Dissertationen zum Thema „Genetic algorithms“
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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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Aguiar, Marilton Sanchotene de. „Análise formal da complexidade de algoritmos genéticos“. reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 1998. http://hdl.handle.net/10183/25941.
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O objetivo do trabalho é estudar a viabilidade de tratar problemas de otimização, considerados intratáveis, através de Algoritmos Genéticos, desenvolvendo critérios para a avaliação qualitativa de um Algoritmo Genético. Dentro deste tema, abordam-se estudos sobre complexidade, classes de problemas, análise e desenvolvimento de algoritmos e Algoritmos Genéticos, este ultimo sendo objeto central do estudo. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproximativos e análise da complexidade. O fato de um problema ser teoricamente resolvível por um computador não é suficiente para o problema ser na prática resolvível. Um problema é denominado tratável se no pior caso possui um algoritmo razoavelmente eficiente. E um algoritmo é dito razoavelmente eficiente quando existe um polinômio p tal que para qualquer entrada de tamanho n o algoritmo termina com no máximo p(n) passos [SZW 84]. Já que um polinômio pode ser de ordem bem alta, então um algoritmo de complexidade polinomial pode ser muito ineficiente. Genéticos é que se pode encontrar soluções aproximadas de problemas de grande complexidade computacional mediante um processo de evolução simulada[LAG 96]. Como produto do estudo deste tema, é proposto um método de desenvolvimento de Algoritmos Genéticos com a consciência de qualidade, utilizando todo o estudo formal de tipos de problemas, desenvolvimento de algoritmos aproximativos e análise da complexidade. Uma axiomatização tem o propósito de dar a semântica do algoritmo, ou seja, ela define, formalmente, o funcionamento do algoritmo, mais especificamente das funções e procedimentos do algoritmo. E isto, possibilita ao projetista de algoritmos uma maior segurança no desenvolvimento, porque para provar a correção de um Algoritmo Genético que satisfaça esse modelo só é necessário provar que os procedimentos satisfazem os axiomas. Para ter-se consciência da qualidade de um algoritmo aproximativo, dois fatores são relevantes: a exatidão e a complexidade. Este trabalho levanta os pontos importantes para o estudo da complexidade de um Algoritmo Genético. Infelizmente, são fatores conflitantes, pois quanto maior a exatidão, pior ( mais alta) é a complexidade, e vice-versa. Assim, um estudo da qualidade de um Algoritmo Genético, considerado um algoritmo aproximativo, só estaria completa com a consideração destes dois fatores. Mas, este trabalho proporciona um grande passo em direção do estudo da viabilidade do tratamento de problemas de otimização via Algoritmos Genéticos.The objective of the work is to study the viability of treating optimization problems, considered intractable, through Genetic Algorithms, developing approaches for the qualitative evaluation of a Genetic Algorithm. Inside this theme, approached areas: complexity, classes of problems, analysis and development of algorithms and Genetic Algorithms, this last one being central object of the study. As product of the study of this theme, a development method of Genetic Algorithms is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The fact that a problem theoretically solvable isn’t enough to mean that it is solvable in pratice. A problem is denominated easy if in the worst case it possesses an algorithm reasonably efficient. And an algorithm is said reasonably efficient when a polynomial p exists such that for any entrance size n the algorithm terminates at maximum of p(n) steps [SZW 84]. Since a polynomial can be of very high order, then an algorithm of polynomial complexity can be very inefficient. The premise of the Genetic Algorithms is that one can find approximate solutions of problems of great computational complexity by means of a process of simulated evolution [LAG 96]. As product of the study of this theme, a method of development of Genetic Algorithms with the quality conscience is proposed, using the whole formal study of types of problems, development of approximate algorithms and complexity analysis. The axiom set has the purpose of giving the semantics of the algorithm, in other words, it defines formally the operation of the algorithm, more specifically of the functions and procedures of the algorithm. And this, facilitates the planner of algorithms a larger safety in the development, because in order to prove the correction of a Genetic Algorithm that satisfies that model it is only necessary to prove that the procedures satisfy the axioms. To have conscience of the quality of an approximate algorithm, two factors are important: the accuracy and the complexity. This work lifts the important points for the study of the complexity of a Genetic Algorithm. Unhappily, they are conflicting factors, because as larger the accuracy, worse (higher) it is the complexity, and vice-versa. Thus, a study of the quality of a Genetic Algorithm, considered an approximate algorithm, would be only complete with the consideration of these two factors. But, this work provides a great step in direction of the study of the viability of the treatment of optimization problems through Genetic Algorithms.
