Rozprawy doktorskie: „Genetic theory”

1

Yan, Ping. "Theory of simple genetic algorithms". Thesis, University of Macau, 2000. http://umaclib3.umac.mo/record=b1446649.

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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 will be analyzed with respect to string rates, child improvement, pool rating resolution, and average string age.
Department of Computer Science

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3

Kang, Yong-Ho. "Adaptive control via genetic algorithms : theory and application". Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308023.

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4

Morr, Lindsey. "Cascade testing communication within Lynch syndrome families: An examination of communication privacy management theory". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1525765585195444.

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5

Popelka, Aleš. "Datamining - theory and it's application". Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-164981.

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This thesis deals with the topic of the technology called data mining. First, the thesis describes the term data mining as an independent discipline and then its processing methods and the most common use. The term data mining is thereafter explained with the help of methodologies describing all parts of the process of knowledge discovery in databases -- CRISP-DM, SEMMA. The study's purpose is presenting new data mining methods and particular algorithms -- decision trees, neural networks and genetic algorithms. These facts are used as theoretical introduction, which is followed by practical application searching for causes of meningoencephalitis development of certain sample of patients. Decision trees in system Clementine, which is one of the top datamining tools, were used for the analysys.

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6

Hellenthal, Garrett. "Exploring rates and patterns of variability in gene conversion and crossover in the human genome /". Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8961.

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7

Reynolds, David. "Theory of genetic algorithms with applications to heat integration networks". Thesis, Glasgow Caledonian University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296464.

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8

Mambrini, Andrea. "Theory grounded design of genetic programming and parallel evolutionary algorithms". Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5928/.

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Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we will advance the theoretical understanding of evolutionary algorithms, particularly focusing on Genetic Programming and Parallel Evolutionary algorithms. We will do that trying to understand how different design choices affect the performance of the algorithms and providing rigorously proven bounds of the running time for different designs. This novel knowledge, built upon previous work on the theoretical foundation of EAs, will then help for the second objective of the thesis, which is to provide theory grounded design for Parallel Evolutionary Algorithms and Genetic Programming. This will consist in being inspired by the analysis of the algorithms to produce provably good algorithm designs.

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9

Richter, James Neal. "On mutation and crossover in the theory of evolutionary algorithms". Thesis, Montana State University, 2010. http://etd.lib.montana.edu/etd/2010/richter/RichterJ0510.pdf.

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The Evolutionary Algorithm is a population-based metaheuristic optimization algorithm. The EA employs mutation, crossover and selection operators inspired by biological evolution. It is commonly applied to find exact or approximate solutions to combinatorial search and optimization problems. This dissertation describes a series of theoretical and experimental studies on a variety of evolutionary algorithms and models of those algorithms. The effects of the crossover and mutation operators are analyzed. Multiple examples of deceptive fitness functions are given where the crossover operator is shown or proven to be detrimental to the speedy optimization of a function. While other research monographs have shown the benefits of crossover on various fitness functions, this is one of the few (or only) doing the inverse. A background literature review is given of both population genetics and evolutionary computation with a focus on results and opinions on the relative merits of crossover and mutation. Next, a family of new fitness functions is introduced and proven to be difficult for crossover to optimize. This is followed by the construction and evaluation of executable theoretical models of EAs in order to explore the effects of parameterized mutation and crossover. These models link the EA to the Metropolis-Hastings algorithm. Dynamical systems analysis is performed on models of EAs to explore their attributes and fixed points. Additional crossover deceptive functions are shown and analyzed to examine the movement of fixed points under changing parameters. Finally, a set of online adaptive parameter experiments with common fitness functions is presented.

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10

Krasnogor, Natalio. "Studies on the theory and design space of memetic algorithms". Thesis, University of the West of England, Bristol, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249135.

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11

Ma, Qiang. "The application of genetic algorithms to the adaptation of IIR filters". Thesis, Loughborough University, 1995. https://dspace.lboro.ac.uk/2134/32269.

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The adaptation of an IIR filter is a very difficult problem due to its non-quadratic performance surface and potential instability. Conventional adaptive IIR algorithms suffer from potential instability problems and a high cost for stability monitoring. Therefore, there is much interest in adaptive IIR filters based on alternative algorithms. Genetic algorithms are a family of search algorithms based on natural selection and genetics. They have been successfully used in many different areas. Genetic algorithms applied to the adaptation of IIR filtering problems are studied in this thesis, and show that the genetic algorithm approach has a number of advantages over conventional gradient algorithms, particularly, for the adaptation of high order adaptive IIR filters, IIR filters with poles close to the unit circle and IIR filters with multi-modal error surfaces. The conventional gradient algorithms have difficulty solving these problems. Coefficient results are presented for various orders of IIR filters in this thesis. In the computer simulations presented in this thesis, the direct, cascade, parallel and lattice form IIR filter structures have been used and compared. The lattice form IIR filter structure shows its superiority over the cascade and parallel form IIR filter structures in terms of its mean square error convergence performance.

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12

Suzuki, Ayaka. "Familial Communication of Positive BRCA1/2 Genetic Testing Results: A Relational Dialectics Theory Approach". University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504787059498275.

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13

Al-Daraiseh, Ahmad. "GENETICALLY ENGINEERED ADAPTIVE RESONANCE THEORY (ART) NEURAL NETWORK ARCHITECTURES". Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3171.

