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Academic literature on the topic 'System of genetic algorithm'

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Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "System of genetic algorithm"

RAJALAKSHMI.M, RAJALAKSHMI M. "Software System Re-Modularization using Interactive Genetic Algorithm." Paripex - Indian Journal Of Research 3, no. 4 (January 15, 2012): 105–7. http://dx.doi.org/10.15373/22501991/apr2014/32.

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Vyas, Sanjay R., and Dr Ved Vyas Dwivedi. "Genetic Algorithm for Plant Generation Schedule in Electrical Power System." Paripex - Indian Journal Of Research 2, no. 1 (January 15, 2012): 52–53. http://dx.doi.org/10.15373/22501991/jan2013/19.

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Nabil, Emad, Amr Badr, and Ibrahim Farag. "An Immuno-Genetic Hybrid Algorithm." International Journal of Computers Communications & Control 4, no. 4 (December 1, 2009): 374. http://dx.doi.org/10.15837/ijccc.2009.4.2454.

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The construction of artificial systems by drawing inspiration from natural systems is not a new idea. The Artificial Neural Network (ANN) and Genetic Algorithms (GAs) are good examples of successful applications of the biological metaphor to the solution of computational problems. The study of artificial immune systems is a relatively new field that tries to exploit the mechanisms of the natural immune system (NIS) in order to develop problem- solving techniques. In this research, we have combined the artificial immune system with the genetic algorithms in one hybrid algorithm. We proposed a modification to the clonal selection algorithm, which is inspired from the clonal selection principle and affinity maturation of the human immune responses, by hybridizing it with the crossover operator, which is imported from GAs to increase the exploration of the search space. We also introduced the adaptability of the mutation rates by applying a degrading function so that the mutation rates decrease with time where the affinity of the population increases, the hybrid algorithm used for evolving a fuzzy rule system to solve the wellknown Wisconsin Breast Cancer Diagnosis problem (WBCD). Our evolved system exhibits two important characteristics; first, it attains high classification performance, with the possibility of attributing a confidence measure to the output diagnosis; second, the system has a simple fuzzy rule system; therefore, it is human interpretable. The hybrid algorithm overcomes both the GAs and the AIS, so that it reached the classification ratio 97.36, by only one rule, in the earlier generations than the two other algorithms. The learning and memory acquisition of our algorithm was verified through its application to a binary character recognition problem. The hybrid algorithm overcomes also GAs and AIS and reached the convergence point before them.

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Noshadi, Tayebe, Marzieh Dadvar, Nastaran Mirza, and Shima Shamseddini. "Adjust genetic algorithm parameter by fuzzy system." Ciência e Natura 37 (December 19, 2015): 190. http://dx.doi.org/10.5902/2179460x20771.

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Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

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Li, Xiaocong, Zhanying Wang, Junhua Xu, and Baochao Chen. "Power System Stabilizer Parameters Designing Based on Genetic Simulated Annealing Algorithm." Journal of Clean Energy Technologies 4, no. 3 (2015): 178–82. http://dx.doi.org/10.7763/jocet.2016.v4.275.

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Ohn, Syng-Yup, and Seung-Do Chi. "Cancer Diagnosis System using Genetic Algorithm and Multi-boosting Classifier." Journal of the Korea Society for Simulation 20, no. 2 (June 30, 2011): 77–85. http://dx.doi.org/10.9709/jkss.2011.20.2.077.

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ZHUANG, Jian, Qing-Yu YANG, Hai-Feng DU, and De-Hong YU. "High Efficient Complex System Genetic Algorithm." Journal of Software 21, no. 11 (January 28, 2011): 2790–801. http://dx.doi.org/10.3724/sp.j.1001.2010.03673.

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Zahradníková, Barbora, Soňa Duchovičová, and Peter Schreiber. "Facial Composite System Using Genetic Algorithm." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 22, no. 341 (December 1, 2014): 47–51. http://dx.doi.org/10.2478/rput-2014-0007.