3
Abu-Bakar, Nordin. „Adaptive genetic algorithms“. Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343268.
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.
5
Wagner, Stefan. „Looking inside genetic algorithms /“. Linz : Trauner, 2005. http://aleph.unisg.ch/hsgscan/hm00116856.pdf.
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, and developing suitable evolutionary operators) are known as Genetic Clustering Algorithms (GCAs). There are two parts to our investigation of GCAs: first we look at clustering into a given number of clusters. The performance of GCAs on three generated data sets, analysed using 4320 differing combinations of adaptions, establishes their efficacy. Choice of adaptions and parameter settings is data set dependent, but comparison between results using generated and real data sets indicate that performance is consistent for similar data sets with the same number of objects, clusters, attributes, and a similar distribution of objects. Generally, group-number representations are better suited to the clustering problem, as are dynamic scaling, elite selection and high mutation rates. Independent generalised models fitted to the correctness and timing results for each of the generated data sets produced accurate predictions of the performance of GCAs on similar real data sets. While GCAs can be successfully adapted to clustering, and the method produces results as accurate and correct as traditional methods, our findings indicate that, given a criterion based on simple distance metrics, GCAs provide no advantages over traditional methods. Second, we investigate the potential of genetic algorithms for the more general clustering problem, where the number of clusters is unknown. We show that only simple modifications to the adapted GCAs are needed. We have developed a merging operator, which with elite selection, is employed to evolve an initial population with a large number of clusters toward better clusterings. With regards to accuracy and correctness, these GCAs are more successful than optimisation methods such as simulated annealing. However, such GCAs can become trapped in local minima in the same manner as traditional hierarchical methods. Such trapping is characterised by the situation where good (k-1)-clusterings do not result from our merge operator acting on good k-clusterings. A marked improvement in the algorithm is observed with the addition of a local heuristic.
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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Delman, Bethany. „Genetic algorithms in cryptography /“. Link to online version, 2003. https://ritdml.rit.edu/dspace/handle/1850/263.
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Krüger, Franz David, und 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 genetiska algoritmer på två utvalda ML-algoritmer, K-nearest neighbors och Random forest, med två numeriska datamängder, Iris-datauppsättning och Wisconsin-bröstcancerdatamängd. Modellen utvärderas med träffsäkerhet och beräkningstid som sedan jämförs med sökmetoden exhaustive search. Resultatet har visat att GA fungerar bra för att hitta bra träffsäkerhetspoäng på en rimlig tid. Det finns vissa begränsningar eftersom parameterns betydelse varierar för olika ML-algoritmer.As machine learning (ML) is being more and more frequent in the business world, information gathering through Data mining (DM) is on the rise, and DM-practitioners are generally using several thumb rules to avoid having to spend a decent amount of time to tune the hyperparameters (parameters that control the learning process) of an ML algorithm to gain a high accuracy score. The proposal in this report is to conduct an approach that systematically optimizes the ML algorithms using genetic algorithms (GA) and to evaluate if and how the model should be constructed to find global solutions for a specific data set. By implementing a GA approach on two ML-algorithms, K-nearest neighbors, and Random Forest, on two numerical data sets, Iris data set and Wisconsin breast cancer data set, the model is evaluated by its accuracy scores as well as the computational time which then is compared towards a search method, specifically exhaustive search. The results have shown that it is assumed that GA works well in finding great accuracy scores in a reasonable amount of time. There are some limitations as the parameter’s significance towards an ML algorithm may vary.
10
Yan, Ping. „Theory of simple genetic algorithms“. Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446649.