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Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data is of noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this phenomenon. In this thesis we propose a new approach to handle the category proliferation problem in Fuzzy ARTMAP by evolving trained FAM architectures. We refer to the resulting FAM architectures as GFAM. We demonstrate through extensive experimentation that an evolved FAM (GFAM) exhibits good (sometimes optimal) generalization, small size (sometimes optimal size), and requires reasonable computational effort to produce an optimal or sub-optimal network. Furthermore, comparisons of the GFAM with other approaches, proposed in the literature, which address the FAM category proliferation problem, illustrate that the GFAM has a number of advantages (i.e. produces smaller or equal size architectures, of better or as good generalization, with reduced computational complexity). Furthermore, in this dissertation we have extended the approach used with Fuzzy ARTMAP to other ART architectures, such as Ellipsoidal ARTMAP (EAM) and Gaussian ARTMAP (GAM) that also suffer from the ART category proliferation problem. Thus, we have designed and experimented with genetically engineered EAM and GAM architectures, named GEAM and GGAM. Comparisons of GEAM and GGAM with other ART architectures that were introduced in the ART literature, addressing the category proliferation problem, illustrate similar advantages observed by GFAM (i.e, GEAM and GGAM produce smaller size ART architectures, of better or improved generalization, with reduced computational complexity). Moverover, to optimally cover the input space of a problem, we proposed a genetically engineered ART architecture that combines the category structures of two different ART networks, FAM and EAM. We named this architecture UART (Universal ART). We analyzed the order of search in UART, that is the order according to which a FAM category or an EAM category is accessed in UART. This analysis allowed us to better understand UART's functionality. Experiments were also conducted to compare UART with other ART architectures, in a similar fashion as GFAM and GEAM were compared. Similar conclusions were drawn from this comparison, as in the comparison of GFAM and GEAM with other ART architectures. Finally, we analyzed the computational complexity of the genetically engineered ART architectures and we compared it with the computational complexity of other ART architectures, introduced into the literature. This analytical comparison verified our claim that the genetically engineered ART architectures produce better generalization and smaller sizes ART structures, at reduced computational complexity, compared to other ART approaches. In review, a methodology was introduced of how to combine the answers (categories) of ART architectures, using genetic algorithms. This methodology was successfully applied to FAM, EAM and FAM and EAM ART architectures, with success, resulting in ART neural networks which outperformed other ART architectures, previously introduced into the literature, and quite often produced ART architectures that attained optimal classification results, at reduced computational complexity.
Ph.D.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Computer Engineering

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14

Hu, Xin-Sheng. "Genetic marker studies of the Larix gmelinii complex and the development of genetic marker theory for plant populations". Thesis, University of Edinburgh, 1998. http://hdl.handle.net/1842/17011.

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The thesis is composed of two parts that are related by the theme of genetic markers. The first part involves the application of genetic markers to investigate the mating system, population genetic structure, and evolutionary relationship of the three Chinese larch taxa: Larix gmelinii, L. olgensis and L. principis-rupprechtii. Seventeen populations of the Larix taxa listed above were analysed using eight polymorphic allozyme markers. Results indicated that mating system was variable among taxa and among natural populations within taxa. Population differentiation of each taxa was very small, showing that less than 2% of total genetic variation occurred among populations. Spatial distribution of genetic variation of L. gmelinii was random, but a weak pattern of isolation by distance was detected in L. olgensis. The genetic relationship among the three taxa elucidated by allozyme markers indicated that the genetic distances were very low between them. Based on morphological traits and the results obtained by allozyme and cpDNA sequence markers, it is reasonable to consider L. olgensis and L. principis-rupprechtii to be two varieties of L. gmelinii rather than two separate Larix species. In the second part of this thesis, theories of plant population genetic structure were developed to incorporate biparentally, paternally, and maternally inherited genes into a variety of models. Population differentiation for each of the three plant genomes was formulated in the island, stepping stone and isolation by distance models of population structure. The results showed that maternally inherited organelle genes maintain larger differentiation than paternally inherited organelle genes, which in turn maintain larger differentiation than biparentally inherited nuclear genes. In the stepping-stone model, differences in genetic correlation with distance among the differently inherited genomes were conditional on the values of long and short distance migration for pollen and seeds. The relative contribution to migration of seed and pollen flow can be estimated in terms of gene frequency data or DNA sequence data.

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15

Stewart, William C. L. "Alternative models for estimating genetic maps from pedigree data /". Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/8975.

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16

Kohli, Sakshi. "Understanding the role of Mycobacterium tuberculosis pe proteins in bacterial virulence". Thesis, IIT Delhi, 2017. http://localhost:8080/xmlui/handle/12345678/7232.

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17

Ranga, Mithun Kumar. "Fixed order optimal control using genetic algorithms /". free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426097.

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18

Ding, Yingjia. "Knowledge retention with genetic algorithms by multiple levels of representation". Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-12052009-020026/.

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19

Field, Paul David. "A multary theory for genetic algorithms : unifying binary and nonbinary problem representations". Thesis, Queen Mary, University of London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309083.

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20

Subramaniam, Velusamy. "Scheduling of manufacturing systems based on extreme value theory and genetic algorithms". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/35464.

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21

Boisvert, Danielle. "Rethinking Gottfredson and Hirschi's General Theory of Crime: A Behavioral Genetic Approach". Cincinnati, Ohio : University of Cincinnati, 2009. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1243306307.

