Teses / dissertações: "Intelligence – genetics"

1

Avgan, Nesli. "The genetic basis of human cognition: Intelligence, learning and memory". Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/122903/1/Nesli_Avgan_Thesis.pdf.

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The complex and highly polygenic traits of intelligence, learning and memory are fundamental functions of neurocognition. Despite improvements in neurogenetics, our knowledge on the genetic architecture of these functions remains poorly understood. This research investigated the contribution of genetic variation to cognitive performance variability in relation to intelligence, learning and memory using a well-defined, healthy and unrelated cohort via a gene-centric and a genome-wide approach. Results of this study validated several previously identified genes, provided new knowledge on various cognitive traits, and identified several novel regions for future studies that may have consequences for managing disorders involving cognitive impairments.

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Prabhakar, Nachiketh. "Deep Learning To Improve Hi-C Data". Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1562582435975614.

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3

Ong, Vy Quoc. "Subgroup Analysis of Patients with Hepatocellular Carcinoma| A Quest for Statistical Algorithms for Tissue Classification Problem". Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10840510.

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Hepatocellular carcinoma (HCC) is the most common type of liver cancer. This type of cancer has been observed with prevalence as the third leading cause of death from cancer worldwide and as the ninth leading cause of cancerous mortality in the United States. People with hepatitis B or C are considered to be at high risk for this kind of cancer. Remarkably poor prognostic HCC patients with low survival rates commonly possess intra-hepatic metastases that are either tumor thrombi in the portal vein or intra-hepatic spread. It is uncommon for them to die of extra-hepatic metastases. Therefore, identifying metastatic HCC has become vital and clinically challenging in efforts of timely therapeutic intervention to improve the survival rate of patients who suffer from this disease.

To date, studies that look for an accurate molecular profiling model have been developed to identify these patients in advance for a better treatment or intervention. An approach has been to focus on identifying individual candidate genes characterizing metastatic HCC. Another direction has been to find a global genome scale solution by using microarray technology to obtain a gene expression for this carcinoma. Among research following the latter was that developed by Qing-Hai Ye et al., Nature Medicine, Volume 9, Number 4, April 2003. They applied cDNA microarray-based gene expression profiling with compound co-variate predictors for primary HCC, metastatic HCC, and metastasis-free HCC binary classification tasks on a dataset of 87 observations and 9984 features taken from 40 hepatitis B-positive Chinese patients. Notably, a robust 153-gene model was generated to successfully classify tumor-thrombi-in-the-portal-vein samples with metastasis-free samples. However, they admitted distinguishing primary tumor samples from their matched-metastatic lesions were still a challenge. In this molecule signature, a gene named osteopontin, a secreted phosphoprotein, served as the lead gene in diagnosing HCC metastasis.

The analysis is based on the metastatic status of HCC, which is clinically predetermined. However, the validation of the class definition is needed to investigate if the data are sufficient to translate the three classes predefined. We will use some statistical clustering algorithms to validate the class defined. After that, we will conduct variable selection to find markers that are differentially expressed genes among clinical groups validated from this research. Next, using the compound markers found by this research, we will develop a statistical model to predict a new patient’s HCC type for intervention. The generalized performance of the prediction model will be evaluated via a cross-validation test. This study aims to build a highly accurate model that renders a better classification of the fore-mentioned clinical groups of HCC and thus enhances the rate of predicting metastatic patients.

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4

Beiko, Robert G. "Evolutionary computing strategies for the detection of conserved patterns in genomic DNA". Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29009.

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The detection of regulatory sequences in DNA is a challenging problem, especially when considered in the context of whole genomes. The degree of sequence conservation of regulatory protein binding sites is often weak, and the sites are obscured by surrounding intergenic sequence. Since structural interactions are vital for protein-DNA interactions, structural representations of regulatory sites can yield a more accurate model and a better understanding of within-site variability. However, the use of multiple alternative representations of DNA introduces a requirement for novel algorithms that can create and test different combinations of DNA features. The Genetic Algorithm Neural Network (GANN) was designed to identify combinations of patterns that can be used to distinguish between different classes of training sequence. GANN trains a set of artificial neural networks to classify sets of sequence using either backpropagation or a genetic algorithm, and uses an 'outer genetic algorithm' to choose the best inputs from a pool of DNA features that can include sequence, structure, and weight matrix representations. When trained with a subset of upstream sequences from a whole genome, GANN was able to detect patterns such as the Shine-Dalgarno sequence in Escherichia coli K12, and sequences consistent with archaeal promoters in the archaeon Sulfolobus solfataricus P2. The Motif Genetic Algorithm (MGA) constructs motif representations by concatenating minimal units of DNA sequence and structure. This algorithm was used to model conserved patterns in DNA, including the binding sites for E. coli cyclic AMP activated protein (CAP), integration host factor (IHF), and two different promoter types recognized by alternative bacterial sigma factors. The CAP models were used to detect other putative binding sites in upstream regions of the E. coli K12 genome, while attempts to train an accurate model of IHF binding sites revealed an important role for structural representations in motif modeling.

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5

Takane, Marina. "Inference of gene regulatory networks from large scale gene expression data". Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.

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With the advent of the age of genomics, an increasing number of genes have been identified and their functions documented. However, not as much is known of specific regulatory relations among genes (e.g. gene A up-regulates gene B). At the same time, there is an increasing number of large-scale gene expression datasets, in which the mRNA transcript levels of tens of thousands of genes are measured at a number of time points, or under a number of different conditions. A number of studies have proposed to find gene regulatory networks from such datasets. Our method is a modification of the continuous-time neural network method of Wahde & Hertz [25, 26]. The genetic algorithm used to update weights was replaced with Levenberg-Marquardt optimization. We tested our method on artificial data as well as Spellman's yeast cell cycle data [22]. Results indicated that this method was able to detect salient regulatory relations between genes.