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.
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.
13
Fang, Xiaopeng. „Engineering design using genetic algorithms“. [Ames, Iowa : Iowa State University], 2007.
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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.
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 solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed.
16
何淑瑩 und 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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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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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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Mason, Andrew J. „Genetic algorithms and scheduling problems“. Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335134.
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.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 individuals which 'compete' with one another in order to survive, or di erently stated, in order to make it into the next generation. Each individual presents a solution to the problem. Surviving solutions which propagate through to the next generation are typically 'better' or ' tter' than the ones that had died o , hence suggesting a process of optimisation. This process continues until a de ned convergence criteria is met (e.g. speci ed maximum number of generations is reached), where after the best individual in the population serves as the ultimate solution to the problem. This study thoroughly investigates the inner workings that drive the algorithm, after which an algorithm is presented to face the challenges of structural optimisation. This algorithm will be concerned only with sizing optimisation; geometry, topology and shape optimisation is outside the scope of this research. The objective of this optimising problem will be to minimise the weight of the structure, it is assumed that the weight is inversely propotional to the cost of the structure. The motive behind using a genetic algorithm in this study is largely due to its ability to handle discrete search spaces; classical search methods are typically limited to some form of gradient search technique for which the search space must be continuous. The algorithm is also preferred due to its ability to e ciently search through vast search spaces, which is typically the case for a structural optimisation problem. The genetic algorithm's performance will be examined through the use of bench-marking problems. Benchmarking is done for both planar and space trusses; the 10 - and 25 bar truss problems. Such problems are typically analysed with stress and displacement constraints. After the performance of the algorithm is validated, the study commences towards solving real life practical problems. The rst step towards solving such problems would be to investigate the 160 bar truss benchmarking problem. This problem will be slightly adapted by applying South African design standards to the design, SANS (2005). This approach is more realistic, when compared to simply specifying stress and displacement constraints due to the fact that an element cannot simply be assigned the same stress constraint for tension and compression; slenderness and buckling e ects need to be taken into account. For this case, the search space will no longer simply be some sample search space, but will consist of real sections taken from the Southern African Steel Construction Handbook, SAISC (2008). Finally, the research will investigate what is needed to optimise a proper real life structure, the Eskom Self-Supporting Suspension 518H Tower. It will address a wide variety of topics, such as modelling the structure as realistically as possible, to investigating key aspects that might make the problem di erent from standard benchmarking problems and what kind of steps can be taken to over-come possible issues and errors. The algorithm runs in parallel with a nite element method program, provided by Dr G.C. van Rooyen, which analyses the solutions obtained from the algorithm and ensures structural feasibility.
AFRIKAANSE OPSOMMING: Die ontwerp van staal strukture moet 'n sekere optimalisasie proses in sluit wat die aanvanklike ontwerp ontwikkel na 'n meer ekonomiese nale ontwerp, terwyl die nuwe ontwerp nog steeds aan al die aanvanklike ontwerp kriteria voldoen. 'n Kandidaat optimeringstegniek wat voorgestel word deur hierdie navorsing is die genetiese algoritme. Die genetiese algoritme (GA) is 'n optimaliserings tegniek wat ge- ïnspireer was deur evolusionêre beginsels soos die oorlewing van die sterkste (ook bekend as natuurlike seleksie). Dit werk deur die skep van 'n bevolking van individue wat 'kompeteer' met mekaar om dit te maak na die volgende generasie. Elke individu bied 'n oplossing vir die probleem. Oorlewende oplossings wat voortplant deur middel van die volgende generasie is tipies 'beter' of ' kser' as die individue wat uitgesterf het, dus word 'n proses van optimalisering word saamgestel. Hierdie proses gaan voort totdat 'n bepaalde konvergensie kriteria voldoen is (bv. 