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Abstract The article deals with genetic algorithms and their application in face identification. The purpose of the research is to develop a free and open-source facial composite system using evolutionary algorithms, primarily processes of selection and breeding. The initial testing proved higher quality of the final composites and massive reduction in the composites processing time. System requirements were specified and future research orientation was proposed in order to improve the results.

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Tam, S. "Genetic algorithm based defect identification system." Expert Systems with Applications 18, no. 1 (January 2000): 17–25. http://dx.doi.org/10.1016/s0957-4174(99)00046-9.

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Sukhija, Pratibha, Sunny Behal, and Pritpal Singh. "Face Recognition System Using Genetic Algorithm." Procedia Computer Science 85 (2016): 410–17. http://dx.doi.org/10.1016/j.procs.2016.05.183.

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Dissertations / Theses on the topic "System of genetic algorithm"

Ma, Jiya. "A Genetic Algorithm for Solar Boat." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.

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Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production

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Rivas-Davalos, Francisco. "A genetic algorithm for power distribution system planning." Thesis, Brunel University, 2004. http://bura.brunel.ac.uk/handle/2438/7891.

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The planning of distribution systems consists in determining the optimum site and size of new substations and feeders in order to satisfy the future power demand with minimum investment and operational costs and an acceptable level of reliability. This problem is a combinatorial, non-linear and constrained optimization problem. Several solution methods based on genetic algorithms have been reported in the literature; however, some of these methods have been reported with applications to small systems while others have long solution time. In addition, the vast majority of the developed methods handle planning problems simplifying them as single-objective problems but, there are some planning aspects that can not be combined into a single scalar objective; therefore, they require to be treated separately. The cause of these shortcomings is the poor representation of the potential solutions and their genetic operators This thesis presents the design of a genetic algorithm using a direct representation technique and specialized genetic operators for power distribution system expansion planning problems. These operators effectively preserve and exploit critical configurations that contribute to the optimization of the objective function. The constraints of the problems are efficiently handle with new strategies. The genetic algorithm was tested on several theoretical and real large-scale power distribution systems. Problems of network reconfiguration for loss reduction were also included in order to show the potential of the algorithm to resolve operational problems. Both single-objective and multi-objective formulations were considered in the tests. The results were compared with results from other heuristic methods such as ant colony system algorithms, evolutionary programming, differential evolution and other genetic algorithms reported in the literature. From these comparisons it was concluded that the proposed genetic algorithm is suitable to resolve problems of largescale power distribution system planning. Moreover, the algorithm proved to be effective, efficient and robust with better performance than other previous methods.

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Muforo, Remigius I. "Automatedgeneration of fuzzy control system using genetic algorithm." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1995. http://digitalcommons.auctr.edu/dissertations/3694.

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Fuzzy logic based controllers have emerged to be an inexpensive and simple solution for complex control problems. The main components of the fuzzy logic control are the rule base, the membership functions, and the inference engine. Membership functions are used to combine the antecedents and consequent (of the rules) to determine the output of the rules. Fuzzy controllers, however, suffer from significant drawbacks such as the formulation of the membership functions and tuning the rule base. In this thesis, a genetic algorithm is used to generate the fuzzy logic controller's rule base and to tune the membership functions. Temperature and pressure control in a boiler plant is used as a test bed application.

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Guc, Gercek. "Optimization Of Water Distribution Networks Using Genetic Algorithm." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607192/index.pdf.

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This study gives a description about the development of a computer model, RealPipe, which relates genetic algorithm (GA) to the well known problem of least-cost design of water distribution network. GA methodology is an evolutionary process, basically imitating evolution process of nature. GA is essentially an efficient search method basically for nonlinear optimization cases. The genetic operations take place within the population of chromosomes. By means of various operators, the genetic knowledge in chromosomes change continuously and the success of the population progressively increases as a result of these operations. GA optimization is also well suited for optimization of water distribution systems, especially large and complex systems. The primary objective of this study is optimization of a water distribution network by GA. GA operations are realized on a special program developed by the author called RealPipe. RealPipe optimizes given water network distribution systems by considering capital cost of pipes only. Five operators are involved in the program algorithm. These operators are generation, selection, elitism, crossover and mutation. Optimum population size is found to be between 30-70 depending on the size of the network (i.e. pipe number) and number of commercially available pipe size. Elitism rate should be around 10 percent. Mutation rate should be selected around 1-5 percent depending again on the size of the network. Multipoint crossover and higher rates are advisable. Also pressure penalty parameters are found to be much important than velocity parameters. Below pressure penalty parameter is the most important one and should be roughly 100 times higher than the other. Two known networks of the literature are examined using RealPipe and expected results are achieved. N8.3 network which is located in the northern side of Ankara is the case study. Total cost achieved by RealPipe is 16.74 percent lower than the cost of the existing network
it should be noted that the solution provided by RealPipe is hydraulically improved.