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6

Смірнов, Олег Ювеналійович, Олег Ювенальевич Смирнов e Oleh Yuvenaliiovych Smirnov. "Генетична складова інтелекту". Thesis, Видавництво СумДУ, 2011. http://essuir.sumdu.edu.ua/handle/123456789/14286.

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7

Yeo, Ronald A., Sephira G. Ryman, den Heuvel Martijn P. van, Reus Marcel A. de, Rex E. Jung, Jessica Pommy, Andrew R. Mayer et al. "Graph Metrics of Structural Brain Networks in Individuals with Schizophrenia and Healthy Controls: Group Differences, Relationships with Intelligence, and Genetics". Cambridge University Press, 2016. https://tud.qucosa.de/id/qucosa%3A70691.

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Objectives: One of the most prominent features of schizophrenia is relatively lower general cognitive ability (GCA). An emerging approach to understanding the roots of variation in GCA relies on network properties of the brain. In this multi-center study, we determined global characteristics of brain networks using graph theory and related these to GCA in healthy controls and individuals with schizophrenia. Methods: Participants (N = 116 controls, 80 patients with schizophrenia) were recruited from four sites. GCA was represented by the first principal component of a large battery of neurocognitive tests. Graph metrics were derived from diffusion-weighted imaging. Results: The global metrics of longer characteristic path length and reduced overall connectivity predicted lower GCA across groups, and group differences were noted for both variables. Measures of clustering, efficiency, and modularity did not differ across groups or predict GCA. Follow-up analyses investigated three topological types of connectivity—connections among high degree “rich club” nodes, “feeder” connections to these rich club nodes, and “local” connections not involving the rich club. Rich club and local connectivity predicted performance across groups. In a subsample (N = 101 controls, 56 patients), a genetic measure reflecting mutation load, based on rare copy number deletions, was associated with longer characteristic path length. Conclusions: Results highlight the importance of characteristic path lengths and rich club connectivity for GCA and provide no evidence for group differences in the relationships between graph metrics and GCA.

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8

Penke, Lars. "Neuroscientific approaches to general intelligence and cognitive ageing". Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2011. http://dx.doi.org/10.18452/13979.

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Nach einem ausführlichem Überblick über den Kenntnisstand der Genetik und Neurowissenschaft von allgemeiner Intelligenz und einer methodischen Anmerkung zur Notwendigkeit der Berücksichtigung latenter Variablen in den kognitiven Neurowissenschaften am Beispiel einer Reanalyse publizierter Ergebnisse wir das am besten etablierte Gehirnkorrelat der Intelligenz, die Gehirngröße, aus evolutionsgenetischer Perspektive neu betrachtet. Schätzungen des Koeffizienten additiv-genetischer deuten an, dass es keine rezente direktionale Selektion auf Gehirngröße gegeben hat, was ihre Validität als Proxy für Intelligenz in evolutionären Studien in Frage stellt. Stattdessen deuten Korrelationen der Gesichtssymmetrie älterer Männer mit Intelligenz und Informationsverarbeitungsgeschwindigkeit an, dass organismusweite Entwicklungsstabilität eine wichtige Grundlage von unterschieden in kognitiven Fähigkeiten sein könnte. Im zweiten Teil dieser Arbeit geht es vornehmlich um die Alterung kognitiver Fähigkeiten, beginnend mit einem allgemeinen Überblick. Daten einer Stichprobe von über 130 Individuen zeigen dann, dass die Integrität verschiedener Nervenbahnen im Gehirn hoch korreliert, was die Extraktion eines Generalfaktors der Traktintegrität erlaubt, der mit Informationsverarbeitungsgeschwindigkeit korreliert. Der einzige Trakt mit schwacher Ladung auf diesem Generalfaktor ist das Splenium des Corpus Callosum, welches mit Veränderungen der Intelligenz über 6 Jahrzehnte korreliert und den Effekt des Bet2 adrenergischem Rezeptorgens (ADRB2) auf diese Veränderung mediiert, möglicherweise durch Effekte auf neuronale Komopensationsprozesse. Schließlich wird auf Basis neuer Analyseverfahren für Magnetresonanzdaten gezeigt, dass vermehrte Eiseneinlagerungen im Gehirn, vermutlich Marker für zerebrale Mikroblutungen, sowohl mit lebenslang stabilen Intelligenzunterschieden als auch mit der altersbedingten Veränderung kognitiver Fähigkeiten assoziiert sind.
After an extensive review of what is known about the genetics and neuroscience of general intelligence and a methodological note emphasising the necessity to consider latent variables in cognitive neuroscience studies, exemplified by a re-analysis of published results, the most well-established brain correlate of intelligence, brain size, is revisited from an evolutionary genetic perspective. Estimates of the coefficient of additive genetic variation in brain size suggest that there was no recent directional selection on brain size, questioning its validity as a proxy for intelligence in evolutionary analyses. Instead, correlations of facial fluctuating asymmetry with intelligence and information processing speed in old men suggest that organism-wide developmental stability might be an important cause of individual differences in cognitive ability. The second half of the thesis focuses on cognitive ageing, beginning with a general review. In a sample of over 130 subjects it has then been found that the integrity of different white matter tracts in the brain is highly correlated, allowing for the extraction of a general factor of white matter tract integrity, which is correlated with information processing speed. The only tract not loading highly on this general factor is the splenium of the corpus callosum, which is correlated with changes in intelligence over 6 decades and mediates the effect of the beta2 adrenergic receptor gene (ADRB2) on cognitive ageing, possibly due to its involvement in neuronal compensation processes. Finally, using a novel analytic method for magnetic resonance data, it is shown that more iron depositions in the brain, presumably markers of a history of cerebral microbleeds, are associated with both lifelong-stable intelligence differences and age-related decline in cognitive functioning.