'n gespesi seerde aantal generasies), waar na die beste individu in die bevolking dien as die uiteindelike oplossing vir die probleem. Hierdie studie ondersoek die genetiese algoritme, waarna 'n algoritme aangebied word om die uitdagings van strukturele optimalisering aan te spreek. Hierdie algoritme het alleenlik te doen met snit optimalisering; meetkunde, topologie en vorm optimalisering is buite die bestek van hierdie navorsing. Die motief agter die gebruik van 'n genetiese algoritme in hierdie studie is grootliks te danke aan sy vermoë om diskrete soek ruimtes te hanteer; klassieke soek metodes word gewoonlik beperk tot 'n vorm van 'n helling tegniek waarvoor die soektog ruimte deurlopende moet wees. Die algoritme is ook gekies as gevolg van sy vermoë om doeltre end deur groot soektog ruimtes te soek, wat gewoonlik die geval vir 'n strukturele probleem met optimering is. Die genetiese algoritme se prestasie sal ondersoek word deur die gebruik van standaarde toetse. Standarde toetse word gedoen vir beide vlak en ruimte kappe, die 10 - en 25 element vakwerk. Sulke probleme word tipies met spanning en verplasing beperkings ontleed. Na a oop van die bekragtiging van die algoritme, word praktiese probleme hanteer. Die eerste stap in die rigting sou wees om die 160 element vakwerk toets probleem te ondersoek. Hierdie probleem sal e ens aangepas word deur die toepassing van die Suid-Afrikaanse ontwerp standaarde, SANS (2005) aan die ontwerp. Dit is 'n meer realistiese benadering in vergelyking met net gespesi seerde spanning en verplasing beperkings as gevolg van die feit dat 'n element nie net eenvoudig dieselfde spanning beperking vir spanning en druk toegeken kan word nie; slankheid en knik e ekte moet ook in ag geneem word. In hierdie geval sal die soek ruimte nie meer net meer eenvoudig 'n sekere teoretiese soek ruimte wees nie, maar sal bestaan uit ware snitte wat uit die Suid Afrikaanse Konstruksie Handboek kom, SAISC (2008). Ten slotte sal die navorsing ondersoek instel na 'n standaard Eskom Transmissie toring en dit sal 'n wye verskeidenheid van onderwerpe aanspreek, soos om die modellering van die struktuur so realisties as moontlik te maak, tot die ondersoek van sleutelaspekte wat die probleem verskillend van standaard toets probleme maak en ook watter soort stappe geneem kan word om moontlike probleme te oor-kom. Die algoritme werk in parallel met 'n eindige element metode program, wat deur Dr GC van Rooyen verskaf is, wat die oplossings ontleed van die algoritme en verseker dat die struktuur lewensvatbaar is.
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 algorithm are proposed and implemented to deal with the single instance tuning. As an instance-based tuning algorithm, the self-learning genetic algorithm, which suggests or predicts a suitable solver configuration for test instances by learning from train instances, is proposed in this thesis. To improve the efficiency of the instance-based tuning in further, a self-learning sexual genetic algorithm, which combines the self-learning mechanism with the sexual genetic algorithm, was discussed. The experiments in the thesis demonstrate how genetic algorithms are implemented and adapted to aid in parameter selection for constraint solvers. Genetic algorithms were implemented as the fundamental algorithm for tuning and the parameter sensitivity of genetic algorithms is also discussed in this thesis.
22
Lochtefeld, Darrell F. „Multi-objectivization in Genetic Algorithms“. Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1308765665.
23
Morrison, Jason (Jason Boyd) Carleton University Dissertation Computer Science. „Co-evolution and genetic algorithms“. Ottawa, 1997.
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.
25
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 concludes from the limited work that was performed that while it seems possible that the use of genetic algorithms might be able to produce visually different graphical resources, Blender currently doesn't seem to be able to produce enough results in a reasonable time frame for this to be usable on a large scale.
26
Harris, Steven C. „A genetic algorithm for robust simulation optimization“. Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178645751.
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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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.
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 techniques are studied and applied in the two algorithms. Constrained benchmarks are optimized and the outcomes show the ability of algorithm in maintaining solutions in the entire pareto-optimal region. In the end, a hybrid method based on the combination of the two algorithms is introduced and the performance is discussed. It is concluded that no significant strength is observed within the approach and more research is required on this topic. For further investigation on the performance of the proposed techniques, implementation on real-world engineering applications are recommended.
