Bibliografias temáticas / Genetic algoritms

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Literatura científica selecionada sobre o tema "Genetic algoritms"

Autor: Grafiati
Publicado: 4 de junho de 2021
Última modificação: 26 de julho de 2025

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Artigos de revistas sobre o assunto "Genetic algoritms"

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Zatsarynin, Serhii. "Market Segmentation of Innovative Products Using Genetic Algoritms." Marketing and Digital Technologies 5, no. 2 (2021): 67–74. http://dx.doi.org/10.15276/mdt.5.2.2021.6.

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One way to increase the company's competitiveness is to find new market niches. The market niche is the result of innovations that stimulate hidden, potential demand, as a result of which the company, developing a new market, avoids intense competition and receives a higher rate of return. It is proved that the growing number and complexity of tasks in the field of marketing research, working with a large amount of information, leads to the need to group data. The aim of the study is to develop a universal approach to solving the problem of market segmentation of innovative products based on a
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Sherin Mary Andrews. "Emerging Role of Artificial Intelligence and Machine learning in precision medicine." international journal of engineering technology and management sciences 7, no. 4 (2023): 622–26. http://dx.doi.org/10.46647/ijetms.2023.v07i04.086.

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Precision medicine is a new discipline that customizes medical interventions and therapies to each patient based on their particular genetic, environmental, and lifestyle factors. Techniques in machine learning (ML) and artificial intelligence (AI) have become effective research tools in precision medicine. This study examines how ML and AI can be used to diagnose diseases, choose the best course of treatment, predictic prognosis and find new drugs, among other precision medicine applications.It also analyzes the algoritms that is used. It discusses the advantages, difficulties, and potential
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Lingga, Sri Wahyuni, Sutarman Sutarman, and Open Darnius. "Modelling of Subject Scheduling Systems Using Hybrid Artificial Bee Colony Algorithm." Sinkron 7, no. 3 (2023): 1599–608. http://dx.doi.org/10.33395/sinkron.v7i3.12560.

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A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the En
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Dzidolikaitė, Agnė. "GENETIC ALGORITHMS FOR MULTIDIMENSIONAL SCALING / GENETINIŲ ALGORITMŲ TAIKYMAS DAUGIAMATĖMS SKALĖMS." Mokslas – Lietuvos ateitis 7, no. 3 (2015): 275–79. http://dx.doi.org/10.3846/mla.2015.781.

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The paper analyzes global optimization problem. In order to solve this problem multidimensional scaling algorithm is combined with genetic algorithm. Using multidimensional scaling we search for multidimensional data projections in a lower-dimensional space and try to keep dissimilarities of the set that we analyze. Using genetic algorithms we can get more than one local solution, but the whole population of optimal points. Different optimal points give different images. Looking at several multidimensional data images an expert can notice some qualities of given multidimensional data. In the p
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Mendaña-Cuervo, C., and E. López-González. "La Gestión Presupuestaria de Distribución con un Algoritmo Genético Borroso." Información Tecnológica 16, no. 3 (2005): 45–56. https://doi.org/10.4067/S0718-07642005000300007.

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<strong>Resumen</strong> En este trabajo se presenta el dise&ntilde;o de un sistema de informaci&oacute;n para la toma de decisiones presupuestarias. Al tratarse de una decisi&oacute;n que afecta al futuro, este problema se caracteriza por la incertidumbre y no linealidad de la informaci&oacute;n disponible para su resoluci&oacute;n. Adem&aacute;s, el elevado n&uacute;mero de variables que intervienen lo convierten en un problema de gran complejidad. El tratamiento de la incertidumbre se ha abordado con la aplicaci&oacute;n de la Teor&iacute;a de los Subconjuntos Borrosos y el desarrollo opera
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Misevičius, Alfonsas, Andrius Blažinskas, Jonas Blonskis, and Vytautas Bukšnaitis. "Genetiniai algoritmai komivojažieriaus uždaviniui: negatyvieji ir pozityvieji aspektai*." Informacijos mokslai 50 (January 1, 2009): 173–80. http://dx.doi.org/10.15388/im.2009.0.3242.