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9

Horstman, Benjamin Philip. "Detecting Epistasis Effect in Genome-Wide Association Studies Based on Permutation Tests and Ensemble Approaches". Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1270577390.

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Thesis (Master of Sciences (Engineering))--Case Western Reserve University, 2010
Department of EECS - Computer and Information Sciences Title from PDF (viewed on 2010-05-25) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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10

Kravchenko, Evgenija. "Association between cognitive measures, global brain surface area, genetics, and screen-time in young adolescents : Estimation of causal inference with machine learning". Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290033.

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Screen media activity such as watching TV and videos, playing video games, and using social media has become a popular leisure activity for children and adolescents. The effect of screen time has been a highly debated topic; however, there is still very little known about it. Using a dataset from the Adolescent Brain Cognitive Development longitudinal study 4 217 young adolescents, that met the requirements, could be retrieved for this thesis project after processing of the data. This thesis project investigated causal order between genetic effect (cognitive performance Polygenic scores (PGSs)), screen time activity, brain morphology (structural Magnetic Resonance Imaging (sMRI) for surface area and cortical thickness), lack of perseverance, and cognitive performance (crystallized IQ) with a machine learning algorithm DirectLiNGAM. A clear correlation between screen media activity and PGS was found for all types of screen time activities but only video games and social media correlated to the global surface area. Furthermore, TV and video seem to affect lack of perseverance, and lack of perseverance, in turn, affects time spent on video games. These findings imply that different types of social media are not as alike as we thought and can affect adolescents differently. Taken together, these findings support previous research on screen media activity's effect on lack of perseverance, brain morphology, and cognitive performance, and propose new causal inference between genetics and screen time. Lastly, the algorithm used in this thesis project inferred reasonable causal orders and can be seen as a very good complement to today's causal modeling.
Skärmaktivitet som att titta på TV och video, spela videospel och använda sociala medier har blivit en populär fritidsaktivitet för barn och ungdomar. Effekten av skärmtid har varit ett mycket debatterat ämne; det finns dock fortfarande mycket lite kunskap om det. Med hjälp av datasetet från Adolescent Brain Cognitive Development långtidsstudien kunde 4 217 ungdomar, som uppfyllde specifika krav, väljas ut för detta avhandlingsprojekt efter bearbetning av datan. Detta avhandlingsprojekt undersökte kausal ordning mellan genetisk effekt (Polygenic scores (PGS) för kognitiv prestation), skärmtidsaktivitet, hjärnmorfologi (strukturell Magnet Resonans Imaging (sMRI) för hjärnans ytarea och hjärnbarks tjocklek), brist på ihärdighet och kognitiv förmåga (kristalliserad IQ) med en maskininlärningsalgoritm DirectLiNGAM. Tydlig korrelation mellan skärmaktivitet och PGS hittades för alla typer av skärmaktiviteter men endast videospel och sociala medier korrelerade till den globala ytarean. Dessutom verkar TV och video påverka brist på ihärdighet och brist på ihärdighet i sin tur påverkar hur mycket tid som spenderas på videospel. Dessa resultat antyder att olika typer av sociala medier inte är så lika som vi trodde och kan påverka ungdomar olika. Sammanlagt stöder dessa upptäckter tidigare forskning om skärmtidseffekt på brist på ihärdighet, hjärnmorfologi och kognitiv förmåga och föreslår en ny kausal inferens mellan genetik och skärmtid. Slutligen ledde algoritmen som användes i detta avhandlingsprojekt fram till rimliga kausala ordningar och kan ses som ett mycket bra komplement till dagens kausala modellering.

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11

Tafreshi, Shabnam. "Cross-Genre, Cross-Lingual, and Low-Resource Emotion Classification". Thesis, The George Washington University, 2021. http://pqdtopen.proquest.com/#viewpdf?dispub=28088437.

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Emotions can be defined as a natural, instinctive state of mind arising from one’s circumstances, mood, and relationships with others. It has long been a question to be answered by psychology that how and what is it that humans feel. Enabling computers to recognize human emotions has been an of interest to researchers since 1990s (Picard et al., 1995). Ever since, this area of research has grown significantly and emotion detection is becoming an important component in many natural language processing tasks. Several theories exist for defining emotions and are chosen by researchers according to their needs. For instance, according to appraisal theory, a psychology theory, emotions are produced by our evaluations (appraisals or estimates) of events that cause a specific reaction in different people. Some emotions are easy and universal, while others are complex and nuanced. Emotion classification is generally the process of labeling a piece of text with one or more corresponding emotion labels. Psychologists have developed numerous models and taxonomies of emotions. The model or taxonomy depends on the problem, and thorough study is often required to select the best model. Early studies of emotion classification focused on building computational models to classify basic emotion categories. In recent years, increasing volumes of social media and the digitization of data have opened a new horizon in this area of study, where emotion classification is a key component of applications, including mood and behavioral studies, as well as disaster relief, amongst many other applications. Sophisticated models have been built to detect and classify emotion in text, but few analyze how well a model is able to learn emotion cues. The ability to learn emotion cues properly and be able to generalize this learning is very important. This work investigates the robustness of emotion classification approaches across genres and languages, with a focus on quantifying how well state-of-the-art models are able to learn emotion cues. First, we use multi-task learning and hierarchical models to build emotion models that were trained on data combined from multiple genres. Our hypothesis is that a multi-genre, noisy training environment will help the classifier learn emotion cues that are prevalent across genres. Second, we explore splitting text (i.e. sentence) into its clauses and testing whether the model’s performance improves. Emotion analysis needs fine-grained annotation and clause-level annotation can be beneficial to design features to improve emotion detection performance. Intuitively, clause-level annotations may help the model focus on emotion cues, while ignoring irrelevant portions of the text. Third, we adopted a transfer learning approach for cross-lingual/genre emotion classification to focus the classifier’s attention on emotion cues which are consistent across languages. Fourth, we empirically show how to combine different genres to be able to build robust models that can be used as source models for emotion transfer to low-resource target languages. Finally, this study involved curating and re-annotating popular emotional data sets in different genres, and annotating a multi-genre corpus of Persian tweets and news, and generating a collection of emotional sentences for a low-resource language, Azerbaijani, a language spoken in the north west of Iran.

