Dissertations / Theses on the topic 'Genetic programming, strategies, applications'
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Fillon, Cyril. "New strategies for efficient and practical genetic programming." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/2581.
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2006/2007In the last decades, engineers and decision makers expressed a growing interest in the development of effective modeling and simulation methods to understand or predict the behavior of many phenomena in science and engineering. Many of these phenomena are translated in mathematical models for convenience and to carry out an easy interpretation. Methods commonly employed for this purpose include, for example, Neural Networks, Simulated Annealing, Genetic Algorithms, Tabu search, and so on. These methods all seek for the optimal or near optimal values of a predefined set of parameters of a model built a priori. But in this case, a suitable model should be known beforehand. When the form of this model cannot be found, the problem can be seen from another level where the goal is to find a program or a mathematical representation which can solve the problem. According to this idea the modeling step is performed automatically thanks to a quality criterion which drives the building process. In this thesis, we focus on the Genetic Programming (GP) approach as an automatic method for creating computer programs by means of artificial evolution based upon the original contributions of Darwin and Mendel. While GP has proven to be a powerful means for coping with problems in which finding a solution and its representation is difficult, its practical applicability is still severely limited by several factors. First, the GP approach is inherently a stochastic process. It means there is no guarantee to obtain a satisfactory solution at the end of the evolutionary loop. Second, the performances on a given problem may be strongly dependent on a broad range of parameters, including the number of variables involved, the quantity of data for each variable, the size and composition of the initial population, the number of generations and so on. On the contrary, when one uses Genetic Programming to solve a problem, he has two expectancies: on the one hand, maximize the probability to obtain an acceptable solution, and on the other hand, minimize the amount of computational resources to get this solution. Initially we present innovative and challenging applications related to several fields in science (computer science and mechanical science) which participate greatly in the experience gained in the GP field. Then we propose new strategies for improving the performances of the GP approach in terms of efficiency and accuracy. We probe our approach on a large set of benchmark problems in three different domains. Furthermore we introduce a new approach based on GP dedicated to symbolic regression of multivariate data-sets where the underlying phenomenon is best characterized by a discontinuous function. These contributions aim to provide a better understanding of the key features and the underlying relationships which make enhancements successful in improving the original algorithm.
Negli ultimi anni, ingegneri e progettisti hanno espresso un interesse crescente nello sviluppo di nuovi metodi di simulazione e di modellazione per comprendere e predire il comportamento di diversi fenomeni sia in ambito scientifico che ingegneristico. Molti di questi fenomeni vengono descritti attraverso modelli matematici che ne facilitano l'interpretazione. A questo fine, i metodi più comunemente impiegati sono, le tecniche basate sui Reti Neurali, Simulated Annealing, gli Algoritmi Genetici, la ricerca Tabu, ecc. Questi metodi vanno a determinare i valori ottimali o quasi ottimali dei parametri di un modello costruito a priori. E evidente che in tal caso, si dovrebbe conoscere in anticipo un modello idoneo. Quando ciò non è possibile, il problema deve essere considerato da un altro punto di vista: l'obiettivo è trovare un programma o una rappresentazione matematica che possano risolvere il problema. A questo scopo, la fase di modellazione è svolta automaticamente in funzione di un criterio qualitativo che guida il processo di ricerca. Il tema di ricerca di questa tesi è la programmazione genetica (“Genetic Programming” che chiameremo GP) e le sue applicazioni. La programmazione genetica si può definire come un metodo automatico per la generazione di programmi attraverso una simulazione artificiale dei principi relativi all'evoluzione naturale basata sui contributi originali di Darwin e di Mendel. La programmazione genetica ha dimostrato di essere un potente mezzo per affrontare quei problemi in cui trovare una soluzione e la sua rappresentazione è difficile. Però la sua applicabilità rimane severamente limitata da diversi fattori. In primo luogo, il metodo GP è inerentemente un processo stocastico. Ciò significa che non garantisce che una soluzione soddisfacente sarà trovata alla fine del ciclo evolutivo. In secondo luogo, le prestazioni su un dato problema dipendono fortemente da una vasta gamma di parametri, compresi il numero di variabili impiegate, la quantità di dati per ogni variabile, la dimensione e la composizione della popolazione iniziale, il numero di generazioni e così via. Al contrario, un utente della programmazione genetica ha due aspettative: da una parte, massimizzare la probabilità di ottenere una soluzione accettabile, e dall'altra, minimizzare la quantità di risorse di calcolo per ottenerla. Nella fase iniziale di questo lavoro sono state considerate delle applicazioni particolarmente innovative relative a diversi campi della scienza (informatica e meccanica) che hanno contributo notevolmente all'esperienza acquisita nel campo della programmazione genetica. In questa tesi si propone un nuovo procedimento con lo scopo di migliorare le prestazioni della programmazione genetica in termini di efficienza ed accuratezza. Abbiamo testato il nostro approccio su un ampio insieme di benchmarks in tre domini applicativi diversi. Si propone inoltre una tecnica basata sul GP per la regressione simbolica di data-set multivariati dove il fenomeno di fondo è caratterizzato da una funzione discontinua. Questi contributi cercano di fornire una comprensione migliore degli elementi chiave e dei meccanismi interni che hanno consentito il miglioramento dell'algoritmo originale.
XX Ciclo
1980
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Heinze, Glenn. "Application of evolutionary algorithm strategies to entity relationship diagrams /." View PDF document on the Internet, 2004. http://library.athabascau.ca/scisthesis/Heinze.pdf.
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Deakin, Anthony Grayham. "Evolving strategies with genetic programming." Thesis, University of Liverpool, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.272651.
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Caroli, Alberto. "Genetic Programming applications in Robotics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/5860/.
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De, Lorenzo Andrea. "Genetic Programming Techniques in Engineering Applications." Doctoral thesis, Università degli studi di Trieste, 2014. http://hdl.handle.net/10077/9991.
