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Thematische Bibliographien / Genetic algorithms / Dissertationen

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Dissertationen zum Thema „Genetic algorithms“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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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 aproxim

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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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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,

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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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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 g

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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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11

Morrison, Jason. "Co-evolution and genetic algorithms." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0027/MQ26973.pdf.

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12

Lankhorst, Marc Martijn. "Genetic algorithms in data analysis." [S.l. : [Groningen] : s.n.] ; [University Library Groningen] [Host], 1996. http://irs.ub.rug.nl/ppn/142964662.

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13

Fang, Xiaopeng. "Engineering design using genetic algorithms." [Ames, Iowa : Iowa State University], 2007.

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14

Seabrook, Graham. "Evolving Robofish using Genetic Algorithms /." Leeds : University of Leeds, School of Computer Studies, 2008. http://www.comp.leeds.ac.uk/fyproj/reports/0708/Seabrook.pdf.

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15

Shen, Gang. "Shadow Price Guided Genetic Algorithms." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/64.

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The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure s

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16

何淑瑩 and Shuk-ying Ho. "Knowledge representation with genetic algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31222638.

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17

Roberts, Christopher. "Genetic algorithms for cluster optimization." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368792.

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18

Pisitpaibool, Chaisak. "Genetic algorithms for holistic design." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.405087.

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19

Mason, Andrew J. "Genetic algorithms and scheduling problems." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335134.

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20

Appelo, Sophia Aletta. "Structural optimisation via genetic algorithms." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/71907.

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Thesis (MScEng)--Stellenbosch University, 2012.<br>ENGLISH ABSTRACT: The design of steel structures needs to incorporate some optimisation procedure that evolves the initial design into a more economic nal design, where this nal design must still satisfy all the initial design criteria. A candidate optimisation technique suggested by this research is the genetic algorithm. The genetic algorithm (GA) is an optimisation technique that was inspired by evolutionary principles, such as the survival of the ttest (also known as natural selection). The GA operates by generating a population of

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21

Xu, Hu. "Solver tuning with genetic algorithms." Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/eee8f5d9-ede2-4b87-af77-8142b2d0209c.

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Currently the parameters in a constraint solver are often selected by hand by experts in the field; these parameters might include the level of preprocessing to be used, the variable ordering heuristic or the suitable modelling approach. The efficient and automatic mechanism of parameters tuning for a constraint solver is a step towards making constraint programming a more widely accessible technology. Two types of tuning algorithms are discussed in this thesis: single instance tuning algorithms and instance-based tuning algorithms. A standard genetic based algorithm and a sexual genetic based

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22

Lochtefeld, Darrell F. "Multi-objectivization in Genetic Algorithms." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1308765665.

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23

Morrison, Jason (Jason Boyd) Carleton University Dissertation Computer Science. "Co-evolution and genetic algorithms." Ottawa, 1997.

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24

Ho, Shuk-ying. "Knowledge representation with genetic algorithms /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22030256.

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25

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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26

Eriksson, Daniel. "Algorithmic Design of Graphical Resources for Games Using Genetic Algorithms." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139332.

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Producing many varying instances of the same type of graphical resource for games can be of interest, such as trees or foliage. But when randomly generating graphical resources, you can often end up with many similar looking results or perhaps results that doesn't look like what it is meant to look like. This work investigates whether genetic algorithms can be applied to produce greater varying results when generating graphical resources by basing the fitness of each individual for each genetic generation on how similar the graphical resource is to previously generated resources. This work con

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27

Flaten, Erlend. "Using genetic algorithms for improving segmentation." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9201.

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<p>Segmentation is one of the core fields in image processing, and the first difficult step in processing and understanding images. There are dozens of different segmentation algorithm, but all these algorithms have some kind of “Achilles’ heal”, or may be limited to one or a few domains . This paper presents a possible solution to avoid problems with single segmentation algorithms by making it possible to use several algorithms. The algorithms can be used separately or in sequences of algorithms. Finally, an application is developed to give some insight into this new way of segmenting.</p>

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28

Amouzgar, Kaveh. "Multi-objective optimization using Genetic Algorithms." Thesis, Högskolan i Jönköping, Tekniska Högskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-19851.