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 investigator along the exploratory pathway. The statistical methodology is demonstrated in this thesis using a number of case studies with a classical genetic algorithm with one-point crossover and bit-replacement mutation. In doing so we answer a number of questions about the relationship between the performance of the GA and the operators and encoding used. The methodology is suitable, however, to be applied to other adaptive optimization algorithms not treated in this thesis. In the first instance, as an initial demonstration of our methodology, we describe case studies using four standard test functions. It is found that the effect upon performance of crossover is predominantly linear while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behaviour. In the case of crossover both positive and negative gradients are found which suggests using rates as high as possible for some problems while possibly excluding it for others. .... This is illustrated by showing how the use of Gray codes impedes the performance on a lower modality test function compared with a higher modality test function. Computer animation is then used to illustrate the actual mechanism by which this occurs. Fourthly, the traditional concept of a GA is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems an exploration is made by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees in the solution space rendering them not-linear-separable. It is shown that for the difficult rotated functions the crossover operator is detrimental to the performance of the GA. It is conjectured that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Furthermore, the GA was tested on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. It is demonstrated that they match and that the features which make certain of the test functions difficult are also present in the real world problem. Overall, the proposed methodology is found to be an effective tool for revealing relationships between a randomized optimization algorithm and its encoding and parameters that are difficult to establish from more ad-hoc experimental studies alone.
30
Kim, Jeongwook. „Genetic algorithms for smart embedded systems“. Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13886.
31
Carlson, Susan Elizabeth. „Component selection optimization using genetic algorithms“. Diss., Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/17886.
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.
Annotation:
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.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 evaluating policies is required. Genetic Algorithms provide such a method, and have been used successfully on a wide variety of tasks (21).
The best replacement policy found using this approach had a mean improvement in overall hit rate of 0.6% over LRU for the benchmarks used. This corresponds to 27% of the 2.2% mean difference between LRU and OPT. Performance of the best of these replacement policies was found to be generally superior to shadow cache (33), an enhanced replacement policy similar to some of those used here.
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.
Annotation:
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 is compared to that of the conventional gradient search algorithm for both the sufficient and insufficient order cases with varying levels of noise. It is found that the genetic algorithm outperforms the gradient algorithm in the insufficient cases.
34
Anderson, Jon K. „Genetic algorithms applied to graph theory“. Virtual Press, 1999. http://liblink.bsu.edu/uhtbin/catkey/1136714.
Annotation:
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 will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age.Department of Computer Science
35
Graham, Ian J. „Genetic algorithms for evolutionary product design“. Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/6900.
Annotation:
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 aesthetically interesting forms (objects). The integration of GA software with a solid modelling system has enabled the development of a prototype `Evolutionary Form Design' (EFD) system. Objects are defined using a genetic data structure and constructed from various geometric primitives and combinations of Boolean operators. The primitives interact in ways that are not easily predicted, often creating novel shapes that are unlikely to have been discovered through conventional means. Edge blending further adds to objects' complexity and visual appeal. Populations of objects are subjected to a `selective breeding' programme, directed through the user's allocation of scores, and may also be guided by simple geometric targets. These factors determine which objects are `fittest' and most likely to parent a new, hopefully improved generation of objects. The challenge has been to turn the concept into a genuinely useful tool, ensuring that desirable features are reproduced in subsequent populations. The key to achieving this is the way objects are recombined during reproduction. Work has included developing 4 novel routine for grouping the individual primitives that form objects using a Teamforming algorithm. Innovative, aesthetically interesting forms can be evolved intuitively and efficiently, providing inspiration and the initial models for original design concepts. Examples are given where the system'is used by undergraduates to generate seating designs, and by the author, to create virtual sculptures and a range of consumer product concepts.
36
Lancaster, John. „Project schedule optimisation utilising genetic algorithms“. Thesis, Brunel University, 2007. http://bura.brunel.ac.uk/handle/2438/5798.
