Dissertations / Theses on the topic 'black box'
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Shuler, Ryan N. "Black box /." Online version of thesis, 2009. http://hdl.handle.net/1850/9735.
Full textLongo-Capobianco, Samuel John. "Black box [1]." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1585583128274337.
Full textBausch, Amanda L. "Black Box Warning." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3814.
Full textArlt, Stephan [Verfasser], and Andreas [Akademischer Betreuer] Podelski. "Program analysis and black-box GUI testing = Program Analysis und Black-box GUI Testing." Freiburg : Universität, 2014. http://d-nb.info/1123479232/34.
Full textWetzel, Matthias. "Document driven black box testing." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11144258.
Full textOliveira, Ivo Fagundes David de. "Optimal black-box sequential searching." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/EABA-98VHPQ.
Full textEsta dissertação constrói algoritmos de busca de raiz e de busca de máximos, ótimos em um sentido estatístico, e compara os métodos estatisticamente ótimos com as já conhecidas estratégias mini-maximais. A fim de construir o chamado método estatístico, novos resultados na área de probabilidade, capazes de determinar a probabilidade de f(x) = y sobre um conjunto pré-determinado de funções, são apresentados.
Torregrosa, Rivero Daniel. "Black-box interactive translation prediction." Doctoral thesis, Universidad de Alicante, 2018. http://hdl.handle.net/10045/77110.
Full textHussain, Jabbar. "Deep Learning Black Box Problem." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-393479.
Full textFrancescato, Riccardo <1993>. "Efficient Black-box JTAG Discovery." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/12266.
Full textEriksson, Josephine, and Sophie Fredén. "The opening of the black box." Thesis, Halmstad University, School of Business and Engineering (SET), 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-2549.
Full textThe purpose of this thesis was to open up the black boxed TMT process by examining the interaction between TMT members using cognitive and demographic diversity variables, and to see how organisational performance could be affected by the process. By opening the process, a model of the process was developed, which can be tested in further research. The major findings are that there are some aspects that stand out; the CEO and the functional responsibilities that influence the process. Further, the integration within the TMT is not that high, so the upper echelon theory should not be used without considerations on studies where composition is related to organisational performance. These have shown to influence performance in different ways. The functional responsibility has shown to create subgroups that practice problem solving and decision making more frequent than the TMT hence also communicate more.
Stolz, Nolan Ryan. "The touch : a black box opera /." view abstract or download file of text, 2006. http://proquest.umi.com/pqdweb?did=1394663551&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.
Full textVenger, Adam. "Black-box analýza zabezpečení Wi-Fi." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445533.
Full textHosein, Anesa. "Students' approaches to mathematical tasks using software as a black-box, glass-box or open-box." Thesis, Open University, 2009. http://oro.open.ac.uk/22482/.
Full textMena, Roldán José. "Modelling Uncertainty in Black-box Classification Systems." Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/670763.
Full textLa tesis propone un método para el cálculo de la incertidumbre asociada a las predicciones de APIs o librerías externas de sistemas de clasificación.
Kell, Stephen Roger. "Black-box composition of mismatched software components." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610557.
Full textAnthony, Tim. "On the Topic of Unconstrained Black-Box Optimization with Application to Pre-Hospital Care in Sweden : Unconstrained Black-Box Optimization." Thesis, Umeå universitet, Institutionen för fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-185718.
Full textI denna rapport undersöks teorin och tillämpningarna av diverse blackbox optimeringsmetoder. Mer specifikt så har vi tittat på två familjer av algoritmer, descentmetoder och responsytmetoder (nära besläktade med tillitsregionmetoder). Vi tittar också på möjligheterna att använda en dimensionreduktionsteknik som kallas active subspace som använder samplade gradienter för att göra descentmetoderna mer lämpade för högdimensionella problem, vilket visade sig vara mest effektivt när datat har en struktur där ändringar i endast en riktning har effekt på responsvärdet. Slutligen användes optimeringsmetoderna på ett verkligt problem från sjukhusvården, där målet var att minimera svarstiderna för ambulansutryckningar i Umeå kommun genom att ändra ambulanspositionerna. Innan metoderna tillämpades på det verkliga ambulansproblemet genomfördes också en simuleringsstudie på syntetiskt data. Detta för att hitta styrkorna och svagheterna hos de olika modellerna genom att undersöka hur dem hanterar ett flertal testfunktioner under olika nivåer av brus. Resultaten visade att vi kunde förbättra ambulansernas responstider över flera olika prestandamått jämfört med responstiderna för de nuvarande ambulanspositionerna. Detta indikerar att det finns förändringar av positioneringen av ambulanser som kan gynna den pre-hospitala vården inom Umeå kommun. Dock, eftersom modellerna i denna rapport hittar lokala och inte globala optimala punkter kan det fortfarande finnas ännu bättre ambulanspositioner som kan förbättra responstiden ytterligare.