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Thesis (Ph.D.)--University of Cincinnati, 2009.
Advisor: John P. Wright. Title from electronic thesis title page (viewed July 29, 2009). Keywords: low self-control; behavioral genetics; Mx; Gottfredson and Hirschi; general theory of crime; sex differences; biosocial criminology. Includes abstract. Includes bibliographical references.

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22

Tsai, Chen-Fang. "Improving the economic design of multiple control charts using a dynamic genetic algorithm-based program". Thesis, University of Sunderland, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.360083.

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23

Fonseca, Carlos Manuel Mira da. "Multiobjective genetic algorithms with application to control engineering problems". Thesis, University of Sheffield, 1995. http://etheses.whiterose.ac.uk/1887/.

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Genetic algorithms (GAs) are stochastic search techniques inspired by the principles of natural selection and natural genetics which have revealed a number of characteristics particularly useful for applications in optimization, engineering, and computer science, among other fields. In control engineering, they have found application mainly in problems involving functions difficult to characterize mathematically or known to present difficulties to more conventional numerical optimizers, as well as problems involving non-numeric and mixed-type variables. In addition, they exhibit a large degree of parallelism, making it possible to effectively exploit the computing power made available through parallel processing. Despite their early recognized potential for multiobjective optimization (almost all engineering problems involve multiple, often conflicting objectives), genetic algorithms have, for the most part, been applied to aggregations of the objectives in a single-objective fashion, like conventional optimizers. Although alternative approaches based on the notion of Pareto-dominance have been suggested, multiobjective optimization with genetic algorithms has received comparatively little attention in the literature. In this work, multiobjective optimization with genetic algorithms is reinterpreted as a sequence of decision making problems interleaved with search steps, in order to accommodate previous work in the field. A unified approach to multiple objective and constraint handling with genetic algorithms is then developed from a decision making perspective and characterized, with application to control system design in mind. Related genetic algorithm issues, such as the ability to maintain diverse solutions along the trade-off surface and responsiveness to on-line changes in decision policy, are also considered. The application of the multiobjective GA to three realistic problems in optimal controller design and non-linear system identification demonstrates the ability of the approach to concurrently produce many good compromise solutions in a single run, while making use of any preference information interactively supplied by a human decision maker. The generality of the approach is made clear by the very different nature of the two classes of problems considered.

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24

Streeter, Matthew J. "Automated discovery of numerical approximation formulae via genetic programming". Link to electronic thesis, 2001. http://www.wpi.edu/Pubs/ETD/Available/etd-0426101-231555.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Title from title screen. Keywords: genetic programming; approximations; machine learning; artificial intelligence. Includes bibliographical references (p. 92-94).

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25

Ahnert, S. E. "Implementations of information theory in molecular dynamics, quantum measurement and genetic data analysis". Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595400.

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The research presented in this thesis is divided into three topics, all of which are related to different areas of Information Theory. Firstly I have worked with my supervisor Professor Mike Payne on proposals for the implementation of two different types of quantum measurements of single photons, namely generalized measurements and weak measurements. This research belongs to the field of Quantum Information Theory. My second area of research has been Molecular Dynamics. With Gabor Csanyi I have worked on the development of a new way of constructing empirical potentials, which are needed for the large-scale simulation of atomic systems. Until now the construction of such potentials has been a laborious task and highly specific to the particular species of atoms involved. Our method, which employs a fitting technique known as Gaussian Processes, aims to provide a comparatively simple and very general way of constructing empirical potentials, using data from quantum-mechanical methods, such as Tight-Binding schemes. In this thesis I present results which demonstrate the feasibility of fitting a function in the configuration space of atomic neighbourhoods. My third research topic has been the analysis of biological data series using algorithmic information theory, together with Thomas Fink of the Institut Curie in Paris. By calculating a bound on the Algorithmic Information Content (AIC) of a given data curve we are able to identify biologically significant curves, thus providing a tool for biological data analysis.

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26

Yeates, Jessica Anne Mellor. "The Foundations of Network Dynamics in an RNA Recombinase System". PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/2919.

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How life originated from physical and chemical processes is one of the great questions still unanswered today. Studies towards this effort have transitioned from the notion of a single self-replicating entity to the idea that a network of interacting molecules made this initial biological leap. In order to understand the chemical kinetic and thermodynamic mechanisms that could engender pre-life type networks we present an empirical characterization of a network of RNA recombinase molecules. We begin with 1-, 2-, and 3-molecular ensembles and provide a game theoretic analysis to describe the frequency dependent dynamics of competing and cooperating RNA genotypes. This is then extended to 4- and 5-membered networks where varying topologies are compared and mechanisms that could lead to preferential growth and selection of genotypes are described. At the core of these network connections is ribozyme catalysis initiated through a 3-nucleotide base-pairing interface. With the development of a fluorescence anisotropy method, we are able to illustrate a correlation between these binding thermodynamics and network outcomes. Finally, we consider how the heterogeneity of the environment could impact network dynamics and develop a spectrum of spatial inducing methods in which our chemical populations can be probed. These experiments illustrate simple chemical dynamics of RNA interactions, yet these very processes are the foundation for building complexity and ultimately from where selection and evolvability derive.