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In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (GA) are reviewed. Two algorithms, one for single objective and the other for multi-objective problems, which are believed to be more efficient are described in details. The algorithms are coded with MATLAB and applied on several test functions. The results are compared with the existing solutions in literatures and shows promising results. Obtained pareto-fronts are exactly similar to the true pareto-fronts with a good spread of solution throughout the optimal region. Constraint handling techniq

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29

Czarn, Andrew Simon Timothy. "Statistical exploratory analysis of genetic algorithms." University of Western Australia. School of Computer Science and Software Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0030.

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[Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the in

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30

Kim, Jeongwook. "Genetic algorithms for smart embedded systems." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13886.

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31

Carlson, Susan Elizabeth. "Component selection optimization using genetic algorithms." Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17886.

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32

Altman, Erik R. (Erik Richter). "Genetic algorithms and cache replacement policy." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61096.

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The most common and generally best performing replacement algorithm in modern caches is LRU. Despite LRU's superiority, it is still possible that other feasible and implementable replacement policies could yield better performance. (34) found that an optimal replacement policy (OPT) would often have a miss rate 70% that of LRU.<br>If better replacement policies exist, they may not be obvious. One way to find better policies is to study a large number of address traces for common patterns. Such an undertaking involves such a large amount of data, that some automated method of generating and eva

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33

Iacoviello, Vincenzo. "Genetic algorithms and decision feedback filters." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=69599.

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A decision feedback (DFB) equalizer is used to correct for the effects of inter-symbol interference in digital communications systems. The order of the DFB filter is reduced to a bare minimum and studied when it is insufficient to equalize the channel, i.e., when the filter does not have enough poles to cancel all the zeroes of the channel. The error surfaces produced by the DFB filter in the symbol-by-symbol, frame-by-frame, and aggregate sense are investigated. A genetic algorithm is then applied to the problem of adapting the DFB filter coefficients. The performance of the genetic algorithm

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34

Anderson, Jon K. "Genetic algorithms applied to graph theory." Virtual Press, 1999. http://liblink.bsu.edu/uhtbin/catkey/1136714.

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This thesis proposes two new variations on the genetic algorithm. The first attempts to improve clustering problems by optimizing the structure of a genetic string dynamically during the run of the algorithm. This is done by using a permutation on the allele which is inherited by the next generation. The second is a multiple pool technique which ensures continuing convergence by maintaining unique lineages and merging pools of similar age. These variations will be tested against two well-known graph theory problems, the Traveling Salesman Problem and the Maximum Clique Problem. The results wil

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35

Graham, Ian J. "Genetic algorithms for evolutionary product design." Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/6900.

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This thesis describes research into the development of a Computer Aided Design (CAD) tool that uses a Genetic Algorithm (GA) to generate and evolve original design concepts through human interaction. CAD technologies are firmly established in the later stages of design, and include many applications of Evolutionary Algorithms (EAs). The use of EAs as generative and search tools for conceptual design is less evident in fields other than abstract art, architecture and styling. This research gains its originality in aiming to assist designers early in the design process, by creating and evolving

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36

Lancaster, John. "Project schedule optimisation utilising genetic algorithms." Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/5798.

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This thesis extends the body of research into the application of Genetic Algorithms to the Project Scheduling Problem (PSP). A thorough literature review is conducted in this area as well as in the application of other similar meta-heuristics. The review extends previous similar reviews to include PSP utilizing the Design Structure Matrix (DSM), as well as incorporating recent developments. There is a need within industry for optimisation algorithms that can assist in the identification of optimal schedules when presented with a network that can present a number of possible alternatives. The o

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37

Ajlouni, Naim. "Genetic algorithms for control system design." Thesis, University of Salford, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308088.

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38

Hatzikos, Vasilis E. "Genetic algorithms into iterative learning control." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408314.

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39

Myers, Richard Oliver. "Genetic algorithms for ambiguous labelling problems." Thesis, University of York, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310985.

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40

Basanta, David. "Using genetic algorithms to evolve microstructures." Thesis, King's College London (University of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421869.