Bruns, Morgan Chase. "Propagation of Imprecise Probabilities through Black Box Models." Thesis, Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10553.
Full textYalcinkaya, Sukru. "Black Box Groups And Related Group Theoretic Constructions." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608546/index.pdf.
Full texts ``Classical Involution Theorem'
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as a model in the final recognition algorithm and we propose an algorithm which constructs all root SL_2(q)-subgroups corresponding to the nodes in the extended Dynkin diagram, that is, our approach is the construction of the the extended Curtis - Phan - Tits presentation of the finite simple groups of Lie type of odd characteristic which further yields the construction of all subsystem subgroups which can be read from the extended Dynkin diagram. In this thesis, we present this algorithm for the groups PSL_n(q) and PSU_n(q). We also present an algorithm which determines whether the p-core (or ``unipotent radical'
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) O_p(G) of a black box group G is trivial or not where G/O_p(G) is a finite simple classical group of Lie type of odd characteristic p answering a well-known question of Babai and Shalev. The algorithms presented in this thesis have been implemented extensively in the computer algebra system GAP.
Fellenius, Gustaf. "Huset som pussel : En black box vid Slussen." Thesis, KTH, Arkitektur, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-30589.
Full textHörmann, Wolfgang, and Josef Leydold. "Black-Box Algorithms for Sampling from Continuous Distributions." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2006. http://epub.wu.ac.at/1042/1/document.pdf.
Full textSeries: Research Report Series / Department of Statistics and Mathematics
Aydal, Emine Gokce. "Model Based Robustness Testing of Black box Systems." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507672.
Full textKamp, Michael [Verfasser]. "Black-Box Parallelization for Machine Learning / Michael Kamp." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1200020057/34.
Full textROSSIGNOLI, DOMENICO. "DEMOCRACY, INSTITUTIONS AND GROWTH: EXPLORING THE BLACK BOX." Doctoral thesis, Università Cattolica del Sacro Cuore, 2013. http://hdl.handle.net/10280/1870.
Full textEconomic and political science literature show a wide consensus about the positive effect of property rights, contract enforcing arrangements and, more generally, economic institutions to long-run growth. Conversely, the linkage between democracy and growth remains unclear and not conclusively supported by empirical research. This work is an attempt to reconcile the stylized facts about democracy and growth –evidencing a long-run “synergic success” between the two terms – with theoretical and empirical literature. After thoroughly surveying the relevant literature on the topic, this study claims that the effect of democracy on long-run growth is indirect, channeled by the means of institutions. To test this hypothesis, the thesis provides an original analytical framework which is applied to a panel of 194 countries over the period 1961-2010, adopting a System-GMM estimation technique and a wide range of robustness controls. The results suggest that democracy is positively related to “better” (namely more growth-enhancing) institutions, especially with respect to economic institutions and rule of law. Hence, the findings suggest that the overall effect on growth is positive, indirect and channeled by institutions. However, since the results are not completely conclusive, a further investigation is suggested, on further determinants of democracy, potentially affecting its pro-growth effect.
Estrada, Vargas Ana Paula, and Vargas Ana Paula Estrada. "Black-Box identification of automated discrete event systems." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00846194.
Full textAmin, Gaurav Shirish. "Investing in hedge funds : analysing the 'black box'." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250708.
Full textLiem, Rhea Patricia. "Surrogate modeling for large-scale black-box systems." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41559.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 105-110).