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2012/2013Machine learning is a suite of techniques that allow developing algorithms for performing tasks by generalizing from examples. Machine learning systems, thus, may automatically synthesize programs from data. This approach is often feasible and cost-effective where manual programming or manual algorithm design is not. In the last decade techniques based on machine learning have spread in a broad range of application domains. In this thesis, we will present several novel applications of a specific machine Learning technique, called Genetic Programming, to a wide set of engineering applications grounded in real world problems. The problems treated in this work range from the automatic synthesis of regular expressions, to the generation of electricity price forecast, to the synthesis of a model for the tracheal pressure in mechanical ventilation. The results demonstrate that Genetic Programming is indeed a suitable tool for solving complex problems of practical interest. Furthermore, several results constitute a significant improvement over the existing state-of-the-art. The main contribution of this thesis is the design and implementation of a framework for the automatic inference of regular expressions from examples based on Genetic Programming. First, we will show the ability of such a framework to cope with the generation of regular expressions for solving text-extraction tasks from examples. We will experimentally assess our proposal comparing our results with previous proposals on a collection of real-world datasets. The results demonstrate a clear superiority of our approach. We have implemented the approach in a web application that has gained considerable interest and has reached peaks of more 10000 daily accesses. Then, we will apply the framework to a popular "regex golf" challenge, a competition for human players that are required to generate the shortest regular expression solving a given set of problems. Our results rank in the top 10 list of human players worldwide and outperform those generated by the only existing algorithm specialized to this purpose. Hence, we will perform an extensive experimental evaluation in order to compare our proposal to the state-of-the-art proposal in a very close and long-established research field: the generation of a Deterministic Finite Automata (DFA) from a labelled set of examples. Our results demonstrate that the existing state-of-the-art in DFA learning is not suitable for text extraction tasks. We will also show a variant of our framework designed for solving text processing tasks of the search-and-replace form. A common way to automate search-and-replace is to describe the region to be modified and the desired changes through a regular expression and a replacement expression. We will propose a solution to automatically produce both those expressions based only on examples provided by user. We will experimentally assess our proposal on real-word search-and-replace tasks. The results indicate that our proposal is indeed feasible. Finally, we will study the applicability of our framework to the generation of schema based on a sample of the eXtensible Markup Language documents. The eXtensible Markup Language documents are largely used in machine-to-machine interactions and such interactions often require that some constraints are applied to the contents of the documents. These constraints are usually specified in a separate document which is often unavailable or missing. In order to generate a missing schema, we will apply and will evaluate experimentally our framework to solve this problem. In the final part of this thesis we will describe two significant applications from different domains. We will describe a forecasting system for producing estimates of the next day electricity price. The system is based on a combination of a predictor based on Genetic Programming and a classifier based on Neural Networks. Key feature of this system is the ability of handling outliers-i.e., values rarely seen during the learning phase. We will compare our results with a challenging baseline representative of the state-of-the-art. We will show that our proposal exhibits smaller prediction error than the baseline. Finally, we will move to a biomedical problem: estimating tracheal pressure in a patient treated with high-frequency percussive ventilation. High-frequency percussive ventilation is a new and promising non-conventional mechanical ventilatory strategy. In order to avoid barotrauma and volutrauma in patience, the pressure of air insufflated must be monitored carefully. Since measuring the tracheal pressure is difficult, a model for accurately estimating the tracheal pressure is required. We will propose a synthesis of such model by means of Genetic Programming and we will compare our results with the state-of-the-art.
XXVI Ciclo
1984
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Pinder, Robert William 1977. "Applications of genetic programming to parallel system optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86507.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 81-84).
by Robert William Pinder.
M.Eng.
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Wang, Xia. "Applications of genetic algorithms, dynamic programming, and linear programming to combinatorial optimization problems." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8778.
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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.Thesis research directed by: Applied Mathematics & Statistics, and Scientific Computation Program. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Day, Peter. "Advances in genetic programming with applications in speech and audio." Thesis, University of Liverpool, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428373.
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Hulse, Paul. "A study of topical applications of genetic programming and genetic algorithms in physical and engineering systems." Thesis, University of Salford, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391313.
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Stasinakis, Charalampos. "Applications of hybrid neural networks and genetic programming in financial forecasting." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4921/.
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This thesis explores the utility of computational intelligent techniques and aims to contribute to the growing literature of hybrid neural networks and genetic programming applications in financial forecasting. The theoretical background and the description of the forecasting techniques are given in the first part of the thesis (chapters 1-3), while the contribution is provided through the last five self-contained chapters (chapters 4-8). Chapter 4 investigates the utility of the Psi Sigma neural network when applied to the task of forecasting and trading the Euro/Dollar exchange rate, while Kalman Filter estimation is tested in combining neural network forecasts. A time-varying leverage trading strategy based on volatility forecasts is also introduced. In chapter 5 three neural networks are used to forecast an exchange rate, while Kalman Filter, Genetic Programming and Support Vector Regression are implemented to provide stochastic and genetic forecast combinations. In addition, a hybrid leverage trading strategy tests if volatility forecasts and market shocks can be combined to boost the trading performance of the models. Chapter 6 presents a hybrid Genetic Algorithm – Support Vector Regression model for optimal parameter selection and feature subset combination. The model is applied to the task of forecasting and trading three euro exchange rates. The results of these chapters suggest that the stochastic and genetic neural network forecast combinations present superior forecasts and high profitability. In that way, more light is shed in the demanding issue of achieving statistical and trading efficiency in the foreign exchange markets. The focus of the next two chapters shifts from exchange rate forecasting to inflation and unemployment prediction through optimal macroeconomic variable selection. Chapter 7 focuses on forecasting the US inflation and unemployment, while chapter 8 presents the Rolling Genetic – Support Vector Regression model. The latter is applied to several forecasting exercises of inflation and unemployment of EMU members. Both chapters provide information on which set of macroeconomic indicators is found relevant to inflation and unemployment targeting on a monthly basis. The proposed models statistically outperform traditional ones. Hence, the voluminous literature, suggesting that non-linear time-varying approaches are more efficient and realistic in similar applications, is extended. From a technical point of view, these algorithms are superior to non-adaptive algorithms; avoid time consuming optimization approaches and efficiently cope with dimensionality and data-snooping issues.
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Andrews, Martin. "Learning strategies for the financial markets." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336624.
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Cattani, Philip Thomas. "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification." Thesis, University of Kent, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651.
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Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin's theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called 'Cartesian', because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency - the time it takes to solve a problem - of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.
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Gadre, Aditya Shrikant. "Learning Strategies in Multi-Agent Systems - Applications to the Herding Problem." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/36116.
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"Multi-Agent systems" is a topic for a lot of research, especially research involving strategy, evolution and cooperation among various agents. Various learning algorithm schemes have been proposed such as reinforcement learning and evolutionary computing.
In this thesis two solutions to a multi-agent herding problem are presented. One solution is based on Q-learning algorithm, while the other is based on modeling of artificial immune system.