Annotation:
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 optimisation requirement may be subtle only performing slight resource levelling or more profound by selecting an optimal mode of execution for a number of activities or evaluating a number of alternative strategies. This research proposes a unique, efficient algorithm using adaptation based on the fitness improvement over successive generations. The algorithm is tested initially using a MATLAB based implementation to solve instances of the travelling salesman problem (TSP). The algorithm is then further developed both within MATLAB and Microsoft Project Visual Basic to optimise both known versions of the Resource Constrained Project Scheduling Problems as well as investigating newly defined variants of the problem class.
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.
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.
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.
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.
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.
Annotation:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORGRUPO DE TECNOLOGIA DE COMPUTAÇÃO GRÁFICA - PUC-RIO
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 durante as operações de exploração de óleo é garantido pelas linhas de ancoragem, que são estruturas flexíveis compostas, geralmente, por trechos de aço, amarras e/ou cabos sintéticos. O presente trabalho apresenta o desenvolvimento de um Algoritmo Genético (AG) para solucionar o problema da disposição das linhas de ancoragem de unidades flutuantes utilizadas nas operações de exploração de petróleo. A distribuição das linhas de ancoragem é um dos fatores que influencia diretamente nos deslocamentos (offsets) sofridos pelas unidades flutuantes quando submetidas às ações ambientais, como ventos, ondas e correntes. Desta forma, o AG busca uma disposição ótima das linhas de ancoragem cujo objetivo final é a minimização dos deslocamentos da unidade flutuante. Os operadores básicos utilizados por este algoritmo são mutação, crossover e seleção. Neste trabalho, foi adotada a técnica steady-state, que só efetua a substituição de um ou dois indivíduos por geração. O cálculo da posição de equilíbrio estático da unidade flutuante é feito aplicando-se a equação da catenária para cada linha de ancoragem com o objetivo de se obterem as forças de restauração na unidade, e empregando-se um processo iterativo para calcular a sua posição final de equilíbrio.
With the increasing demand for oil, oil companies have been forced to exploit new fields in deep waters. Due to the high cost of oil exploitation operations, the development of technologies capable of increasing efficiency and reducing costs is crucial. In this context, the use of floating units in deep waters has become more frequent. The positioning of the floating units during oil exploitation operations is done using mooring lines, which are flexible structures usually made of steel wire, steel chain and/or synthetic cables. This work presents the development of a Genetic Algorithm (GA) procedure to solve the problem of the mooring pattern of floating units used in oil exploitation operations. The distribution of mooring lines is one of the factors that directly influence the displacements (offsets) suffered by floating units when subjected to environmental conditions such as winds, waves and currents. Thus, the GA seeks an optimum distribution of the mooring lines whose final goal is to minimize the units´ displacements. The basic operators used in this algorithm are mutation, crossover and selection. In the present work, the steady- state GA has been implemented, which performs the substitution of only one or two individuals per generation. The computation of the floating unit´s static equilibrium position is accomplished by applying the catenary equilibrium equation to each mooring line in order to obtain the out-of-balance forces on the unit, and by using an iterative process to compute the final unit equilibrium position.
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.
43
Viswanathan, Lakshmi. „Genetic algorithms for uncapacitated network design“. Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36022.
Annotation:
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 124).
by Viswanathan Lakshmi.
M.S.
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.
45
Medlar, A. J. „Manycore algorithms for genetic linkage analysis“. Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1366896/.
Annotation:
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 increasing numbers of physical processing cores, are not designed to take advantage of the available processing power. In this thesis, we will describe our program SwiftLink that embodies our work adapting existing Gibbs samplers to modern computer processor architectures. The processor architectures we target are: multicore processors, that currently feature between 4–8 processor cores, and computer graphics cards (GPUs) that already feature hundreds of processor cores. We implemented parallel versions of the meiosis sampler, that mixes well with tightly linked markers but suffers from irreducibility issues, and the locus sampler which is guaranteed to be irreducible but mixes slowly with tightly linked markers. We evaluate SwiftLink’s performance on real-world datasets of large consanguineous families. We demonstrate that using four processor cores for a single analysis is 3–3.2x faster than the single-threaded implementation of SwiftLink. With respect to the existing MCMC-based programs: it achieves a 6.6–8.7x speedup compared to Morgan and a 66.4– 72.3x speedup compared to Simwalk. Utilising both a multicore processor and a GPU performs 7–7.9x faster than the single-threaded implementation, a 17.6–19x speedup compared to Morgan and a 145.5–192.3x speedup compared to Simwalk.