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Tsang, Yiu-ming. "Intelligent polishing using fuzzy logic and genetic algorithm." View the Table of Contents & Abstract, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37206400.

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Tsang, Yiu-ming, and 曾耀明. "Intelligent polishing using fuzzy logic and genetic algorithm." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B38589291.

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Anderson, Roger J. "Characterization of Performance, Robustness, and Behavior Relationships in a Directly Connected Material Handling System." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/26967.

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In the design of material handling systems with complex and unpredictable dynamics, conventional search and optimization approaches that are based only on performance measures offer little guarantee of robustness. Using evidence from research into complex systems, the use of behavior-based optimization is proposed, which takes advantage of observed relationships between complexity and optimality with respect to both performance and robustness. Based on theoretical complexity measures, particularly algorithmic complexity, several simple complexity measures are created. The relationships between these measures and both performance and robustness are examined, using a model of a directly connected material handling system as a backdrop. The fundamental causes of the relationships and their applicability in the proposed behavior-based optimization approach are discussed.
Ph. D.

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Nguyen, Quoc Tuan. "Using the genetic algorithm to optimize Web search: Lessons from biology." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27160.

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Searching for information on the Web is a relatively inefficient process. My goal is to develop a method that optimizes web search queries without user intervention. Developing intelligent ways to automate this process includes the development of algorithms that automatically manipulate the use of keywords to produce the desired output. Genetic algorithms (GA) provide a potentially useful approach in this area. However, these approaches have not fully exploited the biological concepts associated with genetic reproduction and evolution. I hypothesize that an approach that uses GA but modifies it to include the biological concepts of structural and regulatory gene types and the use of a combination of deletion operator and silent genes will improve GA performance in optimizing Web search. In this paper, I describe this approach and its implementation in simulations of Web search tasks using three popular Web search engines (Google, Yahoo and Netscape). The results of this implementation are presented and are compared to the performance of a similar, but unmodified GA in the same tasks. (Abstract shortened by UMI.)

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Singh, Ravindra. "A Novel Approach for Tuning of Power System Stabilizer Using Genetic Algorithm." Thesis, Indian Institute of Science, 2004. http://hdl.handle.net/2005/65.