Q-learning solution for the herding problem is developed, using region-based local learning for each individual agent. Individual and batch processing reinforcement algorithms are implemented for non-cooperative agents. Agents in this formulation do not share any information or knowledge. Issues such as computational requirements, and convergence are discussed.
An idiotopic artificial immune network is proposed that includes individual B-cell model for agents and T-cell model for controlling the interaction among these agents. Two network models are proposed--one for evolving group behavior/strategy arbitration and the other for individual action selection.
A comparative study of the Q-learning solution and the immune network solution is done on important aspects such as computation requirements, predictability, and convergence.Master of Science
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Lee, Joo Hong. "Hybrid Parallel Computing Strategies for Scientific Computing Applications." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28882.
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Multi-core, multi-processor, and Graphics Processing Unit (GPU) computer architectures pose significant challenges with respect to the efficient exploitation of parallelism for large-scale, scientific computing simulations. For example, a simulation of the human tonsil at the cellular level involves the computation of the motion and interaction of millions of cells over extended periods of time. Also, the simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is an extremely computationally demanding problem. The PMC method is example of the Monte Carlo simulation method—an approach extensively used in wide of application areas. Although the basic algorithmic framework of these Monte Carlo methods is simple, they can be extremely computationally intensive. Therefore, an efficient parallel realization of these simulations depends on a careful analysis of the nature these problems and the development of an appropriate software framework. The overarching goal of this dissertation is develop and understand what the appropriate parallel programming model should be to exploit these disparate architectures, both from the metric of efficiency, as well as from a software engineering perspective.
In this dissertation we examine these issues through a performance study of PathSim2, a software framework for the simulation of large-scale biological systems, using two different parallel architectures’ distributed and shared memory. First, a message-passing implementation of a multiple germinal center simulation by PathSim2 is developed and analyzed for distributed memory architectures. Second, a germinal center simulation is implemented on shared memory architecture with two parallelization strategies based on Pthreads and OpenMP.
Finally, we present work targeting a complete hybrid, parallel computing architecture. With this work we develop and analyze a software framework for generic Monte Carlo simulations implemented on multiple, distributed memory nodes consisting of a multi-core architecture with attached GPUs. This simulation framework is divided into two asynchronous parts: (a) a threaded, GPU-accelerated pseudo-random number generator (or producer), and (b) a multi-threaded Monte Carlo application (or consumer). The advantage of this approach is that this software framework can be directly used within any Monte Carlo application code, without requiring application-specific programming of the GPU. We examine this approach through a performance study of the simulation of RHT effects by the PMC method on a hybrid computing architecture. We present a theoretical analysis of our proposed approach, discuss methods to optimize performance based on this analysis, and compare this analysis to experimental results obtained from simulations run on two different hybrid, parallel computing architectures.Ph. D.
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Pawlas, Krzysztof, and Davood Zall. "Analysis of Forecasting Methods and Applications of System Dynamics and Genetic Programming : Case Studies on Country Throughput." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2140.
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Objectives. In this study we review previous attempts in forecasting country seaborne container throughput, analyze them and then classify in form of table to provide a concrete base for researchers in this field. Another aim of this study is to provide a Decision Support System (DSS) to assist experts in port management and forecast their country seaborne container demand. It will lead to reasonable decisions so as to provide sufficient supply which handles containers demand. This DSS, is a global forecasting model which can be applied to every country, independently of their specific parameters. Methods. In theoretical phase a number of scientific databases such as: Google Scholar, ACM, SCOPUS, IEEE, SpringerLink and some other are used to collect previous studies. After review and analysis, selected papers are classified in a form of table to provide a complete resource for us as well as future researchers in this field. In order to provide appropriate model, we combine System Dynamics modeling with Genetic Programming to provide an accurate and reliable model. This model is the result of the analysis of previous studies and applied in this study for the first time. Results. Our final model was applied to two cases (Sweden and China) and provides provided reliable results for both countries. To analyze the uncertain variables in the model, Monte Carlo simulation was used to assess the sensitivity of our model. In order to compare with other methods, we conducted a case study with Artificial Neural Network (ANN) and compared the results of our model and ANN. The results show the disadvantages of statistical methods to system dynamics. Additionally to compare with other attempts, our model was confronted with another study which provided a model for Finland. By comparing and considering their advantages and disadvantages we found out that our simplified model could be applied as a global model to other countries. Conclusions. We conclude that our model is an appropriate DSS to assist experts, forecast their country throughput and make appropriate decisions so as to invest, extending their ports in right time. The application of Genetic Programming in our model provides accurate mathematical equations for the influencing variables which even may not need to calibrate the model. It is a global model which can be applied to different countries but still requires more experiments to prove this claim.This research aims to provide a decision support system to assist experts in port management to forecast future trends of cargo demand. By forecasting the future demand, decision makers will be able to decide on sufficient supply. For example, in case of necessity, based on forecasting results, the infrastructure can be expanded and also the capacity of ports can be managed. This will help not only to invest in right place and time, but also to balance their demand between ports in a country. The majority of previous researches considered only statistical methods to forecast the future cargo demand. Sometimes the previous research studies applied only one method and then compared it with others and provided advantages and disadvantages of each methods. In some other cases the previous research studies were combining statistical methods to analyze linear and non linear behavior of influencing parameters in cargo demand to conduct a forecast later and its future demand. All the research studies that were collected were analyzed and then classified into a table (c.f., chapter 4). Recently, some studies applied system dynamics to analyze all interactions in the system and forecast the future cargo demand like (Ruutu 2008) and (E. Suryani et al. 2012). In this research we combined system dynamics with genetic programming to benefit from the advantages of each method. By using the system dynamics modeling technique, we defined all influencing parameters and their interactions in the system. By use of genetic programming we provided accurate equations between different parameters and country demand. In Genetic Programming, all the equations can be fitted into data. At last, even we do not need to calibrate the equations to fit into historical data. This will provide a reliable model to forecast demand and align the supply with it. To validate our model, it is applied on two different countries and the results from the analysis indicate that the simplified model provides an acceptable model and it follows the trend of historical data. To compare our model with previous statistical methods the results of our model in Sweden and China were compared with the result of neural network in another case study with the same data. To compare our model with other similar studies, it turned out that it is closely related to the model for Finland. After comparison and analysis of their advantages and disadvantages, we concluded that our simplified model can apply as a global model to other countries, but it needs to prove with a number of different case studies (different countries with different situations). To analyze the uncertain variables, which can affect the model, we used Monte Carlo simulation. It assesses the sensitivity of our model to changes in input variables. The final model is applicable to every country, but it needs to apply the local econometric parameters, which affect the country throughput. By considering the share of each port in total demand of the country, we can apply the model to each port and forecast the future trends in order to find the right date to invest and extend the capacity to handle Demand.
