Gotowe bibliografie tematyczne / Genetic algorithm

Gotowa bibliografia na temat „Genetic algorithm”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 8 czerwca 2024

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Artykuły w czasopismach na temat "Genetic algorithm"

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Kallab, Chadi, Samir Haddad i Jinane Sayah. "Flexible Traceable Generic Genetic Algorithm". Open Journal of Applied Sciences 12, nr 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.

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Neville, Melvin, i Anaika Sibley. "Developing a generic genetic algorithm". ACM SIGAda Ada Letters XXIII, nr 1 (marzec 2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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Chouh, M., i K. Boukhetala. "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization". International Journal of Machine Learning and Computing 6, nr 4 (sierpień 2016): 231·—234. http://dx.doi.org/10.18178/ijmlc.2016.6.4.603.

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Kandeeban, Selvakani S., i R. S. Rajesh. "Desegregated ID Execution Using Genetic Algorithm". International Journal of Engineering and Technology 1, nr 1 (2009): 45–49. http://dx.doi.org/10.7763/ijet.2009.v1.8.

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OKABE, Hidehiko. "Genetic Algorithm". Journal of Japan Society for Fuzzy Theory and Systems 3, nr 4 (1991): 626–38. http://dx.doi.org/10.3156/jfuzzy.3.4_2.

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Dan Liu, Dan Liu, Shu-Wen Yao Dan Liu, Hai-Long Zhao Shu-Wen Yao, Xin Sui Hai-Long Zhao, Yong-Qi Guo Xin Sui, Mei-Ling Zheng Yong-Qi Guo i Li Li Mei-Ling Zheng. "Research on Mutual Information Feature Selection Algorithm Based on Genetic Algorithm". 電腦學刊 33, nr 6 (grudzień 2022): 131–41. http://dx.doi.org/10.53106/199115992022123306011.

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<p>Feature selection is an important part of data preprocessing. Feature selection algorithms that use mutual information as evaluation can effectively handle different types of data, so it has been widely used. However, the potential relationship between relevance and redundancy in the evaluation criteria is often ignored, so that effective feature subsets cannot be selected. Optimize the evaluation criteria of the mutual information feature selection algorithm and propose a mutual information feature selection algorithm based on dynamic penalty factors (Dynamic Penalty Factor Mutual Information Feature Selection Algorithm, DPMFS). The penalty factor is dynamically calculated with different selected features, so as to achieve a relative balance between relevance and redundancy, and effectively play the synergy between relevance and redundancy, and select a suitable feature subset. Experimental results verify that the DPMFS algorithm can effectively improve the classification accuracy of the feature selection algorithm. Compared with the traditional chi-square, MIM and MIFS feature selection algorithms, the average classification accuracy of the random forest classifier for the six standard datasets is increased by 3.73%, 3.51% and 2.44%, respectively.</p> <p>&nbsp;</p>
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Aivaliotis-Apostolopoulos, Panagiotis, i Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization". PLOS ONE 17, nr 9 (23.09.2022): e0275094. http://dx.doi.org/10.1371/journal.pone.0275094.

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.
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Anfyorov, M. A. "Genetic clustering algorithm". Russian Technological Journal 7, nr 6 (10.01.2020): 134–50. http://dx.doi.org/10.32362/2500-316x-2019-7-6-134-150.

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The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making. The algorithm makes it possible to account for different preferences of the analyst in clustering reflected in a calculation formula of fitness function. The place of this algorithm among those used for cluster analysis has been shown. The algorithm is simple in its program implementation, which increases its usage reliability. The used technology of evolutionary modeling is rather expanded in the mentioned algorithm. Firstly, the decimal chromosomes coding is used instead of the traditional binary coding. This has resulted from the fact that the chromosome genes condition is multiple and not binary. Moreover, this is due to the absence of the genetic operator of inversion in this algorithm. Secondly, a new genetic operator used for filtering has been implemented. This operator eliminates chromosomes that do not meet the required clusters quantity condition in a task. Such chromosomes can appear in the stochastic process of their evolution. The presented algorithm has been studied in a series of simulation experiments. As a result, it has been found that stabilization of splitting into clusters is reached when the number of completed generations of evolution is 200 and more, and the population size is rather small: from 150 chromosomes (in this case no considerable amount of random-access store is required). The calculations carried out on real data showed for this algorithm the high quality of clustering and the acceptable computing speed of the same order with the computing speed of SOM and “k-means” algorithms.
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EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, nr 1 (styczeń 2002): 23–30. http://dx.doi.org/10.1017/s0890060401020030.