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12

Fetter, Karl Christian. "Natural Selection For Disease Resistance In Hybrid Poplars Targets Stomatal Patterning Traits And Regulatory Genes". ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1162.

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The evolution of disease resistance in plants occurs within a framework of interacting phenotypes, balancing natural selection for life-history traits along a continuum of fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant populations connected by gene flow are physiologically limited to evolving along a single axis of the spectrum of the growth-defense trade-off, and strong local selection can purge phenotypic variance from a population or species, making it difficult to detect variation linked to the trade-off. Hybridization between two species that have evolved different growth-defense trade-off optima can reveal trade-offs hidden in either species by introducing phenotypic and genetic variance. Here, I investigated the phenotypic and genetic basis for variation of disease resistance in a set of naturally formed hybrid poplars. The focal species of this dissertation were the balsam poplar (Populus balsamifera), black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from natural populations and clonally replicated in a common garden. Ecophysiology and stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae) were collected. To overcome the methodological bottleneck of manually phenotyping stomata density for thousands of cuticle micrographs, I developed a publicly available tool to automatically identify and count stomata. To identify stomata, a deep con- volutional neural network was trained on over 4,000 cuticle images of over 700 plant species. The neural network had an accuracy of 94.2% when applied to new cuticle images and phenotyped hundreds of micrographs in a matter of minutes. To understand how disease severity, stomata, and ecophysiology traits changed as a result of hybridization, statistical models were fit that included the expected proportion of the genome from either parental species in a hybrid. These models in- dicated that the ratio of stomata on the upper surface of the leaf to the total number of stomata was strongly linked to disease, was highly heritable, and wass sensitive to hybridization. I further investigated the genomic basis of stomata-linked disease variation by performing an association genetic analysis that explicitly incorporated admixture. Positive selection in genes involved in guard cell regulation, immune sys- tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis were identified. Together, my dissertation incorporated advances in image-based phenotyping with evolutionary theory, directed at understanding how disease frequency changes when hybridization alters the genomes of a population.

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13

Char, Kalyani Govinda. "Constructivist artificial intelligence with genetic programming". Thesis, University of Glasgow, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265641.

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14

Qi, Dehu. "Multi-agent systems : integrating reinforcement learning, bidding and genetic algorithms /". free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3060133.

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15

Meuth, Ryan James. "Meta-learning computational intelligence architectures". Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Meuth_09007dcc80722172.pdf.

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Thesis (Ph. D.)--Missouri University of Science and Technology, 2009.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 5, 2010) Includes bibliographical references (p. 152-159).

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16

PICCHIOTTI, NICOLA. "Explainable Artificial Intelligence: an application to complex genetic diseases". Doctoral thesis, Università degli studi di Pavia, 2021. http://hdl.handle.net/11571/1447637.

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17

FORESI, GABRIELE. "Computational Intelligence Techniques for Energy Management in Industrial and Residential Systems". Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/274515.

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Nell’era della digitalizzazione, l’Energy Management (EM) è diventato uno degli argomenti più importanti in sia in ambito industriale che residenziali. L’importanza di risolvere e migliorare alcuni compiti in questa era porta alla necessità di applicare alcuni metodi di intelligenza computazionale (CI) e di intelligenza artificiale (AI). Gli obiettivi e i contributi di questa tesi sono legati all’EM in i) sistemi industriali (con riferimento all’Industria 4.0) agendo sulla progettazione del sistema di controllo e ii) sistemi residenziali dando all’utente un ruolo attivo in uno scenario di flessibilità. Nel primo scenario, l’autore presenta due metodologie CI. Il primo approccio consiste nell’applicare un algoritmo meta-euristico a sistemi industriali ricon- figurabili al fine di trovare un set di parametri del controllore ottimale. Le prestazioni di questa tecnica si basano sulla scelta di un opportuno funzionale da ottimizzare. La seconda metodologia è l’applicazione di una tecnica di rile- vamento della somiglianza tra segnali come supervisore del sistema di controllo. Questo è implementato per superare i limiti della prima metodologia. Entrambi questi approcci sono testati su un sistema reale. Nel secondo scenario, l’autore propone l’applicazione di una tecnica CI volta a prevedere l’utilizzo degli elettrodomestici da parte degli utenti. Queste in- formazioni, condivise con il fornitore di energia, potrebbero essere molto utili per alimentare programmi di flessibilità. I risultati ottenuti mostrano come le prestazioni dell’algoritmo proposto siano migliori degli approcci classici.
In the digitalization era, Energy Management (EM) has become one of the most important topics in both industrial and residential scenarios. The importance of solving and improving some tasks in this epoch leads to the need to apply some Computational Intelligence (CI) and Artificial Intelligence (AI) methods. The objectives and contributions of this thesis are related to the EM in i) industrial systems (with reference to the Industry 4.0) by acting on the control system design and ii) residential systems by giving the user a role active in a flexibility scenario. In the first scenario, the author presents two CI methodologies. The first approach consists of applying a meta-heuristic algorithm to reconfigurable sys- tems in order to find a sub-optimal controller parameter set. The performance of this technique is based on the choice of a suitable function to be optimized. The second methodology is the application of a similarity detection technique as a control system supervisor. This is implemented to overcome the limita- tions of the first methodology. Both of these approaches are tested on a real system. In the second scenario, the author proposes the application of a CI technique aiming to forecast home appliances usage. This information, shared with the energy supplier, is could be very useful to fed flexibility programs. The obtained results show how the performance of the proposed algorithm is better than the classic approaches.