This research introduces a systematic method to reduce the complexity of large-scale blackbox systems for which the governing equations are unavailable. For such systems, surrogate models are critical for many applications, such as Monte Carlo simulations; however, existing surrogate modeling methods often are not applicable, particularly when the dimension of the input space is very high. In this research, we develop a systematic approach to represent the high-dimensional input space of a large-scale system by a smaller set of inputs. This collection of representatives is called a multi-agent collective, forming a surrogate model with which an inexpensive computation replaces the original complex task. The mathematical criteria used to derive the collective aim to avoid overlapping of characteristics between representatives, in order to achieve an effective surrogate model and avoid redundancies. The surrogate modeling method is demonstrated on a light inventory that contains light data corresponding to 82 aircraft types. Ten aircraft types are selected by the method to represent the full light inventory for the computation of fuel burn estimates, yielding an error between outputs from the surrogate and full models of just 2.08%. The ten representative aircraft types are selected by first aggregating similar aircraft types together into agents, and then selecting a representative aircraft type for each agent. In assessing the similarity between aircraft types, the characteristic of each aircraft type is determined from available light data instead of solving the fuel burn computation model, which makes the assessment procedure inexpensive.
(cont.) Aggregation criteria are specified to quantify the similarity between aircraft types and a stringency, which controls the tradeoff between the two competing objectives in the modeling -- the number of representatives and the estimation error. The surrogate modeling results are compared to a model obtained via manual aggregation; that is, the aggregation of aircraft types is done based on engineering judgment. The surrogate model derived using the systematic approach yields fewer representatives in the collective, yielding a surrogate model with lower computational cost, while achieving better accuracy. Further, the systematic approach eliminates the subjectivity that is inherent in the manual aggregation method. The surrogate model is also applied to other light inventories, yielding errors of similar magnitude to the case when the reference light inventory is considered.
by Rhea Patricia Liem.
S.M.
Verì, Daniele. "Empirical Model Learning for Constrained Black Box Optimization." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25704/.
Full textGatumu, Michael. "Directors' values: A glimpse into the black box." Thesis, Australian Catholic University, 2015. https://acuresearchbank.acu.edu.au/download/1ab8d120b663dfd716ad9dd067cc9e57af5933184c005c97a9265748740ccfdb/5008733/201610_Michael_Gatumu.pdf.
Full textRowan, Adriaan. "Unravelling black box machine learning methods using biplots." Master's thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/31124.
Full textEstrada, Vargas Ana Paula. "Black-Box identification of automated discrete event systems." Thesis, Cachan, Ecole normale supérieure, 2013. http://www.theses.fr/2013DENS0006/document.
Full textThis thesis deals with the identification of automated discrete event systems (DES) operating in an industrial context. In particular the work focuses on the systems composed by a plant and a programmable logic controller (PLC) operating in a closed loop- the identification consists in obtaining an approximate model expressed in interpreted Petri nets (IPN) from the observed behaviour given under the form of a single sequence of input-output vectors of the PLC. First, an overview of previous works on identification of DES is presented as well as a comparative study of the main recent approaches on the matter. Then the addressed problem is stated- important technological characteristics of automated systems and PLC are detailed. Such characteristics must be considered in solving the identification problem, but they cannot be handled by previous identification techniques. The main contribution in this thesis is the creation of two complementary identification methods. The first method allows constructing systematically an IPN model from a single input-output sequence representing the observable behaviour of the DES. The obtained IPN models describe in detail the evolution of inputs and outputs during the system operation. The second method has been conceived for addressing large and complex industrial DES- it is based on a statistical approach yielding compact and expressive IPN models. It consists of two stages- the first one obtains, from the input-output sequence, the reactive part of the model composed by observable places and transitions. The second stage builds the non observable part of the model including places that ensure the reproduction of the observed input-output sequence. The proposed methods, based on polynomial-time algorithms, have been implemented in software tools, which have been tested with input-output sequences obtained from real systems in operation. The tools are described and their application is illustrated through two case studies
Carter, Brandon M. "Interpreting black-box models through sufficient input subsets." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123008.
Full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 73-77).