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Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.
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Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm". Key Engineering Materials 439-440 (czerwiec 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
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Rozprawy doktorskie na temat "Genetic algorithm"

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Harris, Steven C. "A genetic algorithm for robust simulation optimization". Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178645751.

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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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Liakhovitch, Evgueni. "Genetic algorithm using restricted sequence alignments". Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172598174.

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Bentley, Peter John. "Generic evolutionary design of solid objects using a genetic algorithm". Thesis, University of Huddersfield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338599.

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This thesis investigates the novel idea of using a computer to create and optimise conceptual designs of a range of differently-shaped three-dimensional solid objects from scratch. An extensive literature review evaluates all related areas of research and reveals that no such system exists. The development of a generic evolutionary design system, using a genetic algorithm (GA) as its core, is then presented. The thesis describes a number of significant advances necessitated by the development of this system. Firstly, a new low-parameter spatial-partitioning representation of solid objects is introduced, which allows a wide range of solid objects to be appropriately defined and easily manipulated by a GA. Secondly, multiobjective optimisation is investigated to allow users to define design problems without fine-tuning large numbers of weights. As a result of this, the new concepts of acceptability, range-independence and importance are introduced and a new multiobjective ranking method is identified as being most appropriate. Thirdly, variable-length chromosomes in GAs are addressed, to allow the number of primitive shapes that define a design to be variable. This problem is overcome by the use of a new hierarchical crossover operator, which uses the new concept of a semantic hierarchy to reference chromosomes. Additionally, the thesis describes how the performance of the GA is improved by using an explicit mapping stage between genotypes and phenotypes, steady-state reproduction with preferential selection, and a new lifespan limiter. A library of modular evaluation software is also presented, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of the generic evolutionary design of solid objects is demonstrated by presenting the successful evolution of both conventional and unconventional designs for fifteen different solid-object design tasks, e.g. tables, heatsinks, penta-prisms, boat hulls, aerodynamic cars.
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Chohan, Ossam. "University Scheduling using Genetic Algorithm". Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3791.

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The automated timetabling and scheduling is one of the hardest problem areas. This isbecause of constraints and satisfying those constraints to get the feasible and optimizedschedule, and it is already proved as an NP Complete (1) [1]. The basic idea behind this studyis to investigate the performance of Genetic Algorithm on general scheduling problem underpredefined constraints and check the validity of results, and then having comparative analysiswith other available approaches like Tabu search, simulated annealing, direct and indirectheuristics [2] and expert system. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems and later analysis will prove this argument. The programis written in C++ and analysis is done by using variation in various parameters.
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Murugan, Anandaraj Soundarya Raja. "University Timetabling using Genetic Algorithm". Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3792.

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The field of automated timetabling and scheduling meeting all the requirementsthat we call constraints is always difficult task and already proved as NPComplete. The idea behind my research is to implement Genetic Algorithm ongeneral scheduling problem under predefined constraints and check the validityof results, and then I will explain the possible usage of other approaches likeexpert systems, direct heuristics, network flows, simulated annealing and someother approaches. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems. The program written in C++ and analysisis done with using various tools explained in details later.
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Wen, Mengtao. "Optical design by genetic algorithm". Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27080.

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The design of holographic diffusers and birefringent filters by using the genetic algorithm is studied in this thesis. A holographic diffuser is an optical device that distributes light from a light source with a desired spatial distribution pattern. For diffuse infrared wireless home networking, a holographic diffuser is used to diffuse the laser light to cover a large indoor area such as an office, and at the same time to solve the eye safety problem. In the thesis, a modified genetic algorithm is proposed to design holographic diffuser. The novel algorithm combines the conventional genetic algorithm and the simulated annealing algorithm, in which the simulated annealing algorithm is used to maintain a better diversity of chromosomes for the genetic algorithm. A better performance in locating the global minimum is demonstrated. A 4-level phase-only hologram that is designed by the modified genetic algorithm is fabricated on a quartz substrate. The fabricated hologram is experimentally verified. The results show that diffraction pattern generated by the fabricated hologram agrees well with the theoretically calculated pattern. (Abstract shortened by UMI.)
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Cai, Zesi. "Genetic Algorithm for Integrated SoftwarePipelining". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76088.