46
Fang, Hsiao-Lan. „Genetic algorithms in timetabling and scheduling“. Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/30185.
Annotation:
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 reliability and robustness of the approach is conducted. The basic approach can readily handle a wide variety of general timetabling problem constraints, and is therefore likely to be of great practical usefulness (indeed, an earlier version is already in use). The approach relies for its success on the use of specially designed mutation operators which greatly improve upon the performance of a GA with standard operators. A framework for GAs in job-shop and open-shop scheduling is also presented. One of the key aspects of this approach is the use of specially designed representations for such scheduling problems. The representations implicitly encode a schedule by encoding instructions for a schedule builder. The general robustness of this approach is demonstrated with respect to experiments on a range of widely-used benchmark problems involving many different schedule quality criteria. When compared against a variety of common heuristic search approaches, the GA approach is clearly the most successful method overall. An extension to the representation, in which choices of heuristic for the schedule builder are also incorporated in the chromosome, is found to lead to new best results on the makespan for some well known benchmark open-shop scheduling problems. The general approach is also shown to be readily extendable to rescheduling and dynamic scheduling.
47
Rogers, Alex. „Modelling genetic algorithms and evolving populations“. Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/261289/.
Annotation:
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 caricature of an optimisation problem with many local minima is considered — the basin with a barrier problem. The first passage time — the time required to escape the local minima to the global minimum — is calculated and insights gained as to how the genetic algorithm is searching the landscape. The interaction of the various genetic algorithm operators and how these interactions give rise to optimal parameters values is studied.
48
Lovestead, Raymond L. „Helical Antenna Optimization Using Genetic Algorithms“. Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/35295.
Annotation:
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 control a polynomial that defines the envelope around which the helix is wrapped. The fitness level is defined as a combination of gain, bandwidth and axial ratio as determined by an analysis of the helix using NEC2.
To experimentally verify the optimization results, a prototype 12-turn, two-wavelength high, GA-helix is built and tested on the Virginia Tech outdoor antenna range. Far-field radiation patterns are measured over a wide frequency range. The axial-ratio information is extracted from the measured pattern data. Comparison of measured and NEC-2 computed radiation patterns shows excellent agreement. The agreement between the measured and calculated axial-ratios is reasonable. The prototype GA-helix provides a peak gain of more than 13 dB and an upper-to-lower frequency ratio of 1.89. The 3-dB bandwidth of the antenna is 1.27 GHz (1.435 GHz - 2.705 GHz). Over this bandwidth the computed gain varies less than 3 dB and the axial-ratio remains below 3 dB.Master of Science
49
MUPPIDI, SRINIVAS REDDY. „GENETIC ALGORITHMS FOR MULTI-OBJECTIVE PARTITIONING“. University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1080827924.
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.
Annotation:
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 objective function is introduced to be optimised over the shape parameters. GAs are then employed to optimise the objective function. Quantified assessment of the results using two different anatomical objects is produced and comparisons to interactive object segmentation are made. The problem of texture based segmentation is also considered. The detection of texture is formalised as a problem of designing a mask that exploits relationships between the spectra of different classes of texture. Results are produced in the case of artificial patterns, natural texture and texture present in medical images, including modalities like MRI and X-rays. The results of the segmentation are compared to other better established texture discrimination techniques. Finally, the problem of noise suppression is formulated as a problem of stack filter configuration, a broad family of non-linear filters. Results are produced for different types of noise, including additive uncorrelated noise as well as multiplicative or gaussian and poisson noise. Results from the application of the configured filter are compared to those of other digital filters, commonly used for noise reduction.