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18

Hobday, Steven. "Artificial intelligence and simulations applied to interatomic potentials". Thesis, Loughborough University, 1998. https://dspace.lboro.ac.uk/2134/33255.

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The interatomic potential is a mathematical model that describes the chemistry occurring at the atomic level. It provides a functional mapping between the atomic nuclei coordinates and the total potential energy of a system. This thesis investigates three aspects of interatomic potentials, the first of which is the simulation of materials at the atomic scale using classical molecular dynamics (MD). Molecular dynamics code is used to follow the evolution of a system of discrete particles through time and is employed here to model the bombardment of fullerite films modified with low dose Argon ion impacts.

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19

Mirina, Alexandra. "Computational approaches for intelligent processing of biomedical data". Thesis, Yeshiva University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3664552.

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The rapid development of novel experimental techniques has led to the generation of an abundance of biological data, which holds great potential for elucidating many scientific problems. The analysis of such complex heterogeneous information, which we often have to deal with, requires appropriate state-of-the-art analytical methods. Here we demonstrate how an unconventional approach and intelligent data processing can lead to meaningful results.

This work includes three major parts. In the first part we describe a correction methodology for genome-wide association studies (GWAS). We demonstrate the existing bias for the selection of larger genes for downstream analyses in GWA studies and propose a method to adjust for this bias. Thus, we effectively show the need for data preprocessing in order to obtain a biologically relevant result. In the second part, building on the results obtained in the first part, we attempt to elucidate the underlying mechanisms of aging and longevity by conducting a longevity GWAS. Here we took an unconventional approach to the GWAS analysis by applying the idea of genetic buffering. Doing this allowed us to identify pairs of genetic markers that play a role in longevity. Furthermore, we were able to confirm some of them by means of a downstream network analysis. In the third and final part, we discuss the characteristics of chronic lymphocytic leukemia (CLL) B-cells and perform clustering analysis based on immunoglobulin (Ig) mutation patterns. By comparing the sequences of Ig of CLL patients and healthy donors, we show that different Ig heavy chain (IGHV) regions in CLL exhibit similarities with different B-cell subtypes of healthy donors, which raised a question about the single origin of CLL cases.

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20

Ozbey, Halil. "A Genetic-based Intelligent Intrusion Detection System". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/2/12606636/index.pdf.

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In this study we address the problem of detecting new types of intrusions to computer systems which cannot be handled by widely implemented knowledge-based mechanisms. The solutions offered by behavior-based prototypes either suffer low accuracy and low completeness or require use data eplaining abnormal behavior which actually is not available. Our aim is to develop an algorithm which can produce a satisfactory model of the target system&rsquo
s behavior in the absence of negative data. First, we design and develop an intelligent and behavior-based detection mechanism using genetic-based machine learning techniques with subsidies in the Bucket Brigade Algorithm. It classifies the possible system states to be normal and abnormal and interprets the abnormal state observations as evidences for the presence of an intrusion. Next we provide another algorithm which focuses on capturing normal behavior of the target system to detect intrusions again by identifying anomalies. A compact and highly complete rule set is generated by continuously inserting observed states as rules into the rule set and combining similar rule pairs in each step. Experiments conducted using the KDD-99 data set have produced fairly good results for both of the algorihtms.

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21

Abu-Alola, Abdulmohsin. "Genetic algorithms for intelligent control system design". Thesis, University of Wolverhampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282507.

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Griffiths, Ian. "Microcontroller implementation of artificial intelligence for autonomous guided vehicles". Thesis, University of Wolverhampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266837.

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Castorina, Giovanni. "Artificial intelligence based hybrid systems for financial forecasting". Thesis, University of the West of England, Bristol, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365146.

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Current research carried out on financial forecasting has highlighted some limitations of classical econometric methods based on the assumption that the investigated time series can be described as stationary stochastic processes with Gaussian probability density functions. Chaotic behaviour, fractal characteristics and non-linear dynamics have been emerging in different aspects of the financial forecasting problem. The objective of this thesis is to take a system level perspective of the financial forecasting problem and to explore a number of approaches to enhance more 'traditional' decision making flows for stock market forecasting, with particular emphasis on stock selection and timing. To achieve this purpose, a number of stock selection and timing computational 'modules' are investigated. From a computational point of view, the investigation performed in this work encompass techniques such as artificial neural networks, genetic algorithms, chaos theory and fractal geometry, as well as more traditional methods such as clustering, screening, ranking, and statistics based models. From a financial data point of view, this research takes advantage of both fundamental and technical information to enhance the stock selection and timing processes and to cover several investment horizons. Three computational modules are proposed. First, a multivariate stock ranking module which uses fundamental information and is optimised through genetic algorithms. Second, a multivariate forecasting module which uses technical information and is based on artificial neural networks. Third, a univariate price time series forecasting module based on artificial neural networks. In addition, an integrated flow that takes advantage of some synergies and complementary properties of the devised modules is proposed. The effectiveness of the developed modules and the viability of the proposed integrated flow are evaluated over a number of investment horizons using (out-of-sample) historical data.

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Wååg, Håkan. "Development of a Framework for Genetic Algorithms". Thesis, Jönköping University, JTH, Computer and Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-11537.