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Kampouridis, Michael. "Computational intelligence in financial forecasting and agent-based modeling : applications of genetic programming and self-organizing maps." Thesis, University of Essex, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548594.
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Ghafoor, Sheikh Khaled. "Integrating Algorithmic and Systemic Load Balancing Strategies in Parallel Applications." MSSTATE, 2003. http://sun.library.msstate.edu/ETD-db/theses/available/etd-11112003-113055/.
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Load imbalance is a major source of performance degradation in parallel scientific applications. Load balancing increases the efficient use of existing resources and improves performance of parallel applications running in distributed environments. At a coarse level of granularity, advances in runtime systems for parallel programs have been proposed in order to control available resources as efficiently as possible by utilizing idle resources and using task migration. At a finer granularity level, advances in algorithmic strategies for dynamically balancing computational loads by data redistribution have been proposed in order to respond to variations in processor performance during the execution of a given parallel application. Algorithmic and systemic load balancing strategies have complementary set of advantages. An integration of these two techniques is possible and it should result in a system, which delivers advantages over each technique used in isolation. This thesis presents a design and implementation of a system that combines an algorithmic fine-grained data parallel load balancing strategy called Fractiling with a systemic coarse-grained task-parallel load balancing system called Hector. It also reports on experimental results of running N-body simulations under this integrated system. The experimental results indicate that a distributed runtime environment, which combines both algorithmic and systemic load balancing strategies, can provide performance advantages with little overhead, underscoring the importance of this approach in large complex scientific applications.
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Gadiraju, Sriphani Raju. "Modified selection mechanisms designed to help evolution strategies cope with noisy response surfaces." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-07022003-164112.
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DI, PLACIDO Andrea. "The close enough Traveling Salesman Problem: enhanced heuristics, applications and variants." Doctoral thesis, Università degli studi del Molise, 2022. https://hdl.handle.net/11695/115287.
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Il traveling salesman problem (TSP) è ampiamente studiato per la sua abilità di modellare un'ampia gamma di problemi. L'obiettivo è identificare il percorso minimo che visiti ogni città di un dato set e tornare alla città di partenza ("deposito"). Il TSP ha diverse applicazioni pratiche riguardanti il planning, la logistica, etc. Negli ultimi anni, generalizzazioni del TSP sono state ampiamente trattate per scopi logistici, come il close enough traveling salesman problem (CETSP) e il close enough arc routing problem (CEARP). Nel CETSP, ogni target ha un raggio d'azione entro il quale è considerato visitato. Da ciò, non siamo più vincolati alla posizione esatta, ma basta andare abbastanza vicino. In questo problema non siamo vincolati ad una rete stradale, quindi consideriamo le distanze euclidee. Invece, nel caso del CEARP, abbiamo sempre un raggio d'azione attorno ai target, ma siamo vincolati ad una rete stradale (archi) per definire la rotta. Il CETSP e CEARP hanno applicazioni pratiche in diversi scenari reali, come ad esempio per la lettura di contatori in un quartiere mediante sistemi a radio frequenza (RFID).
In questa tesi, presentiamo uno studio approfondito del CETSP.
Abbiamo prodotto un algoritmo genetico (GA) per la sua risoluzione, che fornisce soluzioni migliori nella maggior parte dei casi di benchmark considerati. Inoltre, presentiamo due metriche, TSPDegree (TSPD) e OverlappingCenter (OC), per la valutazione delle istanze del problema. Abbiamo comparato le nostre metriche con quelle già presenti in letteratura, nello specifico overlap ratio (OR), ottenendo che TSPD e OC forniscono maggiori informazioni sulle caratteristiche di un'istanza rispetto a OR. Riguardo OR, abbiamo identificato valori errati presenti in letteratura, e ne presentiamo i valori corretti.
Presentiamo un caso di studio relativo alla diagnostica di pannelli solari. Nello specifico, abbiamo utilizzato un drone per controllare il corretto funzionamento di un campo fotovoltaico. Il suddetto caso è stato modellato come un CETSP e risolto mediante il nostro algoritmo, ottenendo ottimi risultati.
In questo lavoro, introduciamo due varianti del CETSP, rispettivamente il mixed constrained generalized routing problem (MCGRP) e il generalized close enough traveling salesman problem (GCETSP). Il primo è una generalizzazione di CETSP e CEARP in cui introduciamo il concetto di zone di volo. Ne differenziamo due: una in cui il drone può volare liberamente (FFZ), e una in cui il volo è vincolato o proibito (CFZ). Il MCGRP si pone lo stesso obiettivo del CETSP ma nel rispetto dei vincoli delle zone di volo. Abbiamo formalmente definito il problema e lo abbiamo esaminato nei suoi casi limiti, ossia CETSP e CEARP.
Il secondo è una variante del CETSP, in cui ogni target ha diverse aree associate ad esso, modellate come dischi concentrici, ognuna con un premio che decresce all'allontanarsi dal target. Lo scopo del GCETSP è quello di identificare la rotta di costo minimo che massimizza la differenza tra il premio totale e la lunghezza della rotta, visitando un disco per ogni target. Col GCETSP possiamo modellare diversi scenari applicativi in cui otteniamo maggiori benefici avvicinandoci al target.