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27

Bryant, Cari Rihan. "A computational theory for the generation of solutions during early conceptual design". Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.mst.edu/thesis/pdf/crb5ea_09007dcc8042a16d.pdf.

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Thesis (Ph. D.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 15, 2008) Includes bibliographical references (p. 236-249).

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28

Kunt, Tomáš. "Vícekriteriální analýza portfolia na českých nebo zahraničních trzích". Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15696.

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The objective of this thesis is to apply alternative multi-objective optimization techniques to the portfolio selection problem. Theoretical part starts with detailed analysis of the classical Markowitz model and its assumptions. Following that, introduction of multi-criterion optimization techniques available for finding non-dominated portfolios is given. One of these techniques, the genetic algorithm, is presented in great detail. Some of the basic methods useful for predicting stock prices and its risks are presented at the end of the theoretical part. Practical part presents an application of the theory to the problem of constructing efficient portfolios of 11 selected stocks traded on Prague Stock Exchange. Results achieved by different approaches are compared and interpreted.

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29

Bethelmy, David G. "Aspect Mining Using Multiobjective Genetic Clustering Algorithms". NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/952.

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In legacy software, non-functional concerns tend to cut across the system and manifest themselves as tangled or scattered code. If these crosscutting concerns could be modularized and the system refactored, then the system would become easier to understand, modify, and maintain. Modularized crosscutting concerns are known as aspects and the process of identifying aspect candidates in legacy software is called aspect mining. One of the techniques used in aspect mining is clustering and there are many clustering algorithms. Current aspect mining clustering algorithms attempt to form clusters by optimizing one objective function. However, the objective function to be optimized tends to bias the formation of clusters towards the data model implicitly defined by that function. One solution is to use algorithms that try to optimize more than one objective function. These multiobjective algorithms have been used successfully in data mining but, as far as this author knows, have not been applied to aspect mining. This study investigated the feasibility of using multiobjective evolutionary algorithms, in particular, multiobjective genetic algorithms, in aspect mining. The study utilized an existing multiobjective genetic algorithm, MOCK, which had already been tested against several popular single objective clustering algorithms. MOCK has been shown to be, on average, as good as, and sometimes better than, those algorithms. Since some of those data mining algorithms have counterparts in aspect mining, it was reasonable to assume that MOCK would perform at least as good in an aspect mining context. Since MOCK's objective functions were not directly trying to optimize aspect mining metrics, the study also implemented another multiobjective genetic algorithm, AMMOC, based on MOCK but tailored to optimize those metrics. The reasoning hinged on the fact that, since the goal was to determine if a clustering method resulted in optimizing these quality metrics, it made sense to attempt to optimize these functions directly instead of a posteriori. This study determined that these multiobjective algorithms performed at least as good as two popular aspect mining algorithms, k-means and hierarchical agglomerative. As a result, this study has contributed to both the theoretical body of knowledge in the field of aspect mining as well as provide a practical tool for the field.

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30

White, Melanie Jade. "Understanding impulsivity : molecular genetic and environmental influences". Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/16578/1/Melanie_J._White_Thesis.pdf.

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Features of impulsivity underlie multiple psychological disorders. The body of work examining impulsivity has largely focussed on self-report measurement and has incorporated psychological constructs without reference to the broader biological factors that may influence impulsive behaviour. Two studies were conducted to examine whether environmental stress and genetic status associated with dopaminergic and serotonergic function (DRD2, ANKK1 and 5HT2AR genotypes) were predictive of dimensions of impulsivity and risky behaviour (alcohol use). The two studies used a multi-method approach in a non-clinical community sample of young adults (aged 17-25 years). Dopamine is integral to the two leading theories of impulsive personality, Gray's Reinforcement Sensitivity Theory and Cloninger's Psychobiological model of personality. Dopamine plays a crucial role in reward reinforcement circuits in the brain. The A1 allele of the ANKK1 gene (also referred to as TaqIA of the DRD2 gene region) and the CC genotype of the C957T polymorphism of the DRD2 gene have both been associated with reduced D2 dopamine receptor density in key structures linked to brain reward. In addition, a strong body of evidence implicates their involvement in a number of clinical disorders associated with impulsivity. Serotonin function has also been associated with impulsivity in Cloninger's theory and there is also evidence of associations of two polymorphisms of the 2A serotonin receptor gene (5HT2AR T102C and -1438A/G SNPs) with impulsivity. Acute and chronic forms of stress are also important correlates of impulsive behaviour and the two studies directly examined the relationship between genotype, stress and impulsivity. Study 1 (N=180) utilised a cross-sectional design and examined interactions between these polymorphisms and chronic stress exposure on key impulsivity dimensions of reward sensitivity, Novelty Seeking and rash impulsiveness. Participants completed psychological questionnaires measuring chronic stress, dimensions of impulsivity, mood and substance use and provided mouth swab samples of buccal mucosal cells for DNA analysis. The study confirmed the association between A1 and CC allelic status and chronic stress being associated with harm avoidance and sensitivity to punishment. This suggests a role for both dopamine and background stress in impulsive behaviour. Study 2 (N=73) built upon this questionnaire research in the laboratory by utilising experimental psychological paradigms of impulsive behaviour and experimentally manipulating acute stress. Study 2 employed a mixed experimental design with a sub-sample of those studied in the cross-sectional sample. These behavioural paradigms included pre- and post- stress induction administration of the Card Arranging Reward Responsiveness Objective Test (capturing behavioural approach in the presence of reward cues, presumed to reflect reward sensitivity) and post-induction delay discounting and response inhibition measures. Study 2 confirmed the role of one of the two dopamine-related polymorphisms, with those with A1+ allelic status demonstrating lower reward responsiveness prior to rest or stress induction, which was overcome in the second administration of this task, independent of environment. A1+ allelic individuals also demonstrated significantly poorer response inhibition independent of stress, further confirming the association between A1+ allelic status and impulsivity. Those with CC allelic status showed an increase in reward responsiveness only in the stress induction condition. Together, results from the two studies inform the development of a multidimensional model of impulsivity that captures gene-environment influences on discrete aspects of impulsive personality and behaviour. Further refinement of this model may lead to the development of more effective customised prevention and treatment interventions for clinically disordered impulsivity. The implications of dopaminergic systems and stress in understanding disorders such as ADHD and substance dependence are discussed.