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Genetic algorithms is a method of optimization that can be used tosolve many different kinds of problems. This thesis focuses ondeveloping a framework for genetic algorithms that is capable ofsolving at least the two problems explored in the work. Otherproblems are supported by allowing user-made extensions.The purpose of this thesis is to explore the possibilities of geneticalgorithms for optimization problems and artificial intelligenceapplications.To test the framework two applications are developed that look attwo distinct problems, both of which aim at demonstrating differentparts. The first problem is the so called Travelling SalesmanProblem. The second problem is a kind of artificial life simulator,where two groups of creatures, designated predator and prey, aretrying to survive.The application for the Travelling Salesman Problem measures theperformance of the framework by solving such problems usingdifferent settings. The creature simulator on the other hand is apractical application of a different aspect of the framework, wherethe results are compared against predefined data. The purpose is tosee whether the framework can be used to create useful data forthe creatures.The work showed how important a detailed design is. When thework began on the demonstration applications, things were noticedthat needed changing inside the framework. This led to redesigningparts of the framework to support the missing details. A conclusionfrom this is that being more thorough in the planning, andconsidering the possible use cases could have helped avoid thissituation.The results from the simulations showed that the framework iscapable of solving the specified problems, but the performance isnot the best. The framework can be used to solve arbitrary problemsby user-created extensions quite easily.

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Kuscu, Ibrahim. "Evolutionary generalisation and genetic programming". Thesis, University of Sussex, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285062.

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Wong, King-sau, e 黃敬修. "Improving the performance of lifts using artificial intelligence techniques". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2768295X.

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(Uncorrected OCR) Abstract of thesis entitled Improving the Performance of Lifts Using Artificial Intelligence Techniques submitted by Wong King Sau for the degree of Doctor of Philosophy at the University of Hong Kong in August 2003 An elevator group control system manages multiple elevators to serve hall calls in a building. Most elevator group control systems need to recognize the traffic pattern of the building and then change their control algorithms to improve the efficiency of the elevator system. However, the traffic flow in a building is very difficult to be classified into distinct patterns. Traffic recognition systems can recognize certain traffic patterns, but mixed traffic patterns are difficult to be recognized. The aim of this study was therefore to develop improved duplex elevator group control systems that do not need to recognize the traffic pattern. A fuzzy logic. control unit and genetic algorithms control unit were used. A fuzzy logic control unit integrates with the conventional duplex elevator group control system to improve performance especially in mixed traffic patterns with intermittent heavy traffic demand. This system will send more than one elevator to a floor with heavy demand, . according to the overall passenger traffic conditions in the building. The genetic algorithms control unit divides the building into three zones and assigns an appropriate number of elevators to each zone. The floors covered by each zone are adjusted every five minutes. This control unit optimizes elevator group control by equalizing the number of hall calls in each zone, the total elevator door opening time in each zone, and the number of floors served by each elevator. Both of the control units were tested by a simulator in a computer. The performance of the elevator system is given by indices such as average waiting time, wasted man-hour, and long waiting time percentage. The new performance index "wasted man-hour" indicates the total time spent by passengers in a building waiting for the lift service. Both proposed systems perform better than the conventional duplex control system. (An abstract of 297 words.) ~ Signed _ Wong King Sau
abstract
toc
Mechanical Engineering
Doctoral
Doctor of Philosophy

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Nettleton, David John. "Evolutionary algorithms in artificial intelligence : a comparative study through applications". Thesis, Durham University, 1994. http://etheses.dur.ac.uk/5951/.

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For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system.

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Mohamad, Khalid Y. "Restimulation candidate selection using virtual intelligence". Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1722.

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Thesis (M.S.)--West Virginia University, 2000.
Title from document title page. Document formatted into pages; contains ix, 176 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 64-65).

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Yu, Chen-Ping. "Computational model of MST neuron receptive field and interaction effect for the perception of self-motion /". Online version of thesis, 2008. http://hdl.handle.net/1850/9588.

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

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

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Ushie, Ogri James. "Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13643.

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This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and utilising the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upperfrequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which utilises Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and op-amp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all- pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. High-pass, low-pass, and all- pass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order op-amp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder- Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.

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Wong, Phillip Lee-Ming. "Removing redundancy and reducing fitness evaluation costs in genetic programming : a thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Master of Science in Computer Science /". ResearchArchive@Victoria e-Thesis, 2008. http://hdl.handle.net/10063/541.

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Li, Jin. "FGP : a genetic programming based tool for financial forecasting". Thesis, University of Essex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343550.

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Liu, Fang. "Nature inspired computational intelligence for financial contagion modelling". Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8208.

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Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.

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Surme, Turan. "Application of genetic algorithms to open pit design and scheduling". Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319948.

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Wakelam, Mark. "Intelligent hybrid approach for integrated design". Thesis, University of Nottingham, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263942.

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NARAYANAN, SHRUTHI. "HARDWARE IMPLEMENTATION OF GENETIC ALGORITHM MODULES FOR INTELLIGENT SYSTEMS". University of Cincinnati / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1122909070.

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Chaiyaratana, Nachol. "Neuro-genetic based hybrid frameworks with applications in biomedicine and robotics". Thesis, University of Sheffield, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298963.

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Leigh, Ryan E. "Using genetic algorithms to create believable agents". abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1438914.

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SALACHORIS, GEORGIOS PANAGIOTIS. "Exploiting Artificial Intelligence to build realistic numerical models: A Digital Twin application in Structural Health Monitoring". Doctoral thesis, Università Politecnica delle Marche, 2022. http://hdl.handle.net/11566/295395.