Abbiamo definito formalmente il problema e delle istanze per esso, e abbiamo adattato l'algoritmo risolutivo a questo problema. I risultati mostrano che il nostro approccio è in grado di risolvere il problema correttamente, fornendo eccellenti soluzioni.The traveling salesman problem (TSP) is widely studied to model a wide range of problems. The objective is to identify the minimum path to visit each city in a given set and return to the city of departure ("depot"). TSP has several practical applications in planning, logistics, etc. In recent years, generalizations of TSP have been widely treated for logistical purposes, such as the close enough traveling salesman problem (CETSP) and the close enough arc routing problem (CEARP). In CETSP, each target has a range within which it is considered visited. Hence, we are no longer constrained to the exact location but just close enough. We are not bound to a road network in this problem, so we consider Euclidean distances. In contrast, in the case of CEARP, we always have a range around the targets, but we are constrained to a road network (arcs) to define the route. CETSP and CEARP have practical applications in several real-world scenarios, such as reading meters in a neighborhood using radio frequency identification (RFID) systems. In this thesis, we present an in-depth study of CETSP. We have produced a genetic algorithm (GA) for its resolution, which provides better solutions in most considered benchmark cases. In addition, we present two metrics, TSPDegree (TSPD) and OverlappingCenter (OC), for evaluating instances of the problem. We compared our metrics with those already present in the literature, precisely overlap ratio (OR), obtaining that TSPD and OC provide more information about the characteristics of an instance than OR. We identified incorrect values present in the literature and presented the corrected values regarding OR. We present a case study related to solar panel diagnostics. Specifically, we used a drone to check the correct operation of a solar array. The above case was modeled as a CETSP and solved using our algorithm, obtaining excellent results. This work introduces two variants of CETSP, respectively, the mixed constrained generalized routing problem (MCGRP) and the generalized close enough traveling salesman problem (GCETSP). The former is a generalization of CETSP and CEARP in which we introduce the concept of flight zones. We differentiate two of them: one in which the drone can fly freely (FFZ) and one in which the flight is constrained or prohibited (CFZ). The MCGRP has the same goal as CETSP but within the constraints of the flight zones. We have formally defined the problem and examined it in its limiting cases, namely CETSP and CEARP. The latter is a variant of CETSP. Each target has several areas, modeled as concentric disks, each with a premium that decreases as one moves away from the target. The purpose of GCETSP is to identify the minimum cost route that maximizes the difference between the total premium and the route length by visiting one disk per target. With GCETSP, we can model different application scenarios in which we get more benefits by getting closer to the target. We formally defined the problem and its instances and adapted the solving algorithm to this problem. The results show that our approach can solve the problem correctly, providing excellent solutions.
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Davanzo, Giorgio. "Machine learning in engineering applications." Doctoral thesis, Università degli studi di Trieste, 2011. http://hdl.handle.net/10077/4520.
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2009/2010Nowadays the available computing and information-storage resources grew up to a level that allows to easily collect and preserve huge amount of data. However, several organizations are still lacking the knowledge or the tools to process these data into useful informations. In this thesis work we will investigate several issues that can be solved effectively by means of machine learning techniques, ranging from web defacement detection to electricity prices forecasting, from Support Vector Machines to Genetic Programming. We will investigate a framework for web defacement detection meant to allow any organization to join the service by simply providing the URLs of the resources to be monitored along with the contact point of an administrator. Our approach is based on anomaly detection and allows monitoring the integrity of many remote web resources automatically while remaining fully decoupled from them, in particular, without requiring any prior knowledge about those resources—thus being an unsupervised system. Furthermore, we will test several machine learning algorithms normally used for anomaly detection on the web defacement detection problem. We will present a scrolling system to be used on mobile devices to provide a more natural and effective user experience on small screens. We detect device motion by analyzing the video stream generated by the camera and then we transform the motion in a scrolling of the content rendered on the screen. This way, the user experiences the device screen like a small movable window on a larger virtual view, without requiring any dedicated motion-detection hardware. As regards information retrieval, we will present an approach for information extraction for multi-page printed document; the approach is designed for scenarios in which the set of possible document classes, i.e., document sharing similar content and layout, is large and may evolve over time. Our approach is based on probability: we derived a general form for the probability that a sequence of blocks contains the searched information. A key step in the understanding of printed documents is their classification based on the nature of information they contain and their layout; we will consider both a static and a dynamic scenario, in which document classes are/are not known a priori and new classes can/can not appear at any time. Finally, we will move to the edge of machine learning: Genetic Programming. The electric power market is increasingly relying on competitive mechanisms taking the form of day-ahead auctions, in which buyers and sellers submit their bids in terms of prices and quantities for each hour of the next day. We propose a novel forecasting method based on Genetic Programming; key feature of our proposal is the handling of outliers, i.e., regions of the input space rarely seen during the learning.
Oggigiorno le risorse disponibili in termini computazionali e di archiviazione sono cresciute ad un livello tale da permettere facilmente di raccogliere e conservare enormi quantità di dati. Comunque, molte organizzazioni mancano ancora della conoscenza o degli strumenti necessari a processare tali dati in informazioni utili. In questo lavoro di tesi si investigheranno svariati problemi che possono essere efficacemente risolti attraverso strumenti di machine learning, spaziando dalla rilevazione di web defacement alla previsione dei prezzi della corrente elettrica, dalle Support Vector Machine al Genetic Programming. Si investigherà una infrastruttura per la rilevazione dei defacement studiata per permettere ad una organizzazione di sottoscrivere il servizio in modo semplice, fornendo l'URL da monitorare ed un contatto dell'amministratore. L'approccio presentato si basa sull'anomaly detection e permette di monitorare l'integrità di molte risorse web remote in modo automatico e sconnesso da esse, senza richiedere alcuna conoscenza a priori di tali risorse---ovvero, realizzando un sistema non supervisionato. A questo scopo verranno anche testati vari algoritmi di machine learning solitamente usati per la rilevazione di anomalie. Si presenterà poi un sistema di scorrimento da usare su dispositivi mobili capace di fornire una interfaccia naturale ed efficace anche su piccoli schermi. Il sistema rileva il movimento del dispositivo analizzando il flusso video generato dalla macchina fotografica integrata, trasformando lo spostamento rilevato in uno scorrimento del contenuto visualizzato sullo schermo. In questo modo, all'utente sembrerà che il proprio dispositivo sia una piccola finestra spostabile su una vista virtuale più ampia, senza che sia richiesto alcun dispositivo dedicato esclusivamente alla rilevazione dello spostamento. Verrà anche proposto un sistema per l'estrazione di informazioni da documenti stampati multi pagina; l'approccio è studiato per scenari in cui l'insieme di possibili classi di documenti (simili per contenuto ed organizzazione del testo) è ampio e può evolvere nel tempo. L'approccio si basa sulla probabilità: è stata studiata la probabilità che una sequenza di blocchi contenga l'informazione cercata. Un elemento chiave nel comprendere i documenti stampati è la loro classificazione in base alla natura delle informazioni che contengono e la loro posizione nel documento; verranno considerati sia uno scenario statico che uno dinamico, in cui il numero di classi di documenti è/non è noto a priori e nuove classi possono/non possono apparire nel tempo. Infine, ci si muoverà verso i confini del machine learning: il Genetic Programming. Il mercato della corrente elettrica si basa sempre più su aste in cui ogni giorno venditori ed acquirenti fanno delle offerte per l'acquisto di lotti di energia per il giorno successivo, con una granularità oraria della fornitura. Verrà proposto un nuovo metodo di previsione basato sul Genetic Programming; l'elemento chiave della soluzione qui presentata è la capacità di gestire i valori anomali, ovvero valori raramente osservati durante il processo di apprendimento.