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The problem of dynamic stability of power system has challenged power system engineers since over three decades now. In a generator, the electromechanical coupling between the rotor and the rest of the system causes it to behave in a manner similar to a spring mass damper system, which exhibits an oscillatory behaviour around the equilibrium state, following any disturbance, such as sudden change in loads, change in transmission line parameters, fluctuations in the output of turbine and faults etc. The use of fast acting high gain AVRs and evolution of large interconnected power systems with transfer of bulk power across weak transmission links have further aggravated the problem of low frequency oscillations. The oscillations, which are typically in the frequency range of 0.2 to 3.0 Hz, might be excited by the disturbances in the system or, in some cases, might even build up spontaneously. These oscillations limit the power transmission capability of a network and, sometimes, even cause a loss of synchronism and an eventual breakdown of the entire system. The application of Power System Stabilizer (PSS) can help in damping out these oscillations and improve the system stability. The traditional and till date the most popular solution to this problem is application of conventional power system stabilizer (CPSS). However, continual changes in the operating condition and network parameters result in corresponding change in system dynamics. This constantly changing nature of power system makes the design of CPSS a difficult task. Adaptive control methods have been applied to overcome this problem with some degree of success. However, the complications involved in implementing such controllers have restricted their practical usage. In recent years there has been a growing interest in robust stabilization and disturbance attenuation problem. H∞ control theory provides a powerful tool to deal with robust stabilization and disturbance attenuation problem. However the standard H∞ control theory does not guarantee robust performance under the presence of all the uncertainties in the power plants. This thesis provides a method for designing fixed parameter controller for system to ensure robustness under model uncertainties. Minimum performance required of PSS is decided a priori and achieved over the entire range of operating conditions. A new method has been proposed for tuning the parameters of a fixed gain power system stabilizer. The stabilizer places the troublesome system modes in an acceptable region in the complex plane and guarantees a robust performance over a wide range of operating conditions. Robust D-stability is taken as primary specification for design. Conventional lead/lag PSS structure is retained but its parameters are re-tuned using genetic algorithm (GA) to obtain enhanced performance. The advantage of GA technique for tuning the PSS parameters is that it is independent of the complexity of the performance index considered. It suffices to specify an appropriate objective function and to place finite bounds on the optimized parameters. The efficacy of the proposed method has been tested on single machine as well as multimachine systems. The proposed method of tuning the PSS is an attractive alternative to conventional fixed gain stabilizer design as it retains the simplicity of the conventional PSS and still guarantees a robust acceptable performance over a wide range of operating and system condition. The method suggested in this thesis can be used for designing robust power system stabilizers for guaranteeing the required closed loop performance over a prespecified range of operating and system conditions. The simplicity in design and implementation of the proposed stabilizers makes them better suited for practical applications in real plants.

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Nassif, Nabil. "Optimization of HVAC control system strategy using two-objective genetic algorithm." Mémoire, Montréal : École de technologie supérieure, 2005. http://wwwlib.umi.com/cr/etsmtl/fullcit?pNR03069.

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Thèse (Ph.D.)-- École de technologie supérieure, Montréal, 2005.
"Thesis presented to the École de technologie supérieure in partial fulfiliment [i.e. fulfillment] of the thesis requirement for the degree of philosophiae doctor in engineering". Bibliogr.: f. [178]-184. Également disponible en version électronique.

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Books on the topic "System of genetic algorithm"

Abu-alola, Abdulmohsin. Genetic algorithms for intelligent control system design. Wolverhampton: University of Wolverhampton, 1995.

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Bloch, Tobias A. Storage and pumping system optimization using genetic algorithms. Ottawa: National Library of Canada, 1999.

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Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Karr, C. L. An adaptive system for process control. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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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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Ajlouni, Naim. Genetic algorithms for control systems design. Salford: University of Salford, 1995.

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Fleming, P. J. Genetic algorithms in control systems engineering. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1993.

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Islam, Rafiqul. Genetic algorithms, fuzzy systems, and website classification. Hauppauge, N.Y: Nova Science Publishers, 2011.

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Tao, Jili, Ridong Zhang, and 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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Sara, Silva, Krawiec Krzysztof, Machado Penousal, Cotta Carlos, and SpringerLink (Online service), eds. Genetic Programming: 15th European Conference, EuroGP 2012, Málaga, Spain, April 11-13, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "System of genetic algorithm"

Čepin, Marko. "Genetic Algorithm." In 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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Nariman-zadeh, Nader, and Jamali Ali. "Hybrid Genetic Algorithm and GMDH System." In Hybrid Self-Organizing Modeling Systems, 99–138. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01530-4_3.

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Jiang, Hua, and Lishan Kang. "Building Trade System by Genetic Algorithm." In Advances in Computation and Intelligence, 18–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04843-2_3.

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Tymerski, Richard, Ethan Ott, and Garrison Greenwood. "Genetic Algorithm Based Trading System Design." In Lecture Notes in Computer Science, 360–73. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28270-1_30.

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Wang, Po-Kai, Chao-Fu Hong, and Min-Huei Lin. "Interactive Genetic Algorithm Joining Recommender System." In Intelligent Information and Database Systems, 40–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14802-7_4.

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Leupers, Rainer. "Genetic Algorithm Based DSP Code Optimization." In Evolutionary Algorithms for Embedded System Design, 35–62. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1035-2_2.