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31

White, Melanie Jade. "Understanding impulsivity : molecular genetic and environmental influences". Queensland University of Technology, 2008. http://eprints.qut.edu.au/16578/.

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Features of impulsivity underlie multiple psychological disorders. The body of work examining impulsivity has largely focussed on self-report measurement and has incorporated psychological constructs without reference to the broader biological factors that may influence impulsive behaviour. Two studies were conducted to examine whether environmental stress and genetic status associated with dopaminergic and serotonergic function (DRD2, ANKK1 and 5HT2AR genotypes) were predictive of dimensions of impulsivity and risky behaviour (alcohol use). The two studies used a multi-method approach in a non-clinical community sample of young adults (aged 17-25 years). Dopamine is integral to the two leading theories of impulsive personality, Gray's Reinforcement Sensitivity Theory and Cloninger's Psychobiological model of personality. Dopamine plays a crucial role in reward reinforcement circuits in the brain. The A1 allele of the ANKK1 gene (also referred to as TaqIA of the DRD2 gene region) and the CC genotype of the C957T polymorphism of the DRD2 gene have both been associated with reduced D2 dopamine receptor density in key structures linked to brain reward. In addition, a strong body of evidence implicates their involvement in a number of clinical disorders associated with impulsivity. Serotonin function has also been associated with impulsivity in Cloninger's theory and there is also evidence of associations of two polymorphisms of the 2A serotonin receptor gene (5HT2AR T102C and -1438A/G SNPs) with impulsivity. Acute and chronic forms of stress are also important correlates of impulsive behaviour and the two studies directly examined the relationship between genotype, stress and impulsivity. Study 1 (N=180) utilised a cross-sectional design and examined interactions between these polymorphisms and chronic stress exposure on key impulsivity dimensions of reward sensitivity, Novelty Seeking and rash impulsiveness. Participants completed psychological questionnaires measuring chronic stress, dimensions of impulsivity, mood and substance use and provided mouth swab samples of buccal mucosal cells for DNA analysis. The study confirmed the association between A1 and CC allelic status and chronic stress being associated with harm avoidance and sensitivity to punishment. This suggests a role for both dopamine and background stress in impulsive behaviour. Study 2 (N=73) built upon this questionnaire research in the laboratory by utilising experimental psychological paradigms of impulsive behaviour and experimentally manipulating acute stress. Study 2 employed a mixed experimental design with a sub-sample of those studied in the cross-sectional sample. These behavioural paradigms included pre- and post- stress induction administration of the Card Arranging Reward Responsiveness Objective Test (capturing behavioural approach in the presence of reward cues, presumed to reflect reward sensitivity) and post-induction delay discounting and response inhibition measures. Study 2 confirmed the role of one of the two dopamine-related polymorphisms, with those with A1+ allelic status demonstrating lower reward responsiveness prior to rest or stress induction, which was overcome in the second administration of this task, independent of environment. A1+ allelic individuals also demonstrated significantly poorer response inhibition independent of stress, further confirming the association between A1+ allelic status and impulsivity. Those with CC allelic status showed an increase in reward responsiveness only in the stress induction condition. Together, results from the two studies inform the development of a multidimensional model of impulsivity that captures gene-environment influences on discrete aspects of impulsive personality and behaviour. Further refinement of this model may lead to the development of more effective customised prevention and treatment interventions for clinically disordered impulsivity. The implications of dopaminergic systems and stress in understanding disorders such as ADHD and substance dependence are discussed.

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32

Nikram, Elham. "Three essays on game theory and computation". Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/28755.

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The results section of my thesis includes three chapters. The first two chapters are on theoretical game theory. In both chapters, by mathematical modelling and game theoretical tools, I am predicting the behaviour of the players in some real world issues. Hoteling-Downs model plays an important role in the modern political interpretations. The first chapter of this study investigates an extension of Hoteling-Downs model to have multi-dimensional strategy space and asymmetric candidates. Chapter 3 looks into the inspection game where the inspections are not the same in the series of sequential inspections. By modelling the game as a series of recursive zero-sum games I find the optimal strategy of the players in the equilibrium. The forth chapter investigates direct optimization methods for large scale problems. Using Matlab implementations of Genetic and Nelder-Mead algorithms, I compare the efficiency and accuracy of the most famous direct optimization methods for unconstraint optimization problems based on differing number of variables.

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33

Uduman, Mohamed. "Identifying the largest complete data set from ALFRED /". Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/1876.