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Šiame straipsnyje nagrinėjami klausimai, susiję su genetinių algoritmų taikymu, sprendžiant gerai žinomą kombinatorinio optimizavimo uždavinį – komivojažieriaus uždavinį (KU) (angl. traveling salesman problem). Svarstoma, jog genetinio algoritmo efektyvumui didelę įtaką turi uždavinio specifi nės savybės, todėl labai svarbu kūrybiškai sudaryti genetinį algoritmą konkrečiam sprendžiamam uždaviniui. Pateikiami eksperimentų, atliktų su realizuotu genetiniu algoritmu, rezultatai, iliustruojantys skirtingų veiksnių įtaką rezultatų kokybei. Konstatuojama, kad tinkamas genetinių operatorių ir lokalio
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Suseno, Eka Widya, Alfian Ma'arif, and Riky Dwi Puriyanto. "Tuning Parameter Pengendali PID dengan Metode Algoritma Genetik pada Motor DC." TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol 8, no. 1 (2022): 1–13. http://dx.doi.org/10.15575/telka.v8n1.1-13.

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Saat ini, pengendali Proportional Integral Derivative (PID) digunakan secara umum untuk mendapatkan solusi optimum. Solusi dikatakan optimum apabila output di kehidupan nyata sesuai dengan output yang telah ditentukan. Oleh karena itu, pengendali adalah suatu hal yang dibutuhkan. Tantangan dalam menggunakan pengendali adalah tuning parameter untuk mencari konstanta parameter PID seperti Proporsional Gain (KP), Waktu Integral (KI) dan Waktu Derivatif (KD). Untuk memaksimalkan kinerja motor DC, pengaturan pengendali PID yang tepat merupakan hal yang sangat penting. Desain pengendali PID sebagai
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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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Erama, Rahman, and Retantyo Wardoyo. "Modifikasi Algoritma Genetika untuk Penyelesaian Permasalahan Penjadwalan Pelajaran Sekolah." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 10, no. 1 (2014): 111. http://dx.doi.org/10.22146/ijccs.6539.

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AbstrakModifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover in
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Mubarok, Muhammad Iqbal, Icih Sukarsih, and Yurika Permanasari. "Analisis Panjang Populasi dan Banyak Generasi Algoritma Genetika pada Traveling Salesman Problem." Bandung Conference Series: Mathematics 3, no. 2 (2023): 184–91. http://dx.doi.org/10.29313/bcsm.v3i2.9467.

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Abstrak. Traveling Salesman Problem (TSP) adalah masalah optimasi yang penting dalam bidang ilmu komputer dan matematika. Tujuan utama dari TSP adalah mencari rute terpendek yang melibatkan kunjungan ke sejumlah titik atau kota tertentu oleh seorang salesman. Algoritma Genetika (AG) telah menjadi salah satu pendekatan populer dalam menyelesaikan Traveling Salesman Problem karena kemampuannya untuk menghasilkan solusi yang mendekati optimum. Pada penelitian ini, dilakukan analisis mengenai panjang populasi dan banyak generasi pada Algoritma Genetika dalam menyelesaikan Traveling Salesman Proble
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Teses / dissertações sobre o assunto "Genetic algoritms"

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Němec, Jan. "Efektivita evolučních algoritmů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242045.

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This master's thesis is focused on evolutionary algorithms. The goal of this thesis is to chooche a proper algorithm which will solve a chosen problem. In this case the chosen algorithm is the genetic algorithm and the chosen problem is the travelling salesman problem. The result of this thesis will be implementation of the algorithm, finding the proper setup and lastly the measurment of the results for various input data.
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Anisenia, Andrei. "Stochastic Search Genetic Algorithm Approximation of Input Signals in Native Neuronal Networks." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/26220.

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The present work investigates the applicability of Genetic Algorithms (GA) to the problem of signal propagation in Native Neuronal Networks (NNNs). These networks are comprised of neurons, some of which receive input signals. The signals propagate though the network by transmission between neurons. The research focuses on the regeneration of the output signal of the network without knowing the original input signal. The computational complexity of the problem is prohibitive for the exact computation. We propose to use a heuristic approach called Genetic Algorithm. Three algorithms are develope
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Rezende, Rodrigo Alvarenga. "Desenvolvimento de processos para a aplicação do alginato na biofabricação." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/266851.