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The purpose of the thesis was to study the feasibility of using geneticalgorithm (GA) to do the integrated software pipelining (ISP). Different from phasedcode generation, ISP is a technique which integrates instruction selection, instructionscheduling, and register allocation together when doing code generation. ISP is able toprovide a lager solution space than phased way does, which means that ISP haspotential to generate more optimized code than phased code generation. However,integrated compiling costs more than phased compiling. GA is stochastic beam searchalgorithm which can accelerate the solution searching and find an optimized result.An experiment was designed for verifying feasibility of implementing GA for ISP(GASP). The implemented algorithm analyzed data dependency graphs of loop bodies,created genes for the graphs and evolved, generated schedules, calculated andevaluated fitness, and obtained optimized codes. The fitness calculation wasimplemented by calculating the maximum value between the smallest possibleresource initiation interval and the smallest possible recurrence initiation interval. Theexperiment was conducted by generating codes from data dependency graphsprovided in FFMPEG and comparing the performance between GASP and integerlinear programming (ILP). The results showed that out of eleven cases that ILP hadgenerated code, GASP performed close to ILP in seven cases. In all twelve cases thatILP did not have result, GASP did generate optimized code. To conclude, the studyindicated that GA was feasible of being implemented for ISP. The generated codesfrom GASP performed similar with the codes from ILP. And for the dependencygraphs that ILP could not solve in a limited time, GASP could also generate optimizedresults.
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Haroun, Paul. "Genetic algorithm and data visualization". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0017/MQ37125.pdf.

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Edelstein, Jeffrey. "Truckin' : the genetic algorithm way". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59320.pdf.

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Książki na temat "Genetic algorithm"

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Kramer, Oliver. Genetic Algorithm Essentials. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52156-5.

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Tao, Jili, Ridong Zhang i Yong Zhu. DNA Computing Based Genetic Algorithm. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2.

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Dey, Nilanjan, red. Applied Genetic Algorithm and Its Variants. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3428-7.

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The simple genetic algorithm: Foundations and theory. Cambridge, Mass: MIT Press, 1999.

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French, Alan Paul. A genetic algorithm for the satisfiability problem. Loughborough, Leics: Loughborough University Business School, 1995.

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K, Kokula Krishna Hari, red. Energy Efficient Node Placement Using Genetic Algorithm. Pondicherry, India: Association of Scientists, Developers and Faculties, 2014.

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Karr, C. L. Genetic algorithm applied to least squares curve fitting. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1990.

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Graves, Paul J. J. A genetic algorithm for routing printed circuit boards. Manchester: University of Manchester, Department of Computer Science, 1995.

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Timmerman, Michael Jay. A genetic algorithm based anti-submarine warfare simulator. Monterey, Calif: Naval Postgraduate School, 1993.

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Gen, Mitsuo. Network models and optimization: Multiobjective genetic algorithm approach. London: Springer, 2008.

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Części książek na temat "Genetic algorithm"

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Čepin, Marko. "Genetic Algorithm". W Assessment of Power System Reliability, 257–69. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-688-7_18.

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Brabazon, Anthony, Michael O’Neill i Seán McGarraghy. "Genetic Algorithm". W Natural Computing Algorithms, 21–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-43631-8_3.

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Wang, Sun-Chong. "Genetic Algorithm". W Interdisciplinary Computing in Java Programming, 101–16. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0377-4_6.

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Ünal, Muhammet, Ayça Ak, Vedat Topuz i Hasan Erdal. "Genetic Algorithm". W Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, 19–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32900-5_3.

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Mirjalili, Seyedali. "Genetic Algorithm". W Studies in Computational Intelligence, 43–55. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93025-1_4.

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Agbinya, Johnson I. "Genetic Algorithm". W Applied Data Analytics - Principles and Applications, 75–91. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-5.

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Okwu, Modestus O., i Lagouge K. Tartibu. "Genetic Algorithm". W Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications, 125–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61111-8_13.

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Badar, Altaf Q. H. "Genetic Algorithm". W Evolutionary Optimization Algorithms, 29–70. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-3.

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Kubat, Miroslav. "Genetic Algorithm". W An Introduction to Machine Learning, 429–49. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81935-4_21.

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Shukla, Anupam, Ritu Tiwari i Rahul Kala. "Genetic Algorithm". W Towards Hybrid and Adaptive Computing, 59–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14344-1_3.

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Streszczenia konferencji na temat "Genetic algorithm"

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Neville, Melvin, i Anaika Sibley. "Developing a generic genetic algorithm". W the 2002 annual ACM SIGAda international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/589451.589462.