XXIII Ciclo
1981
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Di, Pietro Anthony. "Optimising evolutionary strategies for problems with varying noise strength." University of Western Australia. School of Computer Science and Software Engineering, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0210.
Full textAbstract:
For many real-world applications of evolutionary computation, the fitness function is obscured by random noise. This interferes with the evaluation and selection processes and adversely affects the performance of the algorithm. Noise can be effectively eliminated by averaging a large number of fitness samples for each candidate, but the number of samples used per candidate (the resampling rate) required to achieve this is usually prohibitively large and time-consuming. Hence there is a practical need for algorithms that handle noise without eliminating it. Moreover, the amount of noise (noise strength and distribution) may vary throughout the search space, further complicating matters. We study noisy problems for which the noise strength varies throughout the search space. Such problems have generally been ignored by previous work, which has instead generally focussed on the specific case where the noise strength is the same at all points in the search domain. However, this need not be the case, and indeed this assumption is false for many applications. For example, in games of chance such as Poker, some strategies may be more conservative than others and therefore less affected by the inherent noise of the game. This thesis makes three significant contributions in the field of noisy fitness functions: We present the concept of dynamic resampling. Dynamic resampling is a technique that varies the resampling rate based on the noise strength and fitness for each candidate individually. This technique is designed to exploit the variation in noise strength and fitness to yield a more efficient algorithm. We present several dynamic resampling algorithms and give results that show that dynamic resampling can perform significantly better than the standard resampling technique that is usually used by the optimisation community, and that dynamic resampling algorithms that vary their resampling rates based on both noise strength and fitness can perform better than algorithms that vary their resampling rate based on only one of the above. We study a specific class of noisy fitness functions for which we counterintuitively find that it is better to use a higher resampling rate in regions of lower noise strength, and vice versa. We investigate how the evolutionary search operates on such problems, explain why this is the case, and present a hypothesis (with supporting evidence) for classifying such problems. We present an adaptive engine that automatically tunes the noise compensation parameters of the search during the run, thereby eliminating the need for the user to choose these parameters ahead of time. This means that our techniques can be readily applied to real-world problems without requiring the user to have specialised domain knowledge of the problem that they wish to solve. These three major contributions present a significant addition to the body of knowledge for noisy fitness functions. Indeed, this thesis is the first work specifically to examine the implications of noise strength that varies throughout the search domain for a variety of noise landscapes, and thus starts to fill a large void in the literature on noisy fitness functions.
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Khaleel, Ali. "Optimisation of a Hadoop cluster based on SDN in cloud computing for big data applications." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17076.
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Big data has received a great deal attention from many sectors, including academia, industry and government. The Hadoop framework has emerged for supporting its storage and analysis using the MapReduce programming module. However, this framework is a complex system that has more than 150 parameters and some of them can exert a considerable effect on the performance of a Hadoop job. The optimum tuning of the Hadoop parameters is a difficult task as well as being time consuming. In this thesis, an optimisation approach is presented to improve the performance of a Hadoop framework by setting the values of the Hadoop parameters automatically. Specifically, genetic programming is used to construct a fitness function that represents the interrelations among the Hadoop parameters. Then, a genetic algorithm is employed to search for the optimum or near the optimum values of the Hadoop parameters. A Hadoop cluster is configured on two severe at Brunel University London to evaluate the performance of the proposed optimisation approach. The experimental results show that the performance of a Hadoop MapReduce job for 20 GB on Word Count Application is improved by 69.63% and 30.31% when compared to the default settings and state of the art, respectively. Whilst on Tera sort application, it is improved by 73.39% and 55.93%. For better optimisation, SDN is also employed to improve the performance of a Hadoop job. The experimental results show that the performance of a Hadoop job in SDN network for 50 GB is improved by 32.8% when compared to traditional network. Whilst on Tera sort application, the improvement for 50 GB is on average 38.7%. An effective computing platform is also presented in this thesis to support solar irradiation data analytics. It is built based on RHIPE to provide fast analysis and calculation for solar irradiation datasets. The performance of RHIPE is compared with the R language in terms of accuracy, scalability and speedup. The speed up of RHIPE is evaluated by Gustafson's Law, which is revised to enhance the performance of the parallel computation on intensive irradiation data sets in a cluster computing environment like Hadoop. The performance of the proposed work is evaluated using a Hadoop cluster based on the Microsoft azure cloud and the experimental results show that RHIPE provides considerable improvements over the R language. Finally, an effective routing algorithm based on SDN to improve the performance of a Hadoop job in a large scale cluster in a data centre network is presented. The proposed algorithm is used to improve the performance of a Hadoop job during the shuffle phase by allocating efficient paths for each shuffling flow, according to the network resources demand of each flow as well as their size and number. Furthermore, it is also employed to allocate alternative paths for each shuffling flow in the case of any link crashing or failure. This algorithm is evaluated by two network topologies, namely, fat tree and leaf-spine, built by EstiNet emulator software. The experimental results show that the proposed approach improves the performance of a Hadoop job in a data centre network.
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Svobodová, Jitka. "Neuronové sítě a evoluční algoritmy." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218221.
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Objective of this master's thesis is optimizing of neral network topology using some of evolutionary algorithms. The backpropagation neural network was optimized using genetic algorithms, evolutionary programming and evolutionary strategies. The text contains an application in the Matlab environment which applies these methods to simple tasks as pattern recognition and function prediction. Created graphs of fitness and error functions are included as a result of this thesis.
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Basma, Hussein. "Energy management strategies for battery electric bus fleet." Thesis, Université Paris sciences et lettres, 2020. http://thesesprivees.mines-paristech.fr/2020/2020UPSLM036_archivage.pdf.
