problem of clustering (by adapting the representation, fitness function,

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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.

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8

Delman, Bethany. "Genetic algorithms in cryptography /." Link to online version, 2003. https://ritdml.rit.edu/dspace/handle/1850/263.

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Krüger, Franz David, and 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.

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Maskininlärning har blivit allt vanligare inom näringslivet. Informationsinsamling med Data mining (DM) har expanderats och DM-utövare använder en mängd tumregler för att effektivisera tillvägagångssättet genom att undvika en anständig tid att ställa in hyperparametrarna för en given ML-algoritm för nå bästa träffsäkerhet. Förslaget i denna rapport är att införa ett tillvägagångssätt som systematiskt optimerar ML-algoritmerna med hjälp av genetiska algoritmer (GA), utvärderar om och hur modellen ska konstrueras för att hitta globala lösningar för en specifik datamängd. Genom att implementera g

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Yan, Ping. "Theory of simple genetic algorithms." Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446649.

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Mais fontes

Livros sobre o assunto "Genetic algorithms"

1

Man, K. F., K. S. Tang, and S. Kwong. Genetic Algorithms. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0577-0.

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2

1942-, Buckles Bill P., and Petry Fred, eds. Genetic algorithms. IEEE Computer Society Press, 1986.

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3

Anup, Kumar, and Gupta Yash P, eds. Genetic algorithms. Pergamon, 1995.

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4

Dr, Herrera Francisco, and Verdegay José-Luis, eds. Genetic algorithms and soft computing. Physica-Verlag, 1996.

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5

Luque, Gabriel, and Enrique Alba. Parallel Genetic Algorithms. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22084-5.

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Mutingi, Michael, and Charles Mbohwa. Grouping Genetic Algorithms. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-44394-2.

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7

E, Haupt S., ed. Practical genetic algorithms. 2nd ed. John Wiley, 2004.

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E, Haupt S., ed. Practical genetic algorithms. Wiley, 1998.

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9

Lance, Chambers, ed. The practical handbook of genetic algorithms: Applications. 2nd ed. Chapman & Hall/CRC, 2001.

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10

Lance, Chambers, ed. Practical handbook of genetic algorithms. CRC Press, 1995.

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Capítulos de livros sobre o assunto "Genetic algorithms"

1

Hardy, Yorick, and Willi-Hans Steeb. "Genetic Algorithms." In Classical and Quantum Computing. Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8366-5_15.

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2

Du, Ke-Lin, and M. N. S. Swamy. "Genetic Algorithms." In Search and Optimization by Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_3.

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3

Sastry, Kumara, David E. Goldberg, and Graham Kendall. "Genetic Algorithms." In Search Methodologies. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-6940-7_4.

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4

Rathore, Heena. "Genetic Algorithms." In Mapping Biological Systems to Network Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29782-8_8.

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Ansari, Nirwan, and Edwin Hou. "Genetic Algorithms." In Computational Intelligence for Optimization. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_6.

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Rowe, Jonathan E. "Genetic Algorithms." In Springer Handbook of Computational Intelligence. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43505-2_42.

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Reeves, Colin R. "Genetic Algorithms." In Handbook of Metaheuristics. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-1665-5_5.

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Dracopoulos, Dimitris C. "Genetic Algorithms." In Perspectives in Neural Computing. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0903-7_7.

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Kingdon, Jason. "Genetic Algorithms." In Perspectives in Neural Computing. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0949-5_4.

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10

Dawid, Herbert. "Genetic Algorithms." In Lecture Notes in Economics and Mathematical Systems. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-00211-7_3.

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Trabalhos de conferências sobre o assunto "Genetic algorithms"

1

Antonini, G. "Genetic Algorithms: An Introduction." In 15th International Zurich Symposium and Technical Exposition on Electromagnetic Compatibility. IEEE, 2003. https://doi.org/10.23919/emc.2003.10806154.

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2

Chapman, Colin D., Kazuhiro Saitou, and 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.

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Abstract The Genetic Algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology optimization. Given a structure’s boundary conditions and maximum allowable design domain, a discretized design representation is created. Populations of genetic algorithm “chromosomes” are then mapped into the design representation, creating potentially optimal structure topologies. Utilizing genetics-based operators such as crossover and mutation, generations of increasingly-desirable structure topologies are created. In this paper, the u

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3

Skomorokhov, Alexander O. "Genetic algorithms." In the conference. ACM Press, 1996. http://dx.doi.org/10.1145/253341.253399.

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Alfonseca, Manuel. "Genetic algorithms." In the international conference. ACM Press, 1991. http://dx.doi.org/10.1145/114054.114056.

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Butt, Fouad, and Abdolreza Abhari. "Genetic algorithms." In the 2010 Spring Simulation Multiconference. ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878779.

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Pandey, Hari Mohan, Anurag Dixit, and Deepti Mehrotra. "Genetic algorithms." In the CUBE International Information Technology Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2381716.2381766.

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Carlson, Susan E., Michael Ingrim, and 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.

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Abstract Genetic algorithms are investigated for use in obtaining optimal component configurations in dynamic engineering systems. Given a system layout, a database of component information from manufacturers’ catalogs, and a design specification, genetic algorithms are used to successfully select an optimal set of components. Five different algorithms are investigated for use on an hydraulic example. The algorithms are compared on the basis of expected hitting lime: the number of system simulations required to find the solution. One algorithm is found to have the best set of characteristics f

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Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel, and Luís Paquete. "Algorithm selection of anytime algorithms." In GECCO '20: Genetic and Evolutionary Computation Conference. ACM, 2020. http://dx.doi.org/10.1145/3377930.3390185.

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Ladkany, George S., and 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.

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Genetic algorithms have been extensively used as a reliable tool for global optimization. However these algorithms suffer from their slow convergence. To address this limitation, this paper proposes a two-fold approach to address these limitations. The first approach is to introduce a twinkling process within the crossover phase of a genetic algorithm. Twinkling can be incorporated within any standard algorithm by introducing a controlled random deviation from its standard progression to avoiding being trapped at a local minimum. The second approach is to introduce a crossover technique: the w

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Misir, Mustafa, Stephanus Daniel Handoko, and Hoong Chuin Lau. "Building algorithm portfolios for memetic algorithms." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2598455.

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Relatórios de organizações sobre o assunto "Genetic algorithms"

1

Arthur, Jennifer Ann. Genetic Algorithms. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1375151.

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Sharp, David H., John Reinitz, and Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada256223.

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3

Kargupta, H. Messy genetic algorithms: Recent developments. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/378868.

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Messa, K., and M. Lybanon. Curve Fitting Using Genetic Algorithms. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada247206.

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Thomas, E. V. Frequency selection using genetic algorithms. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10177075.

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6

Vlek, R. J., and D. J. M. Willems. Recipe reconstruction with genetic algorithms. Wageningen Food & Biobased Research, 2021. http://dx.doi.org/10.18174/540621.

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Cobb, Helen G., and John J. Grefenstette. Genetic Algorithms for Tracking Changing Environments. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada294075.

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Pittman, Jennifer, and C. A. Murthy. Optimal Line Fitting Using Genetic Algorithms. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada328266.

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Goldberg, David. Competent Probabilistic Model Building Genetic Algorithms. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416564.

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Vemuri, V. R. Genetic algorithms at UC Davis/LLNL. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10122640.

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