Recent progress in machine learning has come at the cost of interpretability, earning the field a reputation of producing opaque, "black-box" models. While deep neural networks are often able to achieve superior predictive accuracy over traditional models, the functions and representations they learn are usually highly nonlinear and difficult to interpret. This lack of interpretability hinders adoption of deep learning methods in fields such as medicine where understanding why a model made a decision is crucial. Existing techniques for explaining the decisions by black-box models are often restricted to either a specific type of predictor or are undesirably sensitive to factors unrelated to the model's decision-making process. In this thesis, we propose sufficient input subsets, minimal subsets of input features whose values form the basis for a model's decision. Our technique can rationalize decisions made by a black-box function on individual inputs and can also explain the basis for misclassifications. Moreover, general principles that globally govern a model's decision-making can be revealed by searching for clusters of such input patterns across many data points. Our approach is conceptually straightforward, entirely model-agnostic, simply implemented using instance-wise backward selection, and able to produce more concise rationales than existing techniques. We demonstrate the utility of our interpretation method on various neural network models trained on text, genomic, and image data.
by Brandon M. Carter.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Söderberg, John. "Black-box modeling of a semi-active motorcycle damper." Thesis, KTH, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-57722.
Full textGrosfils, Aline. "First principles and black box modelling of biological systems." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210677.
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Mathematical models of cell cultures may come in various shapes and be phrased with varying degrees of mathematical formalism. Typically, three main model classes are available to describe the nonlinear dynamic behaviour of such biological systems. They consist of macroscopic models which only describe the main phenomena appearing in a culture. Indeed, a high model complexity may lead to long numerical computation time incompatible with engineering tools like software sensors or controllers. The first model class is composed of the first principles or white box models. They consist of the system of mass balances for the main species (biomass, substrates, and products of interest) involved in a reaction scheme, i.e. a set of irreversible reactions which represent the main biological phenomena occurring in the considered culture. Whereas transport phenomena inside and outside the cell culture are often well known, the reaction scheme and associated kinetics are usually a priori unknown, and require special care for their modelling and identification. The second kind of commonly used models belongs to black box modelling. Black boxes consider the system to be modelled in terms of its input and output characteristics. They consist of mathematical function combinations which do not allow any physical interpretation. They are usually used when no a priori information about the system is available. Finally, hybrid or grey box modelling combines the principles of white and black box models. Typically, a hybrid model uses the available prior knowledge while the reaction scheme and/or the kinetics are replaced by a black box, an Artificial Neural Network for instance.
Among these numerous models, which one has to be used to obtain the best possible representation of a bioprocess? We attempt to answer this question in the first part of this work. On the basis of two simulated bioprocesses and a real experimental one, two model kinds are analysed. First principles models whose reaction scheme and kinetics can be determined thanks to systematic procedures are compared with hybrid model structures where neural networks are used to describe the kinetics or the whole reaction term (i.e. kinetics and reaction scheme). The most common artificial neural networks, the MultiLayer Perceptron and the Radial Basis Function network, are tested. In this work, pure black box modelling is however not considered. Indeed, numerous papers already compare different neural networks with hybrid models. The results of these previous studies converge to the same conclusion: hybrid models, which combine the available prior knowledge with the neural network nonlinear mapping capabilities, provide better results.
From this model comparison and the fact that a physical kinetic model structure may be viewed as a combination of basis functions such as a neural network, kinetic model structures allowing biological interpretation should be preferred. This is why the second part of this work is dedicated to the improvement of the general kinetic model structure used in the previous study. Indeed, in spite of its good performance (largely due to the associated systematic identification procedure), this kinetic model which represents activation and/or inhibition effects by every culture component suffers from some limitations: it does not explicitely address saturation by a culture component. The structure models this kind of behaviour by an inhibition which compensates a strong activation. Note that the generalization of this kinetic model is a challenging task as physical interpretation has to be improved while a systematic identification procedure has to be maintained.
The last part of this work is devoted to another kind of biological systems: proteins. Such macromolecules, which are essential parts of all living organisms and consist of combinations of only 20 different basis molecules called amino acids, are currently used in the industrial world. In order to allow their functioning in non-physiological conditions, industrials are open to modify protein amino acid sequence. However, substitutions of an amino acid by another involve thermodynamic stability changes which may lead to the loss of the biological protein functionality. Among several theoretical methods predicting stability changes caused by mutations, the PoPMuSiC (Prediction Of Proteins Mutations Stability Changes) program has been developed within the Genomic and Structural Bioinformatics Group of the Université Libre de Bruxelles. This software allows to predict, in silico, changes in thermodynamic stability of a given protein under all possible single-site mutations, either in the whole sequence or in a region specified by the user. However, PoPMuSiC suffers from limitations and should be improved thanks to recently developed techniques of protein stability evaluation like the statistical mean force potentials of Dehouck et al. (2006). Our work proposes to enhance the performances of PoPMuSiC by the combination of the new energy functions of Dehouck et al. (2006) and the well known artificial neural networks, MultiLayer Perceptron or Radial Basis Function network. This time, we attempt to obtain models physically interpretable thanks to an appropriate use of the neural networks.
Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished
Zaiser, Stefan Sebastian [Verfasser]. "Mengenbasierte Black-Box-Identifikation linearer Systeme / Stefan Sebastian Zaiser." Ulm : Universität Ulm, 2017. http://d-nb.info/1132713129/34.
Full textYates, James W. T. "Black box and mechanistic modelling of electronic nose systems." Thesis, University of Warwick, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.413428.
Full textSinha, Aradhana. "Scalable black-box model explainability through low-dimensional visualizations." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113109.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 39-40).
Two methods are proposed to provide visual intuitive explanations for how black-box models work. The first is a projection pursuit-based method that seeks to provide data-point specific explanations. The second is a generalized additive model approach that seeks to explain the model on a more holistic level, enabling users to visualize the contributions across all features at once. Both models incorporate visual and interactive elements designed to create an intuitive understanding of both the logic and limits of the model. Both explanation systems are designed to scale well to large datasets with many data points and many features.
by Aradhana Sinha.
M. Eng.
Baran, Ilya 1981. "Adaptive algorithms for problems involving black-box Lipschitz functions." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17934.
Full textIncludes bibliographical references (p. 61-62).
Suppose we are given a black-box evaluator (an oracle that returns the function value at a given point) for a Lipschitz function with a known Lipschitz constant. We consider queries that can be answered about the function by using a finite number of black-box evaluations. Specifically, we study the problems of approximating a Lipschitz function, approximately integrating a Lipschitz function, approximately minimizing a Lipschitz function, and computing the winding number of a Lipschitz curve in R² around a point. The goal is to minimize the number of evaluations used for answering a query. Because the complexity of the problem instances varies widely, depending on the actual function, we wish to design adaptive algorithms whose performance is close to the best possible on every problem instance. We give optimally adaptive algorithms for winding number computation and univariate approximation and integration. We also give a near-optimal adaptive algorithm for univariate approximation when the output of function evaluations is corrupted by random noise. For optimization over higher dimensional domains, we prove that good adaptive algorithms are impossible.
by Ilya Baran.
M.Eng.
Kugelberg, Ingrid. "Black-Box Modeling and Attitude Control of a Quadcopter." Thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125649.
Full textPósch, Krisztián. "Procedural justice theory and the black box of causality." Thesis, London School of Economics and Political Science (University of London), 2018. http://etheses.lse.ac.uk/3805/.
Full textSayed, Shereef. "Black-Box Fuzzing of the REDHAWK Software Communications Architecture." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/54566.
Full textMaster of Science
Arnedo, Luis. "System Level Black-Box Models for DC-DC Converters." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/29193.
Full textPh. D.
Rees, Glyn Owen. "Efficient "black-box" multigrid solvers for convection-dominated problems." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/efficient-blackbox-multigrid-solvers-for-convectiondominated-problems(d49ec3ea-1dc2-4238-b0c1-0688e5944ddd).html.
Full textKlarner, Patricia, Gilbert Probst, and Michael Useem. "Opening the black box: Unpacking board involvement in innovation." Sage, 2019. http://dx.doi.org/10.1177/1476127019839321.
Full textAit, Elhara Ouassim. "Stochastic Black-Box Optimization and Benchmarking in Large Dimensions." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS211/document.