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Godley, Paul Michael. "Directed intervention crossover approaches in genetic algorithms with application to optimal control problems". Thesis, University of Stirling, 2009. http://hdl.handle.net/1893/1143.

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Genetic Algorithms (GAs) are a search heuristic modeled on the processes of evolution. They have been used to solve optimisation problems in a wide variety of fields. When applied to the optimisation of intervention schedules for optimal control problems, such as cancer chemotherapy treatment scheduling, GAs have been shown to require more fitness function evaluations than other search heuristics to find fit solutions. This thesis presents extensions to the GA crossover process, termed directed intervention crossover techniques, that greatly reduce the number of fitness function evaluations required to find fit solutions, thus increasing the effectiveness of GAs for problems of this type. The directed intervention crossover techniques use intervention scheduling information from parent solutions to direct the offspring produced in the GA crossover process towards more promising areas of a search space. By counting the number of interventions present in parents and adjusting the number of interventions for offspring schedules around it, this allows for highly fit solutions to be found in less fitness function evaluations. The validity of these novel approaches are illustrated through comparison with conventional GA crossover approaches for optimisation of intervention schedules of bio-control application in mushroom farming and cancer chemotherapy treatment. These involve optimally scheduling the application of a bio-control agent to combat pests in mushroom farming and optimising the timing and dosage strength of cancer chemotherapy treatments to maximise their effectiveness. This work demonstrates that significant advantages are gained in terms of both fitness function evaluations required and fitness scores found using the proposed approaches when compared with traditional GA crossover approaches for the production of optimal control schedules.

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35

Reif, Jochen. "Assessing the genetic diversity in crops with molecular markers theory and experimental results with CIMMYT wheat and maize elite germplasm and genetic resources /". [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11406735.

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36

Gutfraind, Alexander. "Error-Tolerant Coding and the Genetic Code". Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2913.

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The following thesis is a project in mathematical biology building upon the so-called "error minimization hypothesis" of the genetic code. After introducing the biological context of this hypothesis, I proceed to develop some relevant information-theoretic ideas, with the overall goal of studying the structure of the genetic code. I then apply the newfound understanding to an important question in the debate about the origin of life, namely, the question of the temperatures in which the genetic code, and life in general, underwent their early evolution.

The main advance in this thesis is a set of methods for calculating the primordial evolutionary pressures that shaped the genetic code. These pressures are due to genetic errors, and hence the statistical properties of the errors and of the genome are imprinted in the statistical properties of the code. Thus, by studying the code it is possible to reconstruct, to some extent, the primordial error rates and the composition of the primordial genome. In this way, I find evidence that the fixation of the genetic code occurred in organisms which were not thermophiles.

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37

Borairi, M. "Genetic design of tunable digital set-point tracking controllers for linear multivariable plants". Thesis, University of Salford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262674.

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38

Galal, Ushma. "The statistical theory underlying human genetic linkage analysis based on quantitative data from extended families". Thesis, University of the Western Cape, 2010. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_2684_1361989724.

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Traditionally in human genetic linkage analysis, extended families were only used in the analysis of dichotomous traits, such as Disease/No Disease. For quantitative traits, analyses initially focused on data from family trios (for example, mother, father, and child) or sib-pairs. Recently however, there have been two very important developments in genetics: It became clear that if the disease status of several generations of a family is known and their genetic information is obtained, researchers can pinpoint which pieces of genetic material are linked to the disease or trait. It also became evident that if a trait is quantitative (numerical), as blood pressure or viral loads are, rather than dichotomous, one has much more power for the same sample size. This led to the
development of statistical mixed models which could incorporate all the features of the data, including the degree of relationship between each pair of family members. This is necessary because a parent-child pair definitely shares half their genetic material, whereas a pair of cousins share, on average, only an eighth. The statistical methods involved here have however been developed by geneticists, for their specific studies, so there does not seem to be a unified and general description of the theory underlying the methods. The aim of this dissertation is to explain in a unified and statistically comprehensive manner, the theory involved in the analysis of quantitative trait genetic data from extended families. The focus is on linkage analysis: what it is and what it aims to do.
There is a step-by-step build up to it, starting with an introduction to genetic epidemiology. This includes an explanation of the relevant genetic terminology. There is also an application section where an appropriate human genetic family dataset is analysed, illustrating the methods explained in the theory sections.

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39

Mok, Tsz-kin, i 莫子建. "Modeling, analysis and control design for the UPFC with fuzzy theory and genetic algorithm application". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224969.

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40

Winkleblack, Scott Kenneth swinkleb. "ReGen: Optimizing Genetic Selection Algorithms for Heterogeneous Computing". DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1236.

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GenSel is a genetic selection analysis tool used to determine which genetic markers are informational for a given trait. Performing genetic selection related analyses is a time consuming and computationally expensive task. Due to an expected increase in the number of genotyped individuals, analysis times will increase dramatically. Therefore, optimization efforts must be made to keep analysis times reasonable. This thesis focuses on optimizing one of GenSel’s underlying algorithms for heterogeneous computing. The resulting algorithm exposes task-level parallelism and data-level parallelism present but inaccessible in the original algorithm. The heterogeneous computing solution, ReGen, outperforms the optimized CPU implementation achieving a 1.84 times speedup.

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41

Mondraǵon, Palomino Octavio. "A numerical study of the effects of multiplicative noise on a supercritical delay induced Hopf bifurcation in a gene expression model /". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101627.

