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Добірка наукової літератури з теми "Genetic algorithms"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 30 липня 2024

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Статті в журналах з теми "Genetic algorithms"

1

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

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2

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

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3

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 (December 31, 2017): 217–23. http://dx.doi.org/10.31142/ijtsrd5905.

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4

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 (May 30, 2022): 9–23. http://dx.doi.org/10.25139/ijair.v4i1.4323.

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Анотація:

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 algorithms among evolutionary algorithms for scheduling are genetic algorithms and memetics algorithms. When it comes to the two algorithms, using genetic algorithms or memetics algorithms may not always offer the optimum outcomes in every situation. Therefore, it is necessary to compare the genetic algorithm and the algorithm's memetic algorithm to determine which one is suitable for the nurse picket schedule. From the results of this study, the memetic algorithm is better than the genetic algorithm in making picket schedules. The memetic algorithm with a population of 10000 and a generation of 5000 does not produce convergent data. While for the genetic algorithm, when the population is 5000 and the generation is 50, the data convergence starts. For accuracy, the memetic algorithm violates only 24 of the 124 existing constraints (80,645%). The genetic algorithm violates 27 of the 124 constraints (78,225%). The average runtime used to generate optimal data using the memetic algorithm takes 20.935592 seconds. For the genetic algorithm, it takes longer, as much as 53.951508 seconds.

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5

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. Furthermore, the utility of the evolutionary algorithm has been demonstrated by programming it in Python code and comparing the outcomes to conventional intuitions.

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6

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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7

Frenzel, J. F. "Genetic algorithms." IEEE Potentials 12, no. 3 (October 1993): 21–24. http://dx.doi.org/10.1109/45.282292.

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8

Fulkerson, William F. "Genetic Algorithms." Journal of the American Statistical Association 97, no. 457 (March 2002): 366. http://dx.doi.org/10.1198/jasa.2002.s468.

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9

Forrest, Stephanie. "Genetic algorithms." ACM Computing Surveys 28, no. 1 (March 1996): 77–80. http://dx.doi.org/10.1145/234313.234350.

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10

Holland, John H. "Genetic Algorithms." Scientific American 267, no. 1 (July 1992): 66–72. http://dx.doi.org/10.1038/scientificamerican0792-66.

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Дисертації з теми "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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2

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 aproximativos e análise da complexidade. O fato de um problema ser teoricamente resolvível por um computador não é suficiente para o problema ser na prática resolvível. Um problema é denominado tratável se no pior caso possui um algoritmo razoavelmente eficiente. E um algoritmo é dito razoavelmente eficiente quando existe um polinômio p tal que para qualquer entrada de tamanho n o algoritmo termina com no máximo p(n) passos [SZW 84]. Já que um polinômio pode ser de ordem bem alta, então um algoritmo de complexidade polinomial pode ser muito ineficiente. Genéticos é que se pode encontrar soluções aproximadas de problemas de grande complexidade computacional mediante um processo de evolução simulada[LAG 96]. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos com a consciência de qualidade, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproximativos e análise da complexidade. Uma axiomatização tem o propósito de dar a semântica do algoritmo, ou seja, ela define, formalmente, o funcionamento do algoritmo, mais especificamente das funções e procedimentos do algoritmo. E isto, possibilita ao projetista de algoritmos uma maior segurança no desenvolvimento, porque para provar a correção de um Algoritmo Genético que satisfaça esse modelo só é necessário provar que os procedimentos satisfazem os axiomas. Para ter-se consciência da qualidade de um algoritmo aproximativo, dois fatores são relevantes: a exatidão e a complexidade. Este trabalho levanta os pontos importantes para o estudo da complexidade de um Algoritmo Genético. Infelizmente, são fatores conflitantes, pois quanto maior a exatidão, pior ( mais alta) é a complexidade, e vice-versa. Assim, um estudo da qualidade de um Algoritmo Genético, considerado um algoritmo aproximativo, só estaria completa com a consideração destes dois fatores. Mas, este trabalho proporciona um grande passo em direção do estudo da viabilidade do tratamento de problemas de otimização via Algoritmos Genéticos.
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.