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Les bus électriques à batterie (BEB) représentent une solution prometteuse pour remplacer les flottes de bus diesel actuelles consommant des énergies fossiles grâce à leur efficacité énergétique élevée et à leur potentiel de réduction des émissions de gaz à effet de serre et à l’absence d’émissions de polluants atmosphériques locaux. Cependant, cette technologie doit faire face à plusieurs défis, en particulier le coût total de possession (TCO) élevé et des contraintes opérationnelles comme l’autonomie des bus, le temps et le lieu de recharge. Cette thèse présente une méthodologie systématique qui vise à développer des solutions pour surmonter ces défis en fournissant un dimensionnement des batteries et une stratégie de recharge optimales pour les BEB. D'abord, un modèle énergétique multi-physique de bus est développé pour évaluer ses besoins énergétiques en prenant en considération ses différents systèmes énergétiques. Ensuite, la consommation d'énergie du bus est évaluée dans plusieurs conditions de fonctionnement afin de quantifier sa consommation d'énergie réelle. Un modèle techno-économique d'une ligne de bus est développé afin d'évaluer l'impact des différentes stratégies de dimensionnement et de recharge des batteries sur les coûts et le fonctionnement du BEB. Ensuite, un modèle TCO est introduit en tenant compte les coûts unitaires BEB, les coûts d'achat et de remplacement des batteries, les coûts d'électricité, les coûts d'infrastructure et de maintenance. L'analyse des résultats d’un cas d’étude à Paris souligne les compromis entre le TCO et les perturbations et les retards des horaires du BEB en fonction des différentes tailles de batterie et stratégies de recharge. Enfin, une méthodologie minimisant le TCO est proposée en déterminant un dimensionnement des batteries et une stratégie de recharge optimales pour la flotte de BEB tout en garantissant l'absence de perturbation des horaires ou des interruptions du service. Elle repose sur une optimisation en deux étapes qui utilise à la fois la programmation dynamique et un algorithme génétique. Les résultats montrent que la méthodologie proposée pourrait réduire le TCO du BEB entre 15-25% par rapport aux approches actuellement adoptéesInitiatives to decrease emissions from the transport sector are increasing worldwide by seeking alternative technologies to replace oil-based mobility. Battery Electric Buses (BEB) present a promising solution thanks to their high energy efficiency, low greenhouse gas emissions and the absence of local pollutant emissions. However, this technology still faces many challenges, especially its high total cost of ownership (TCO) and other operational factors such as the limited bus driving range, the high energy refueling time, and the required charging technologies and strategies. In this context, this thesis presents a systematic methodology that aims at developing solutions to help overcoming these challenges by providing optimal battery sizing and charging strategy for BEB. First, a comprehensive multi-physical bus energy model is developed to evaluate its energy needs considering all the energy systems encountered within. The energy consumption of the bus is then evaluated at a variety of operating conditions. Then, a techno-economic model of an entire bus line is developed in order to assess the impact of different battery sizing and charging strategies on the costs and operation of BEB. A TCO model is introduced considering the BEB unit costs, battery purchase and replacement costs, energy and power costs, infrastructure, and maintenance costs. A case study in Paris city is presented and the analysis reveals the resulting tradeoff between the TCO and BEB schedule disruptions and delays as function of different battery sizes and charging strategies. A methodology to minimize the TCO of BEB deployment is presented providing the optimal battery sizing and charging strategy for BEB, while respecting the BEB operation constraints. The methodology is a 2-step optimization algorithm that utilizes both Dynamic programming and Genetic Algorithm optimization routines. The results show that the proposed methodology could reduce the BEB TCO between 15-25% compared to the currently adopted approaches to deploy BEB
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Amein, Hussein Aly Abbass. "Computational intelligence techniques for decision making : with applications to the dairy industry." Thesis, Queensland University of Technology, 2000. https://eprints.qut.edu.au/36867/1/36867_Digitised%20Thesis.pdf.
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Renaud-Goud, Paul. "Energy-aware scheduling : complexity and algorithms." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00744247.
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In this thesis we have tackled a few scheduling problems under energy constraint, since the energy issue is becoming crucial, for both economical and environmental reasons. In the first chapter, we exhibit tight bounds on the energy metric of a classical algorithm that minimizes the makespan of independent tasks. In the second chapter, we schedule several independent but concurrent pipelined applications and address problems combining multiple criteria, which are period, latency and energy. We perform an exhaustive complexity study and describe the performance of new heuristics. In the third chapter, we study the replica placement problem in a tree network. We try to minimize the energy consumption in a dynamic frame. After a complexity study, we confirm the quality of our heuristics through a complete set of simulations. In the fourth chapter, we come back to streaming applications, but in the form of series-parallel graphs, and try to map them onto a chip multiprocessor. The design of a polynomial algorithm on a simple problem allows us to derive heuristics on the most general problem, whose NP-completeness has been proven. In the fifth chapter, we study energy bounds of different routing policies in chip multiprocessors, compared to the classical XY routing, and develop new routing heuristics. In the last chapter, we compare the performance of different algorithms of the literature that tackle the problem of mapping DAG applications to minimize the energy consumption.
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Hsu, Han-Hao, and 許涵皓. "Benefit and Cost Analysis of Green Building Renovation Strategies: An application of Genetic Algorithm in Optimization and Multi-objective Programming." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/45069779107081727772.
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碩士國立臺灣大學
生物環境系統工程學研究所
101
Nowadays, there are various choices of reformation strategies for saving energy for existing buildings. The different building types and designs also have the most appropriate and efficient combination of renovation strategies. However, in the past, the selection of strategies can only rely on architects’ experiences without any systemic principle. Thus, this study attempts to obtain the best combination of reformation strategies in fixed cost through the simulation model, EnergyPlus coupled with genetic algorithm. The former is utilized to analyze the annual energy consumption for improving the optimization of reformation strategies, and the latter is used to make a proper choice among alternatives. In addition, the final decision will be affected by both the energy saving and the cost of reformation strategies during the building renovation process. Hence, this study also makes an effort to find a trade-off line for a collision between two objectives to provide a basis of selection, by means of the constraint method and weight method in multi-objective programming for selections of reformation strategies. The results represent that the use of Energyplus coupled with genetic algorithm for the application of selections of building renovation strategies can effectively shorten the searching time. Besides, albeit the selected combination of reformation strategies is not guaranteed to be the best, it will be close to the optimal solution at least. Finally, the trade-off line derived from multi-objective programming can effectively estimate the correlation between the use cost and the decrease of energy consumption to offer a basis of selection of strategies to decision-makers.
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Lin, Jung Yi, and 林忠億. "Layered Multi-Population Genetic Programming And Its Applications." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/69613223870510998398.