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Jesus, Alexandre D., Arnaud Liefooghe, Bilel Derbel i Luís Paquete. "Algorithm selection of anytime algorithms". W 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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Misir, Mustafa, Stephanus Daniel Handoko i Hoong Chuin Lau. "Building algorithm portfolios for memetic algorithms". W 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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Lyons, Donald P., i Ken D. Kihm. "A New Tomographic Image Reconstruction Method: Using a Hybrid Genetic Algorithm". W 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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Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm". W ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/dac-14231.

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Abstract Nelder and Mead Simplex (NMS) algorithm is an effective nonlinear programming technique. Trabia and Lu (1999) recently presented a novel algorithm, Fuzzy Simplex (FS), which improved the efficiency of Nelder and Mead Simplex by using fuzzy logic to determine the orientation and size of the simplex. While Fuzzy Simplex algorithm can be successfully used to search a wide variety of functions, it suffers, as other simplex algorithms, from its dependence on the initial guess and the original simplex size. This paper addresses this problem by combining the Fuzzy Simplex with Genetic Algorithm (GA) in a hybrid algorithm. Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The Hybrid GA Fuzzy Simplex algorithm generally results in a faster convergence.
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Averill, R. C., W. F. Punch, E. D. Goodman, S. C. Lin, Y. C. Yip i Y. Ding. "Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams". W ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0012.

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Abstract This paper describes a general approach to structural design using Genetic Algorithms, and an application of that approach to the design of energy absorbing laminated composite beams containing distributed thin, compliant layers. We first discuss a method for applying a Genetic Algorithm (GA) to structural design, using it as an evolutionary search optimizer in conjunction with a structural simulator as its objective function. The simulator used is an efficient and robust special purpose finite element model based on a layerwise laminate theory. The GA “designs” the beam by selecting material assignments for the subregions and the locations of compliant layers, and evaluates the design using the simulator. The efficiency of the GA search is improved by use of the “injection island” architecture. The results demonstrate that the parallel GA architectures achieved algorithmic superlinear speedup to similar quality of solution in comparison with single-population genetic algorithms.
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Kudova, Petra. "Clustering Genetic Algorithm". W 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.4312873.

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Kudova, Petra. "Clustering Genetic Algorithm". W 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.65.

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Torchinskii, V. E., O. S. Logunova, N. S. Sibileva i P. Yu Romanov. "Genetic algorithm modification". W ICISS '18: 2018 International Conference on Information Science and System. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3209914.3209928.

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Rowe, Jonathan E. "Genetic algorithm theory". W the fourteenth international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2330923.

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Raporty organizacyjne na temat "Genetic algorithm"

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), luty 2018. http://dx.doi.org/10.2172/1419729.

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Arthur, Jennifer Ann. Genetic algorithm for nuclear data evaluation. Office of Scientific and Technical Information (OSTI), czerwiec 2018. http://dx.doi.org/10.2172/1441274.

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Gazonas, George A., Daniel S. Weile, Raymond Wildman i Anuraag Mohan. Genetic Algorithm Optimization of Phononic Bandgap Structures. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2006. http://dx.doi.org/10.21236/ada456655.

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Van Zandt, James R. A Genetic Algorithm for Search Route Planning. Fort Belvoir, VA: Defense Technical Information Center, lipiec 1992. http://dx.doi.org/10.21236/ada254894.

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Toskova, Asya, Borislav Toshkov, Stanimir Stoyanov i Ivan Popchev. Genetic Algorithm for a Learning Humanoid Robot. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, sierpień 2019. http://dx.doi.org/10.7546/crabs.2019.08.13.

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Ghosh, Payel. Medical Image Segmentation Using a Genetic Algorithm. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.25.

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Fellman, Laura. The Genetic Algorithm and Maximum Entropy Dice. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.7120.

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Levine, D., P. Hallstrom, D. Noelle i B. Walenz. Experiences with the PGAPack Parallel Genetic Algorithm library. Office of Scientific and Technical Information (OSTI), lipiec 1997. http://dx.doi.org/10.2172/525039.

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Vega, Richard Manuel, i Parma, Edward J.,. GenSpec: A Genetic Algorithm for Neutron Energy Spectrum Adjustment. Office of Scientific and Technical Information (OSTI), grudzień 2015. http://dx.doi.org/10.2172/1494341.

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Levine, D. Users guide to the PGAPack parallel genetic algorithm library. Office of Scientific and Technical Information (OSTI), styczeń 1996. http://dx.doi.org/10.2172/366458.

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