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Orientadores: Rubens Maciel Filho, Paulo Jorge da Silva Bártolo<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química<br>Made available in DSpace on 2018-08-17T08:33:25Z (GMT). No. of bitstreams: 1 Rezende_RodrigoAlvarenga_D.pdf: 29746268 bytes, checksum: 24e72154795e591bf70b4fc339cf0127 (MD5) Previous issue date: 2010<br>Resumo: A busca por soluções que resgatem a saúde e a dignidade de vítimas de acidentes ou com problemas de saúde, como perda ou danos de órgãos, tem motivado uma evolução acelerada da biofabricação. Novos materiais e técnicas de fabricaç
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Silva, Ricardo Coelho. "Contribuições ao estudo de programação não-linear com incertezas." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261703.

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Orientadores: Akebo Yamakami, Luiza Amalia Pinto Cantão<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-05T00:06:55Z (GMT). No. of bitstreams: 1 Silva_RicardoCoelho_M.pdf: 524540 bytes, checksum: f7f63ffea96b8bbf8897b3c3f27fc427 (MD5) Previous issue date: 2005<br>Resumo: Neste trabalho foram desenvolvidos alguns métodos iterativos e algoritmos meta-heurísticos, adaptados de modelos clássicos, que solucionam problemas de programação não-linear com parâmetros fuzzy na função objetivo e no con
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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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Victorino, Igor Ricardo de Souza. "Otimização de um reator industrial de produção de alcool ciclico utilizando algoritmos geneticos." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267417.

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Orientador: Rubens Maciel Filho<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica<br>Made available in DSpace on 2018-08-05T16:58:49Z (GMT). No. of bitstreams: 1 Victorino_IgorRicardodeSouza_D.pdf: 14787602 bytes, checksum: 301a75422a3abe39a4a0cf4033a18eae (MD5) Previous issue date: 2005<br>Resumo: Nas últimas décadas, com o aumento da competitividade do mercado mundial (redução de custos, preços, aumento da produtividade e eficiência dos processos produtivos), houve um grande interesse em aperfeiçoar e otimizar os diversos processos de variadas plantas
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Bortel, Martin. "Evoluční algoritmy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219807.

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Thesis describes main attributes and principles of Evolutionary and Genetic algorithms. Crossover, mutation and selection are described as well as termination options. There are examples of practical use of evolutionary and genetic algorithms. Optimization of distribution routes using PHP&MySQL and Google Maps API technologies.
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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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Staurovská, Jana. "Nástroj pro vizuální analýzu evoluce obvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236483.

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The main goal of the master's thesis is to compose a study on cartesian genetic programming with focus on evolution of circuits and to design a concept for visualisation of this evolution. Another goal is to create a program to visualise the circuit evolution in cartesian genetic programming, its generations and chromosomes. The program is capable of visualising the changes between generations and chromosomes and comparing more chromosomes at once. Several user cases had been prepared for the resulting program.
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Kolářová, Jana. "Evoluční algoritmy pro ultrazvukovou perfúzní analýzu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401037.

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This master´s thesis is focused on the application of evolutionary algorithms for interleaving data obtained by ultrasound scanning of tissue. The interleaved curve serves to estimate perfusion parameters, thus allowing to detect possible pathophysiology in the scanned area. The theoretical introduction is devoted to perfusion and its parameters, contrast agents for ultrasonic application, ultrasonic modality scanning, optimization, evolutionary algorithms in general and two selected evolutionary algorithms - genetic algorithm and bee algorithm. These algorithms were tested on noisy data obtai
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Livros sobre o assunto "Genetic algoritms"

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

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

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Jörg, Biethahn, and Nissen Volker 1965-, eds. Evolutionary algorithms in management applications. Springer, 1995.

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

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

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

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J, Grefenstette John, ed. Genetic algorithms for machine learning. Kluwer Academic Publishers, 1994.

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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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Capítulos de livros sobre o assunto "Genetic algoritms"

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Affenzeller, Michael. "New Generic Hybrids Based upon Genetic Algorithms." In Advances in Artificial Intelligence — IBERAMIA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_34.