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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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4

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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5

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

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6

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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Анотація:

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, and developing suitable evolutionary operators) are known as Genetic Clustering Algorithms (GCAs). There are two parts to our investigation of GCAs: first we look at clustering into a given number of clusters. The performance of GCAs on three generated data sets, analysed using 4320 differing combinations of adaptions, establishes their efficacy. Choice of adaptions and parameter settings is data set dependent, but comparison between results using generated and real data sets indicate that performance is consistent for similar data sets with the same number of objects, clusters, attributes, and a similar distribution of objects. Generally, group-number representations are better suited to the clustering problem, as are dynamic scaling, elite selection and high mutation rates. Independent generalised models fitted to the correctness and timing results for each of the generated data sets produced accurate predictions of the performance of GCAs on similar real data sets. While GCAs can be successfully adapted to clustering, and the method produces results as accurate and correct as traditional methods, our findings indicate that, given a criterion based on simple distance metrics, GCAs provide no advantages over traditional methods. Second, we investigate the potential of genetic algorithms for the more general clustering problem, where the number of clusters is unknown. We show that only simple modifications to the adapted GCAs are needed. We have developed a merging operator, which with elite selection, is employed to evolve an initial population with a large number of clusters toward better clusterings. With regards to accuracy and correctness, these GCAs are more successful than optimisation methods such as simulated annealing. However, such GCAs can become trapped in local minima in the same manner as traditional hierarchical methods. Such trapping is characterised by the situation where good (k-1)-clusterings do not result from our merge operator acting on good k-clusterings. A marked improvement in the algorithm is observed with the addition of a local heuristic.

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7

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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9

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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Анотація:

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 genetiska algoritmer på två utvalda ML-algoritmer, K-nearest neighbors och Random forest, med två numeriska datamängder, Iris-datauppsättning och Wisconsin-bröstcancerdatamängd. Modellen utvärderas med träffsäkerhet och beräkningstid som sedan jämförs med sökmetoden exhaustive search. Resultatet har visat att GA fungerar bra för att hitta bra träffsäkerhetspoäng på en rimlig tid. Det finns vissa begränsningar eftersom parameterns betydelse varierar för olika ML-algoritmer.
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.

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10

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

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Книги з теми "Genetic algorithms"

1

Man, K. F., K. S. Tang, and S. Kwong. Genetic Algorithms. London: 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. Los Alamitos, Calif: IEEE Computer Society Press, 1986.

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3

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

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4

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

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5

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

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6

Mutingi, Michael, and Charles Mbohwa. Grouping Genetic Algorithms. Cham: 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. Hoboken, N.J: John Wiley, 2004.

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8

E, Haupt S., ed. Practical genetic algorithms. New York: Wiley, 1998.

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9

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

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10

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

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Частини книг з теми "Genetic algorithms"

1

Hardy, Yorick, and 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.

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2

Du, Ke-Lin, and 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.

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3

Sastry, Kumara, David E. Goldberg, and 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.

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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.

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5

Ansari, Nirwan, and 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.

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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.

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7

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.

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8

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.

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9

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.

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10

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.

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Тези доповідей конференцій з теми "Genetic algorithms"

1

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

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2

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

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3

Butt, Fouad, and 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.

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4

Pandey, Hari Mohan, Anurag Dixit, and 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.

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5

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 use of the genetic algorithm (GA) in structural topology optimization is presented. An overview of the genetic algorithm will describe the genetics-based representations and operators used in a typical genetic algorithm search. After defining topology optimization and its relation to the broader area of structural optimization, a review of previous research in GA-based and non-GA-based structural optimization is provided. The design representations, and methods for mapping genetic algorithm “chromosomes” into structure topology representations, are then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of beam cross-section topologies and cantilevered plate topologies, and we also investigate efficient techniques for using finite element analysis in a genetic algorithm-based search. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

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6

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 for solving component selection problems.

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7

Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel, and 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.

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8

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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Анотація:

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 weighted average normally-distributed arithmetic crossover that is shown to enhance the rate of convergence. Two possible twinkling genetic algorithms are proposed. The performance of the proposed algorithms is successfully compared to simple genetic algorithms using various standard mathematical and engineering design problems. The twinkling genetic algorithms show their ability to consistently reach known global minima, rather than nearby sub-optimal points with a competitive rate of convergence.

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9

Misir, Mustafa, Stephanus Daniel Handoko, and 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.

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10

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.

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Звіти організацій з теми "Genetic algorithms"

1

Arthur, Jennifer Ann. Genetic Algorithms. Office of Scientific and Technical Information (OSTI), August 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. Fort Belvoir, VA: Defense Technical Information Center, January 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), September 1996. http://dx.doi.org/10.2172/378868.

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4

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

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5

Thomas, E. V. Frequency selection using genetic algorithms. Office of Scientific and Technical Information (OSTI), May 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: Wageningen Food & Biobased Research, 2021. http://dx.doi.org/10.18174/540621.

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7

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

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8

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

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9

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

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10

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

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