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41

CARBONO, ALONSO JOAQUIN JUVINAO. "MOORING PATTERN OPTIMIZATION USING GENETIC ALGORITHMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2005. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8242@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>GRUPO DE TECNOLOGIA DE COMPUTAÇÃO GRÁFICA - PUC-RIO<br>Com o crescimento da demanda de óleo, as empresas de petróleo têm sido forçadas a explorar novas reservas em águas cada vez mais profundas. Em função do alto custo das operações de exploração de petróleo, torna-se necessário o desenvolvimento de tecnologias capazes de aumentar a eficiência e reduzir os custos envolvidos. Neste contexto, a utilização de unidades flutuantes torna-se cada vez mais freqüente em águas profundas. O posicionamento das unidades flutuantes durant

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42

Bourne, Juliet C. (Juliet Cassandra). "Setting language parameters using genetic algorithms." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/109629.

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43

Viswanathan, Lakshmi. "Genetic algorithms for uncapacitated network design." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36022.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 124).<br>by Viswanathan Lakshmi.<br>M.S.

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44

Shaw, Nicholas Robert. "MRI magnet design using genetic algorithms." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619968.

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45

Medlar, A. J. "Manycore algorithms for genetic linkage analysis." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1366896/.

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Exact algorithms to perform linkage analysis scale exponentially with the size of the input. Beyond a critical point, the amount of work that needs to be done exceeds both available time and memory. In these circumstances, we are forced to either abbreviate the input in some manner or else use an approximation. Approximate methods, like Markov chain Monte Carlo (MCMC), though they make the problem tractable, can take an immense amount of time to converge. The problem of high convergence time is compounded by software which is single-threaded and, as computer processors are manufactured with in

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46

Fang, Hsiao-Lan. "Genetic algorithms in timetabling and scheduling." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/30185.

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This thesis investigates the use of genetic algorithms (GAs) for solving a range of timetabling and scheduling problems. Such problems are very hard in general, and GAs offer a useful and successful alternative to existing techniques. A framework is presented for GAs to solve modular timetabling problems in educational institutions. The approach involves three components: declaring problem-specific constraints, constructing a problem-specific evaluation function and using a problem-independent GA to attempt to solve the problem. Successful results are demonstrated and a general analysis of the

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47

Rogers, Alex. "Modelling genetic algorithms and evolving populations." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/261289/.

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A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics, originally due to Pr¨ugel-Bennett and Shapiro, is extended to ranking selection, a form of selection commonly used in the genetic algorithm community. The extension allows a reduction in the number of macroscopic variables required to model the mean behaviour of the genetic algorithm. This reduction allows a more qualitative understanding of the dynamics to be developed without sacrificing quantitative accuracy. The work is extended beyond modelling the dynamics of the genetic algorithm. A cari

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48

Lovestead, Raymond L. "Helical Antenna Optimization Using Genetic Algorithms." Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/35295.

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The genetic algorithm (GA) is used to design helical antennas that provide a significantly larger bandwidth than conventional helices with the same size. Over the bandwidth of operation, the GA-optimized helix offers considerably smaller axial-ratio and slightly higher gain than the conventional helix. Also, the input resistance remains relatively constant over the bandwidth. On the other hand, for nearly the same bandwidth and gain, the GA-optimized helix offers a size reduction of 2:1 relative to the conventional helix. The optimization is achieved by allowing the genetic algorithm to contro

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49

MUPPIDI, SRINIVAS REDDY. "GENETIC ALGORITHMS FOR MULTI-OBJECTIVE PARTITIONING." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1080827924.

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50

Delibasis, Konstantinos K. "Genetic algorithms for medical image analysis." Thesis, University of Aberdeen, 1995. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU068560.

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This work attempts the formulation of a number of computer vision problems, often emerging when processing medical images, as optimisation problems. The ability of Genetic Algorithms, a global optimisation technique, to efficiently and reliably perform the required optimisations is assessed. Results are quantified and compared to other more established methods. The problem of anatomical object detection and extraction of their shape from 3D medical images is considered first. Two geometric primitives with parametrically controllable shape are used for geometric modelling and an adequate object

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