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In the context of gene expression, we proposed a nonlinear stochastic delay differential equation as a mathematical model to study the effects of extrinsic noise on a delay induced Hopf bifurcation. We envisaged its direct numerical resolution. Following the example of the noisy oscillator, we first solved a linearized version of the equation, close to the Hopf bifurcation. The numerical scheme used is a combination of a standard algorithm to solve a deterministic delay differential equation and a stochastic Euler scheme. From our calculations we verified that the deterministic behaviour is fully recovered. For the stochastic case, we found that our solution is qualitatively accurate, in the sense that the noise induced shift in the critical value a, follows the trend the known analytic results predict. However, our numerical solution systematically overestimates the value of the shift. This is explained because the accuracy in the numerical estimation of the decay rate of a solution towards the stationary state value is a function of the control parameter a. We believe the mismatch between the numerical solution and the analytic results is due to a lack of convergence of our scheme, rather than to lack of accuracy. As our numerical scheme is an hybrid, the convergence problem can be improved, both at the deterministic and at the stochastic parts of the scheme. In this work we left our numerical results on the nonlinear case out, because before proceeding to the investigation of the nonlinear equation, the convergence must be assured in the linear case.

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42

姚濬帆 i Chun-fan Yiu. "Evolutionary synthesis of time-optimal control policies". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2002. http://hub.hku.hk/bib/B29866170.

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Yiu, Chun-fan. "Evolutionary synthesis of time-optimal control policies /". Hong Kong : University of Hong Kong, 2002. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25151058.

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44

Ceyhan, Ozlem. "Aerodynamic Design And Optimization Of Horizontal Axis Wind Turbines By Using Bem Theory And Genetic Algorithm". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610024/index.pdf.

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An aerodynamic design and optimization tool for wind turbines is developed by using both Blade Element Momentum (BEM) Theory and Genetic Algorithm. Turbine blades are optimized for the maximum power production for a given wind speed, a rotational speed, a number of blades and a blade radius. The optimization variables are taken as a fixed number of sectional airfoil profiles, chord lengths, and twist angles along the blade span. The airfoil profiles and their aerodynamic data are taken from an airfoil database for which experimental lift and drag coefficient data are available. The BEM analysis tool developed is first validated with the experimental data for low wind speeds. A 100 kW wind turbine, which is used in the validation, is then optimized. As a result of the optimization, the power production is improved by 40 to 80 percent. The optimization methodology is then employed to design a 1MW wind turbine with a 25m radius.

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45

Robinson, Clare. "Multi-objective optimisation of polynomial models for time series prediction using genetic algorithms and neural networks". Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.300079.

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46

Tsai, Huai-Jie, i 蔡懷介. "Assets Prices under Genetic Algorithm and Prospect Theory". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/62053230313292736982.

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碩士
國立高雄應用科技大學
金融資訊研究所
97
Efficient market hypothesis of the traditional finance assume the investors are reason in the market, but many scholars detect the phenomenons violate Efficient market hypothesis and the capital asset pricing model (CAPM). The phenomenons like January Effect, Small Firm Effect and the weekend effect. Therefore the behavioral finance replace the rational behavior in the traditional finance with normal behavior. Kahneman and Tverskey(1979) publish the Prospect Theory, the purpose explains that the investors make risk policy with different risk Preference in the different situations. But behavioral finance or traditional finance can’t break away that the first is the fixed model after proving and analysis in the study flow. My study uses the Euler equation which extends from Merton (1973) publish the intertemporal capital asset pricing model (ICAPM) then optimal utility model(Genetic Algorithm-Utility function, GA-U) uses genetic algorithm(GA) that has the ability to search the generally optimization and the return ratio data of listed companies. The intention is investor’s intertemporal marginal utility to be maximum. The compared model is Mao-wei Hung and Jr-yan Wang utility model (2005) which uses two type data group in the analysis. The first type data group are daily return which include fifty-one listed companies on the Taiwan stock market. The second type data group are daily return which include N.Y. S&P 500 Stock Index, D.J. Indus. Average(NY) and TSE Weigh.Stk.Indx-end M. Each type of data group be used to do three type of experiments which are expected value of marginal utility, error of forecast next return ratio and right way of forecast next return ratio. Last we can find GA-U to use test one year of train two year which has stable and not bad forecast ability in the first type data group. We can’t find the forecast ability of GA-U striking to win HW-U in the data of USA, but the forecast ability of GA-U has better result than HW-U in the data of Taiwan.

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47

王正苡. "Multiobjective optimization using genetic algorithm and fuzzy theory". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/27561126267315445765.

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48

Angeles-Albores, David. "A Theory of Genetic Analysis Using Transcriptomic Phenotypes". Thesis, 2019. https://thesis.library.caltech.edu/11243/1/AngelesAlbores_David_2019.pdf.

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This thesis deals with the conceptual and computational framework required to use transcriptomes as effective phenotypes for genetic analysis. I demonstrate that there are powerful theoretical reasons why Batesonian epistasis should feature prominently in transcriptional phenotypes. I also show how to compute and interpret the aggregate statistics for transcriptome-wide epistasis and transcriptome-wide dominance using whole-organism transcriptomic profiles of C. elegans mutants. Finally, I developed the WormBase Enrichment Suite for enrichment analysis of genomic data.