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Tao, Jili, Ridong Zhang, and Yong Zhu. "GA-Based RBF Neural Network for Nonlinear SISO System." In DNA Computing Based Genetic Algorithm, 119–66. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2_6.

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Tao, Jili, Ridong Zhang, and Yong Zhu. "GA Based Fuzzy Neural Network Modeling for Nonlinear SISO System." In DNA Computing Based Genetic Algorithm, 167–91. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5403-2_7.

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Karthikeyan, P., and P. Priyadharshini. "Personalized Route Finding System Using Genetic Algorithm." In Advances in Intelligent Systems and Computing, 383–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6981-8_31.

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Paxton, John, and John Evans. "Two Genetic Algorithm Enhancements." In Intelligent Systems Third Golden West International Conference, 451–56. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-7108-3_46.

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Conference papers on the topic "System of genetic algorithm"

Torchinskii, V. E., O. S. Logunova, N. S. Sibileva, and P. Yu Romanov. "Genetic algorithm modification." In 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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Marius, Olteanu, Paraschiv Nicolae, Koprinkova Petia, and Todorov Yancho. "Genetic algorithm for system modelling." In 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). IEEE, 2017. http://dx.doi.org/10.1109/ecai.2017.8166517.

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Caponetto, R. C. "Chaotic system identification via genetic algorithm." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951044.

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Karatas, Gozde. "Genetic algorithm for intrusion detection system." In 2016 24th Signal Processing and Communication Application Conference (SIU). IEEE, 2016. http://dx.doi.org/10.1109/siu.2016.7495996.

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Bozzo, L. Manuel, P. Martin Gutierrez, and L. Tomas Bozzo. "Genetic Algorithm Based Voice Imitation System." In 2010 XXIX International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2010. http://dx.doi.org/10.1109/sccc.2010.48.

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Liu, Yi Ling, and Fernando Gomide. "Genetic participatory algorithm and system modeling." In Proceeding of the fifteenth annual conference companion. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2482753.

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Karaboga, Nurhan, and Bahadir Cetinkaya. "Genetic Algorithm Based Adaptive System Identification." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298810.

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Norizan, Nurhidayatul Ain Mohd, Shakirah Mohd Taib, and Nordin Zakaria. "E-internship system with genetic algorithm." In 2017 IEEE Conference on e-Learning, e-Management and e-Services (IC3e). IEEE, 2017. http://dx.doi.org/10.1109/ic3e.2017.8409236.

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Hao, Guo-Sheng, Yong-Qing Huang, Jun-Rong Yan, Jun-Huai Lu, and Gu Gong. "System analysis of interactive genetic algorithm." In 2008 Chinese Control and Decision Conference (CCDC). IEEE, 2008. http://dx.doi.org/10.1109/ccdc.2008.4598317.

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Benaicha, Salah Eddine, Lalia Saoudi, Salah Eddine Bouhouita Guermeche, and Ouarda Lounis. "Intrusion detection system using genetic algorithm." In 2014 Science and Information Conference (SAI). IEEE, 2014. http://dx.doi.org/10.1109/sai.2014.6918242.

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Reports on the topic "System of genetic algorithm"

Davis, Lawrence, Betsy Constantine, Stuart Shieber, Joe Marks, and Rebecca Hwa. Optimizing Cockpit Display Configurations with a Genetic Algorithm System. Phase 1. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada289799.

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Cestaro, Ronald, and Jack Howard. Genetic Algorithm-Based System Design and Photonics-Based Receiver Technologies Program SETA Support. Fort Belvoir, VA: Defense Technical Information Center, March 2005. http://dx.doi.org/10.21236/ada432273.

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Chuang, Feng-Chuan. Global structual optimizations of surface systems with a genetic algorithm. Office of Scientific and Technical Information (OSTI), January 2005. http://dx.doi.org/10.2172/850036.

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Goldman, Geoffrey H. Preliminary Study of a Hybrid Genetic Algorithm/Expert System for Modeling Complex Radar Signatures. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada371056.

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

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

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

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

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

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

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