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Structural Health Monitoring ha ricevuto un ampio riconoscimento e sviluppo negli ultimi anni. Sono stati fatti progressi in molti campi, come le tecnologie di rilevamento, le applicazioni e l'elaborazione dei dati. A causa della crescente necessità di preservare gli edifici del patrimonio culturale che sono interessati dall'ambiente multi-pericoloso e in particolare dai terremoti, l'automazione delle tecniche di monitoraggio della salute strutturale è diventata un importante argomento di ricerca. La conservazione del patrimonio culturale richiede la combinazione di indagini in situ e modelli analitici accurati al fine di comprendere e interpretare correttamente l'evidenza empirica al fine di applicare con successo analisi strutturali avanzate e valutare lo stato degli edifici del patrimonio. La Tesi si concentra sui metodi computazionali, sulle tecniche di correlazione e sull'aggiornamento dei modelli con un algoritmo metaeuristico ispirato alla natura indicandone vantaggi e svantaggi. L'applicazione di tutti i componenti si presenta sotto forma di un caso studio in cui il comportamento dinamico della Torre Civica di Ostra viene approfondito mediante un modello numerico dettagliato e calibrato rispetto alle caratteristiche modali sperimentali. L’applicazione si basa sulla procedura automatica di calibrazione mediante un algoritmo genetico metaeuristico basato sulla popolazione e sull'uso dell'apprendimento automatico. Questo passaggio consente di stimare con successo le incertezze dei parametri sconosciuti del materiale, considerando sia un modello comportamentale isotropo che ortotropo per la muratura. I risultati consentono di convalidare la metodologia e stabilire informazioni di base sulle condizioni della struttura insieme a standard di prestazione che serviranno a controllare l'integrità strutturale nel tempo.
Structural Health Monitoring has received wide recognition and development the last years. Advancements have been made in many of the fields, like sensing technologies, applications and data processing. Due to the increasing need for preservation of Cultural Heritage buildings that are affected by the multi hazardous environment and specifically earthquakes, the automatization of Structural Health Monitoring techniques has become an important research subject. The preservation of Cultural Heritage requires the combination of in situ investigations and accurate analytical models in order to understand and correctly interpret the empirical evidence in order to to successfully apply advanced structural analyses and assess the state of Heritage Buildings. The Thesis focuses on the computational methods, correlation techniques and model updating with a nature-inspired metaheuristic algorithm indicating their advantages and drawbacks. The application of all the components comes under the form of a case study where the dynamic behavior of the Civic Tower of Ostra is thoroughly investigated by means of a detailed numerical model and calibrated against the experimental modal features. The pairing is based on the automatic procedure of calibration by a metaheuristic population-based genetic algorithm and use of machine learning. This step allows to successfully estimate the uncertainties of the unknown material parameters, considering both an isotropic and an orthotropic behavioral model for masonry. The results enable to validate the methodology and establish baseline information of the condition of the structure along with performance standards that will serve to control the structural integrity over time.

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Heaton, Jeff. "Automated Feature Engineering for Deep Neural Networks with Genetic Programming". Thesis, Nova Southeastern University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10259604.

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Feature engineering is a process that augments the feature vector of a machine learning model with calculated values that are designed to enhance the accuracy of a model's predictions. Research has shown that the accuracy of models such as deep neural networks, support vector machines, and tree/forest-based algorithms sometimes benefit from feature engineering. Expressions that combine one or more of the original features usually create these engineered features. The choice of the exact structure of an engineered feature is dependent on the type of machine learning model in use. Previous research demonstrated that various model families benefit from different types of engineered feature. Random forests, gradient-boosting machines, or other tree-based models might not see the same accuracy gain that an engineered feature allowed neural networks, generalized linear models, or other dot-product based models to achieve on the same data set.

This dissertation presents a genetic programming-based algorithm that automatically engineers features that increase the accuracy of deep neural networks for some data sets. For a genetic programming algorithm to be effective, it must prioritize the search space and efficiently evaluate what it finds. This dissertation algorithm faced a potential search space composed of all possible mathematical combinations of the original feature vector. Five experiments were designed to guide the search process to efficiently evolve good engineered features. The result of this dissertation is an automated feature engineering (AFE) algorithm that is computationally efficient, even though a neural network is used to evaluate each candidate feature. This approach gave the algorithm a greater opportunity to specifically target deep neural networks in its search for engineered features that improve accuracy. Finally, a sixth experiment empirically demonstrated the degree to which this algorithm improved the accuracy of neural networks on data sets augmented by the algorithm's engineered features.

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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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Petrill, Stephen Anthony. "The phenotypic and genetic relationships among school-related temperament, intelligence, and scholastic achievement". Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1057952905.

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Mitchell, Sophia. "A Cascading Fuzzy Logic Approach for Decision Making in Dynamic Applications". University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037866.

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Swift, Adam Glen. "Mapping posthuman discourse and the evolution of living information". Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16439/1/Adam_Swift_Thesis.pdf.

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The discourse that surrounds and constitutes the post-human emerged as a response to earlier claims of an essential or universal human or human nature. These discussions claim that the human is a discursive construct that emerges from various configurations of nature, embodiment, technology, and culture, configurations that have also been variously shaped by the forces of social history. And in the absence of an essential human figure, post-human discourses suggest that there are no restrictions or limitations on how the human can be reconfigured. This axiom has been extended in light of a plethora of technological reconfigurations and augmentations now potentially available to the human, and claims emerge from within this literature that these new technologies constitute a range of possibilities for future human biological evolution. This thesis questions the assumption contained within these discourses that technological incursions or reconfigurations of the biological human necessarily constitute human biological or human social evolution by discussing the role the evolution theories plays in our understanding of the human, the social, and technology. In this thesis I show that, in a reciprocal process, evolution theory draws metaphors from social institutions and ideologies, while social institutions and ideologies simultaneously draw on metaphors from evolution theory. Through this discussion, I propose a form of evolution literacy; a tool, I argue, is warranted in developing a sophisticated response to changes in both human shape and form. I argue that, as a whole, our understanding of evolution constitutes a metanarrative, a metaphor through which we understand the place of the human within the world; it follows that historical shifts in social paradigms will result in new definitions of evolution. I show that contemporary evolution theory reflects parts of the world as codified informatic systems of associated computational network logic through which the behaviour of participants is predefined according to an evolved or programmed structure. Working from within the discourse of contemporary evolution theory I develop a space through which a version of the post-human figure emerges. I promote this version of the post-human as an Artificial Intelligence computational programme or autonomous agent that, rather than seeking to replace, reduce or deny the human subject, is configured as an exosomatic supplement to and an extension of the biological human.