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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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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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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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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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Trabalhos de conferências sobre o assunto "Genetic algoritms"

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Andreichikov, Alexandr V., Anatoliy A. Zudinov, Anatoliy A. Nasyrov, and Inna S. Kravchuk. "Genetic Algoritms for the Synthesis of Innovative Engineering Solutions." In 2019 International Conference "Quality Management, Transport and Information Security, Information Technologies" (IT&QM&IS). IEEE, 2019. http://dx.doi.org/10.1109/itqmis.2019.8928382.

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Mateos, Maria Jose Tena, Cristina Lopez-Pujalte, and Antonio Munoz Canavate. "Decision Support System based on Competitive Intelligence and Genetic Algoritms." In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2020. http://dx.doi.org/10.23919/cisti49556.2020.9141060.

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Martinez, Ricardo, Antonio Rodriguez, Oscar Castillo, and Luis T. Aguilar. "Type-2 Fuzzy Logic Controllers Optimization Using Genetic Algoritms and Particle Swarm Optimization." In 2010 IEEE International Conference on Granular Computing (GrC-2010). IEEE, 2010. http://dx.doi.org/10.1109/grc.2010.43.

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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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Santana, Wesley, Waslon Terllizzie Lopes, and Francisco Madeiro Bernardino Junior. "Uma Função Objetivo para o Problema de Otimização do Projeto de Quantizadores Vetoriais Robustos." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2024. http://dx.doi.org/10.21528/cbic2023-091.

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A Quantização Vetorial Robusta (QVR) é um problema de otimização de natureza combinatorial, o qual envolve a atribuição de índices aos vetores-código do dicionário. No processo de otimização da QVR, a função objetivo utilizada é o índice de desordem do dicionário, o qual deve ser minimizado. Neste trabalho, foi proposta e avaliada a utilização de uma função de densidade de probabilidade dos vetores-código para ponderar no calculo do índice de desordem. Para a tarefa de otimização dos dicionários foram utilizados os algoritmos Busca por Cardume (FSS Fish School Search), Otimização por Enxame de
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Neville, Melvin, and Anaika Sibley. "Developing a generic genetic algorithm." In the 2002 annual ACM SIGAda international conference. ACM Press, 2002. http://dx.doi.org/10.1145/589451.589462.

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Lyons, Donald P., and Ken D. Kihm. "A New Tomographic Image Reconstruction Method: Using a Hybrid Genetic Algorithm." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0134.

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Abstract A new tomographic image reconstruction method is introduced using a hybrid genetic algorithm. Both traditional genetic algorithms and a hybrid genetic algorithm associated with the concurrent simplex method are described and the merits of each are discussed. For the purposes of discussion, results from simple axisymmetric phantom test data are shown for the traditional and the hybrid genetic algorithm approaches. This new methodology shows the potential of alleviating difficulties with existing tomographic algorithms under the constraint of limited projection data.
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8

Nielsen, David R., and Kazem Kazerounian. "Evolutionary Design of Planar Kinematic Chains." In ASME 1998 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/detc98/mech-5936.

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Abstract A procedure is developed to optimize planar mechanism type. A Genetic Algorithm is used to cycle populations of kinematic chain link adjacency matrices, through selection, crossover, and mutation. During this optimization, fit kinematic chains survive while unfit kinematic chains do not. Upon convergence, synthesized kinematic chains of high fitness remain. This technique was lead to be called the Genetic Algorithm for Type Synthesis (GATS). GATS introduces four new ideas for the type synthesis of mechanisms. First, it does not permute all possible kinematic chains. It searches for th
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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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Jacquenot, Guillaume, Fouad Bennis, Jean-Jacques Maisonneuve, and Philippe Wenger. "2D Multi-Objective Placement Algorithm for Free-Form Components." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87603.

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This article presents a generic method to solve 2D multiobjective placement problem for free-form components. The proposed method is a relaxed placement technique combined with an hybrid algorithm based on a genetic algorithm and a separation algorithm. The genetic algorithm is used as a global optimizer and is in charge of efficiently exploring the search space. The separation algorithm is used to legalize solutions proposed by the global optimizer, so that placement constraints are satisfied. A test case illustrates the application of the proposed method. Extensions for solving the 3D proble
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Relatórios de organizações sobre o assunto "Genetic algoritms"

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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2

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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4

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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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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