RNA-seq as a tool has enormous potential because it relies on protocols that are fast, simple and increasingly cheap. In spite of their potential, transcriptomes have seen their use largely limited to single-factor experiments. Even when many transcriptomes are collected, the main analytic approach is to apply clustering algorithms that correlate responses but do not have any power to identify causal mechanisms.

I demonstrate that if a complete genetic experimental design is used (in the form of a full two-factor matrix), transcriptomes can establish genetic interactions between a pair of genes without the need for clustering algorithms. Surprisingly, when we performed epistasis analyses of hypoxia pathway mutants in C. elegans we did not simply observe a generalized epistatic interaction between the mutants. In fact, the transcriptomes recapitulated the same Batesonian epistatic relationship that had been observed using classical phenotypes. In other words, we observed that the transcriptomic phenotype of one gene can be masked by the transcriptomic phenotype of a second gene, such that a double mutant of these two genes has exactly the same phenotype as a single mutant of the epistatic gene. Motivated by this observation, we developed methods to recognize and interpret Batesonian epistasis at the transcriptomic level. This method relies on the calculation of a single aggregate coefficient that we named the transcriptome-wide epistasis coefficient.

The observation that Batesonian epistasis could be reproduced on a transcriptomic level was surprising. To explain how transcriptome-wide epistasis can arise, I studied a simplified model of transcriptional regulation using statistical mechanics. These studies demonstrate that epistatic analysis is equivalent to a perturbative analysis of the partition function of a promoter. Moreover, these studies revealed that a sufficient condition for Batesonian epistasis to occur is if the two genes encode variables that are transformed and multiplied together to form an effective single compound variable. Finally, these studies clearly demonstrate the connection between statistical (or generalized) epistasis and Batesonian epistasis and establish a physical basis for genetic logic.

Genetic analyses of gene functional units can also be carried out using allelic series in tandem with complementation (also known as dominance) tests. I developed a statistical coefficient known as transcriptome-wide dominance to enable analyses of allelic series using expression profiles. A crucial aspect of allelic series is the ability to enumerate the independent phenotypes associated with an arbitrary set of alleles. I developed the concept of phenotypic classes as a transcriptomic analogue of classical phenotypes for this purpose. Briefly, a phenotypic class is a set of transcripts that are differentially expressed in a specific set of genotypes. Thus, an allelic series consisting of two mutant alleles (and a wild-type) can at most result in 7 phenotypic classes. However, some of these phenotypic classes may be artifactual as a result of the significant false positive and false negative rates that are associated with RNA-seq. I developed a simple algorithm that tries to identify phenotypic classes that are artifactual, though often these classes may also be identified through a critical evaluation of their biological implications. I applied these concepts to a small allelic series of the dpy-22 gene, which encodes a Mediator subunit in C. elegans, and identified 3–4 functional units along with their sequence requirements.

Finally, I developed the WormBase Enrichment Suite by implementing a hypergeometric test on the tissue, gene and phenotype ontology for C. elegans. The importance of this tool derives mainly from its integration to WormBase, the repository of all C. elegans knowledge, which means that the databases that are tested will undergo continuous improvement and curation, and thus will yield the most accurate results.

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49

Mishra, Bineet, i Rakesh Kumar Patnaik. "Genetic Algorithm and its Variants: Theory and Applications". Thesis, 2009. http://ethesis.nitrkl.ac.in/199/1/FINAL_REPORT.pdf.

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The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories proposed by Charles Darwin. The Algorithm functions on three basic genetic operators of selection, crossover and mutation. Based on the types of these operators GA has many variants like Real coded GA, Binary coded GA, Sawtooth GA, Micro GA, Improved GA, Differential Evolution GA. This paper discusses a few of the forms of GA and applies the techniques to the problem of Function optimization and System Identification. The paper makes a comparative analysis of the advantages and disadvantages of the different types of GA. The computer simulations illustrate the results. It also makes a comparison between the GA technique and Incremental LMS algorithm for System Identification.

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50

Huang, Tian-Kang, i 黃添康. "Fuzzy Neural network Combining Theory of Mechanics and Genetic Fuzzy Neural network Combining Theory of Mechanics and Genetic Algorithms for Exploration of Breast Tumor". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/83c734.

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碩士
國立臺北科技大學
自動化科技研究所
97
Breast cancer has been ranked the fourth leading cause of cancer deaths for females in Taiwan for the past five years in a row. It has also been reported that one of every eight women in the USA was affected by the breast cancer and the breast cancer had become the first cause of death for the female populations in the developed countries. While mammography has been the most widely used approach for early detection of breast cancers, the high false positive rate for the breast lesions using mammography has led to a high percentage of unnecessary biopsy referral, which is an expensive and disconcerting procedure. Recently, many studies have shown that it has a very high potential to use the breast sonography to reduce the number of biopsies. Nevertheless, these studies were usually made by highly experienced medical doctors and the sonographic features suggested by them were very dependent on the interpretation of the ultrasound images. First a membership function with adaptive spans under the fuzzy orthogonal condition is established to define the fuzzy set. Then using the genetic algorithms, the modulated membership factors(MMF) of membership function with adaptive span are learned automatically, so that the fuzzy inference system of automatic weighing is constructed. In this study, the fuzzy neural network is divided to premise and consequence, to establish the fuzzy modeling.

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