Full textBecause of the generally high computational costs that come with large-scale problems, more so on real world problems, the use of benchmarks is a common practice in algorithm design, algorithm tuning or algorithm choice/evaluation. The question is then the forms in which these real-world problems come. Answering this question is generally hard due to the variety of these problems and the tediousness of describing each of them. Instead, one can investigate the commonly encountered difficulties when solving continuous optimization problems. Once the difficulties identified, one can construct relevant benchmark functions that reproduce these difficulties and allow assessing the ability of algorithms to solve them. In the case of large-scale benchmarking, it would be natural and convenient to build on the work that was already done on smaller dimensions, and be able to extend it to larger ones. When doing so, we must take into account the added constraints that come with a large-scale scenario. We need to be able to reproduce, as much as possible, the effects and properties of any part of the benchmark that needs to be replaced or adapted for large-scales. This is done in order for the new benchmarks to remain relevant. It is common to classify the problems, and thus the benchmarks, according to the difficulties they present and properties they possess. It is true that in a black-box scenario, such information (difficulties, properties...) is supposed unknown to the algorithm. However, in a benchmarking setting, this classification becomes important and allows to better identify and understand the shortcomings of a method, and thus make it easier to improve it or alternatively to switch to a more efficient one (one needs to make sure the algorithms are exploiting this knowledge when solving the problems). Thus the importance of identifying the difficulties and properties of the problems of a benchmarking suite and, in our case, preserving them. One other question that rises particularly when dealing with large-scale problems is the relevance of the decision variables. In a small dimension problem, it is common to have all variable contribute a fair amount to the fitness value of the solution or, at least, to be in a scenario where all variables need to be optimized in order to reach high quality solutions. This is however not always the case in large-scales; with the increasing number of variables, some of them become redundant or groups of variables can be replaced with smaller groups since it is then increasingly difficult to find a minimalistic representation of a problem. This minimalistic representation is sometimes not even desired, for example when it makes the resulting problem more complex and the trade-off with the increase in number of variables is not favorable, or larger numbers of variables and different representations of the same features within a same problem allow a better exploration. This encourages the design of both algorithms and benchmarks for this class of problems, especially if such algorithms can take advantage of the low effective dimensionality of the problems, or, in a complete black-box scenario, cost little to test for it (low effective dimension) and optimize assuming a small effective dimension. In this thesis, we address three questions that generally arise in stochastic continuous black-box optimization and benchmarking in high dimensions: 1. How to design cheap and yet efficient step-size adaptation mechanism for evolution strategies? 2. How to construct and generalize low effective dimension problems? 3. How to extend a low/medium dimension benchmark to large dimensions while remaining computationally reasonable, non-trivial and preserving the properties of the original problem?
Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.
Full textThis PhD thesis focuses on the automated algorithm configuration that aims at finding the best parameter setting for a given problem or a' class of problem. The Algorithm Configuration problem thus amounts to a metal Foptimization problem in the space of parameters, whosemetaFobjective is the performance measure of the given algorithm at hand with a given parameter configuration. However, in the continuous domain, such method can only be empirically assessed at the cost of running the algorithm on some problem instances. More recent approaches rely on a description of problems in some features space, and try to learn a mapping from this feature space onto the space of parameter configurations of the algorithm at hand. Along these lines, this PhD thesis focuses on the Per Instance Algorithm Configuration (PIAC) for solving continuous black boxoptimization problems, where only a limited budget confessionnalisations available. We first survey Evolutionary Algorithms for continuous optimization, with a focus on two algorithms that we have used as target algorithm for PIAC, DE and CMAFES. Next, we review the state of the art of Algorithm Configuration approaches, and the different features that have been proposed in the literature to describe continuous black box optimization problems. We then introduce a general methodology to empirically study PIAC for the continuous domain, so that all the components of PIAC can be explored in real Fworld conditions. To this end, we also introduce a new continuous black box test bench, distinct from the famous BBOB'benchmark, that is composed of a several multiFdimensional test functions with different problem properties, gathered from the literature. The methodology is finally applied to two EAS. First we use Differential Evolution as'target algorithm, and explore all the components of PIAC, such that we empirically assess the best. Second, based on the results on DE, we empirically investigate PIAC with Covariance Matrix Adaptation Evolution Strategy (CMAFES) as target algorithm. Both use cases empirically validate the proposed methodology on the new black box testbench for dimensions up to100
Conley, Natasha. "BARRIERS AND FACILITATORS OF GROWTH IN BLACK ENTREPRENEURIAL VENTURES: THINKING OUTSIDE THE BLACK BOX." Case Western Reserve University School of Graduate Studies / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=case1522882124350055.
Full textJoel, Viklund. "Explaining the output of a black box model and a white box model: an illustrative comparison." Thesis, Uppsala universitet, Filosofiska institutionen, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420889.
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