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Relevant bibliographies by topics / Genetic algorithm

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Academic literature on the topic 'Genetic algorithm'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Genetic algorithm"

1

Kallab, Chadi, Samir Haddad, and Jinane Sayah. "Flexible Traceable Generic Genetic Algorithm." Open Journal of Applied Sciences 12, no. 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.

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Neville, Melvin, and Anaika Sibley. "Developing a generic genetic algorithm." ACM SIGAda Ada Letters XXIII, no. 1 (2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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

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

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Chouh, M., and K. Boukhetala. "Semi-nonnegative Matrix Factorization Algorithm Based on Genetic Algorithm Initialization." International Journal of Machine Learning and Computing 6, no. 4 (2016): 231·—234. http://dx.doi.org/10.18178/ijmlc.2016.6.4.603.

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Kandeeban, Selvakani S., and R. S. Rajesh. "Desegregated ID Execution Using Genetic Algorithm." International Journal of Engineering and Technology 1, no. 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, no. 4 (1991): 626–38. http://dx.doi.org/10.3156/jfuzzy.3.4_2.

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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, no. 1 (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

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Aivaliotis-Apostolopoulos, Panagiotis, and Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization." PLOS ONE 17, no. 9 (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

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Anfyorov, M. A. "Genetic clustering algorithm." Russian Technological Journal 7, no. 6 (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 expa

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Kanwal, Maxinder S., Avinash S. Ramesh, and Lauren A. Huang. "A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms." F1000Research 2 (November 19, 2013): 139. http://dx.doi.org/10.12688/f1000research.2-139.v2.

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Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderiva

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Dissertations / Theses on the topic "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 i

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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 Gene

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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

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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

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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 t

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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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Books on the topic "Genetic algorithm"

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

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

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

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

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

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Karr, C. L. Genetic algorithm applied to least squares curve fitting. 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. University of Manchester, Department of Computer Science, 1995.

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

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

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Kothari, Ravi. A competitive genetic algorithm for single row facility layout. Indian Institute of Management, 2012.

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Book chapters on the topic "Genetic algorithm"

1

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

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Brabazon, Anthony, Michael O’Neill, and Seán McGarraghy. "Genetic Algorithm." In Natural Computing Algorithms. 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." In Interdisciplinary Computing in Java Programming. 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, and Hasan Erdal. "Genetic Algorithm." In Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32900-5_3.

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Mirjalili, Seyedali. "Genetic Algorithm." In Studies in Computational Intelligence. 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." In Applied Data Analytics - Principles and Applications. River Publishers, 2022. http://dx.doi.org/10.1201/9781003337225-5.

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Şen, Zekâi. "Genetic Algorithm." In Shallow and Deep Learning Principles. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29555-3_6.

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Okwu, Modestus O., and Lagouge K. Tartibu. "Genetic Algorithm." In Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. 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." In Evolutionary Optimization Algorithms. CRC Press, 2021. http://dx.doi.org/10.1201/9781003206477-3.

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

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Conference papers on the topic "Genetic algorithm"

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

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

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

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

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

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Trabia, Mohamed B. "A Hybrid Fuzzy Simplex Genetic Algorithm." In 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 Algori

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Averill, R. C., W. F. Punch, E. D. Goodman, S. C. Lin, Y. C. Yip, and Y. Ding. "Genetic Algorithm-Based Design of Energy Absorbing Laminated Composite Beams." In 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

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Chapman, Colin D., Kazuhiro Saitou, and Mark J. Jakiela. "Genetic Algorithms As an Approach to Configuration and Topology Design." In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0338.

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

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Kudova, Petra. "Clustering Genetic Algorithm." In 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." In 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, and P. Yu Romanov. "Genetic algorithm modification." In ICISS '18: 2018 International Conference on Information Science and System. ACM, 2018. http://dx.doi.org/10.1145/3209914.3209928.

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Reports on the topic "Genetic algorithm"

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

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

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

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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, 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, 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, 2000. http://dx.doi.org/10.15760/etd.7120.

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

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Vega, Richard Manuel, and Parma, Edward J.,. GenSpec: A Genetic Algorithm for Neutron Energy Spectrum Adjustment. Office of Scientific and Technical Information (OSTI), 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), 1996. http://dx.doi.org/10.2172/366458.

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