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Swift, Adam Glen. "Mapping posthuman discourse and the evolution of living information". Queensland University of Technology, 2006. http://eprints.qut.edu.au/16439/.

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The discourse that surrounds and constitutes the post-human emerged as a response to earlier claims of an essential or universal human or human nature. These discussions claim that the human is a discursive construct that emerges from various configurations of nature, embodiment, technology, and culture, configurations that have also been variously shaped by the forces of social history. And in the absence of an essential human figure, post-human discourses suggest that there are no restrictions or limitations on how the human can be reconfigured. This axiom has been extended in light of a plethora of technological reconfigurations and augmentations now potentially available to the human, and claims emerge from within this literature that these new technologies constitute a range of possibilities for future human biological evolution. This thesis questions the assumption contained within these discourses that technological incursions or reconfigurations of the biological human necessarily constitute human biological or human social evolution by discussing the role the evolution theories plays in our understanding of the human, the social, and technology. In this thesis I show that, in a reciprocal process, evolution theory draws metaphors from social institutions and ideologies, while social institutions and ideologies simultaneously draw on metaphors from evolution theory. Through this discussion, I propose a form of evolution literacy; a tool, I argue, is warranted in developing a sophisticated response to changes in both human shape and form. I argue that, as a whole, our understanding of evolution constitutes a metanarrative, a metaphor through which we understand the place of the human within the world; it follows that historical shifts in social paradigms will result in new definitions of evolution. I show that contemporary evolution theory reflects parts of the world as codified informatic systems of associated computational network logic through which the behaviour of participants is predefined according to an evolved or programmed structure. Working from within the discourse of contemporary evolution theory I develop a space through which a version of the post-human figure emerges. I promote this version of the post-human as an Artificial Intelligence computational programme or autonomous agent that, rather than seeking to replace, reduce or deny the human subject, is configured as an exosomatic supplement to and an extension of the biological human.

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Nolle, Lars. "Application of computation intelligence to optimisation problems in the hot rolling of wide steel strip". Thesis, Open University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301882.

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Johnson, Clayton Matthew. "A grammar-based technique for genetic search and optimization". W&M ScholarWorks, 1996. https://scholarworks.wm.edu/etd/1539623893.

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The genetic algorithm (GA) is a robust search technique which has been theoretically and empirically proven to provide efficient search for a variety of problems. Due largely to the semantic and expressive limitations of adopting a bitstring representation, however, the traditional GA has not found wide acceptance in the Artificial Intelligence community. In addition, binary chromosones can unevenly weight genetic search, reduce the effectiveness of recombination operators, make it difficult to solve problems whose solution schemata are of high order and defining length, and hinder new schema discovery in cases where chromosome-wide changes are required.;The research presented in this dissertation describes a grammar-based approach to genetic algorithms. Under this new paradigm, all members of the population are strings produced by a problem-specific grammar. Since any structure which can be expressed in Backus-Naur Form can thus be manipulated by genetic operators, a grammar-based GA strategy provides a consistent methodology for handling any population structure expressible in terms of a context-free grammar.;In order to lend theoretical support to the development of the syntactic GA, the concept of a trace schema--a similarity template for matching the derivation traces of grammar-defined rules--was introduced. An analysis of the manner in which a grammar-based GA operates yielded a Trace Schema Theorem for rule processing, which states that above-average trace schemata containing relatively few non-terminal productions are sampled with increasing frequency by syntactic genetic search. Schemata thus serve as the "building blocks" in the construction of the complex rule structures manipulated by syntactic GAs.;As part of the research presented in this dissertation, the GEnetic Rule Discovery System (GERDS) implementation of the grammar-based GA was developed. A comparison between the performance of GERDS and the traditional GA showed that the class of problems solvable by a syntactic GA is a superset of the class solvable by its binary counterpart, and that the added expressiveness greatly facilitates the representation of GA problems. to strengthen that conclusion, several experiments encompassing diverse domains were performed with favorable results.

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Sozio, John Charles. "Intelligent Parameter Adaptation for Chemical Processes". Thesis, Virginia Tech, 1999. http://hdl.handle.net/10919/34089.

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Reducing the operating costs of chemical processes is very beneficial in decreasing a company's bottom line numbers. Since chemical processes are usually run in steady-state for long periods of time, saving a few dollars an hour can have significant long term effects. However, the complexity involved in most chemical processes from nonlinear dynamics makes them difficult processes to optimize. A nonlinear, open-loop unstable system, called the Tennessee Eastman Chemical Process Control Problem, is used as a test-bed problem for minimization routines. A decentralized controller is first developed that stabilizes the plant to set point changes and disturbances. Subsequently, a genetic algorithm calculates input parameters of the decentralized controller for minimum operating cost performance. Genetic algorithms use a directed search method based on the evolutionary principle of "survival of the fittest". They are powerful global optimization tools; however, they are typically computationally expensive and have long convergence times. To decrease the convergence time and avoid premature convergence to a local minimum solution, an auxiliary fuzzy logic controller was used to adapt the parameters of the genetic algorithm. The controller manipulates the input and output data through a set of linguistic IF-THEN rules to respond in a manner similar to human reasoning. The combination of a supervisory fuzzy controller and a genetic algorithm leads to near-optimum operating costs for a dynamically modeled chemical process.
Master of Science

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