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博士國立交通大學
資訊科學與工程研究所
95
This study focuses on a proposed method based on genetic programming (GP). Genetic programming is a prominent technique of evolutionary computation (EC). It mimics the evolution mechanism of biological environment to determine optimal solutions for given training instances. Many researchers have been devoted to enhance effectiveness and efficiency of genetic programming. The applications of the proposed method include classification and feature processing. Classification problems play an important role in the development of knowledge engineering. Hidden relations that can be used as a basis for classification are often unclear and not easily elucidated. Thus, many machine learning algorithms have arisen to solve such problems. Feature selection and feature generation are two important techniques dealing with features. Feature selection is capable of removing useless, irrelevant, redundant, and noisy features. Feature generation generates new useful features that could improve classification accuracy. In this study we propose a layered multi-population genetic programming method to solve classification problems. The proposed method that can complete feature selection and feature construction simultaneously is also proposed. The layered multipopulation genetic programming method employs layer architecture to arrange multiple populations. A layer is composed of a number of populations. Each population evolves to generate a discriminant function. A set of discriminant functions generated by one layer will be integrated and be transformed by the successive layer. To improve the learning performance, an adaptive mutation probability tuning method is proposed. Moreover, a statistical-based method is proposed to solve multi-category classification problems. Several experiments on classical classification problems and real-world medical problems are conducted using different configurations. Experimental results show that the proposed methods are accurate and effective.
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Yeh, Chia-Hsuan, and 葉佳炫. "Agent-Based Modeling of Macroeconomics: Applications of Genetic Programming." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/57552254449410823228.
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博士國立政治大學
經濟學系
87
By means of the development of evolutionary computation, we have the chance to analyze economic system by relaxing the representative agent assumption. The important phenomena in economy, coordination and coevolution, can be observed and studied in the framework. However, this can''t be done in traditional analytical methods. From the agent-based point of view, we can understand why and how the coordination succeeds (or fails), and how the environment factors influence the economic dynamic process. Furthermore, we can see that the representative agent could be evolved. That means the representative agent is a result of evolution rather than proper assumption. Also, it doesn''t guarantee that the representative agent is a natural result. The technique used in this dissertation is genetic programming (GP). The introduction about genetic programming is given briefly. The economic interpretations of using genetic programming are also detailed. From the simulations results, we can observe that the issues unsolved in macroeconomics, convergence of equilibrium in a heterogeneous agents environment and the equilibrium selection, can be analyzed. Moreover, our GP-based learning agents can replicate the results obtained from laboratory experiments with human subjects. The rich dynamic phenomena are also observed in the simulations. Theses motivate us to rethink about the role of learning behavior in economic system and the concept of equilibrium.
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Huang, Chih-Chun, and 黃植群. "A Study of Business Valuation Model and Trading Strategies Based on Genetic Programming." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/13785468229916279601.
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碩士輔仁大學
資訊管理學系
93
This study applied Genetic Programming (GP) in the modelling of business valuation. Through the essences of GP, like robustness, domain independence and ability to search for satisficing solutions in solving complicated nonlinear problems, this study hoped that the evolved GP models could have a better applicability and accuracy of evaluations. Futhermore, this study tried to include not only the financial ratios but indicators of business valuation and the intellectual capital. The importance of the intellectual capital is dramatically rising day after day specially to the knowledge-based and innovated enterprises. The purpose of this study also tried to apply the generated models in the Taiwan Stock Exchange that helps investors with forming trading strategies, in order to examine the practical usefulness. The results of experiments showed that the integrated model brought into indicators of business valuation and the intellectual capital compared to the model simply with financial ratios would have a better stability and performance of investments. While comparing buy-and-hold strategy with dynamic trading strategy, buy-and-hold strategy is not hedging, hence it gained more in the Bull market. However, in the Bear market dynamic trading strategy would obtain more than buy-and-hold strategy because of the stop loss mechanism. In the Bear market, dynamic trading strategy generated by GP even could have profitabilities when Industry Indexes returns as well as Taiwan Stock Index returns are negative.
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Dhot, Tarundeep Singh. "GPIS: genetic programming based image segmentation with applications to biomedical object detection." Thesis, 2009. http://spectrum.library.concordia.ca/976222/1/MR63236.pdf.
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Image segmentation plays a critical role in many image analysis applications. However, it is ill-defined in nature and remains one of the most intractable problems in image processing. In this thesis, we propose a genetic programming based algorithm for image segmentation (GPIS). Typically, genetic programming is a Darwinian-evolution inspired program discovery method and in the past it has been successfully used as an automatic programming tool. We make use of this property of GP to evolve efficient and accurate image segmentation programs from a pool of basic image analysis operators. In addition, we provide no a priori information about that nature of the images to the GP. The algorithm was tested on two separate medical image databases and results show the proposed GP's ability to adapt and produce short and accurate segmentation algorithms, irrespective of the database in use. We compared our results with a popular GA based image segmentation/classification system, GENIE Pro. We found that our proposed algorithm produced accurate image segmentations performed consistently on both databases and could possibly be extended to other image databases as a general-purpose image segmentation tool.
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Sastry, Kumara Narasimha. "Genetic algorithms and genetic programming for multiscale modeling : applications in materials science and chemistry and advances in scalability /." 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3290369.
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Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 2007.Source: Dissertation Abstracts International, Volume: 68-11, Section: B, page: 7640. Advisers: David E. Goldberg; Duane D. Johnson. Includes bibliographical references (leaves 188-205). Available on microfilm from Pro Quest Information and Learning.
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"Accelerated strategies of evolutionary algorithms for optimization problem and their applications." 2003. http://library.cuhk.edu.hk/record=b6073589.
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by Yong Liang."November 2003."
Thesis (Ph.D.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (p. 237-266).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Mode of access: World Wide Web.
Abstracts in English and Chinese.
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Vermersch, Polly Smith. "Genetic strategies for analyzing proteins: Applications utilizing the R388 type II dihydrofolate reductase." Thesis, 1988. http://hdl.handle.net/1911/16196.
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Applications of recombinant DNA technology to protein analysis have been demonstrated using the trimethoprim resistant (Tp$\sp{\rm R}$)R388 type II dihydrofolate reductase (DHFR).
DHFR fusion vectors were constructed which contain cloning sites in the C-terminal coding region of the DHFR. Segments of nematode major sperm protein (MSP) fused to the DHFR were recognized by antibody to MSP. A 15 aa segment of MSP sequence, fused to the DHFR, was shown to be sufficient to elicit antibody to MSP. A two-step procedure was developed for purifying the DHFR proteins.
A selectable FokI/supF cassette was developed to facilitate DNA excision and replacement mutagenesis. When the cassette is inserted into DNA, the presence of the tyrosine tRNA suppressor gene (supF) contained on the cassette is selected by the suppression of amber mutations in the recipient host. Subsequent refined mutagenesis is possible due to the unique cleavage properties of FokI. The cassette/vector system was used to produce a deletion corresponding to amino acid residues 2-7 of the DHFR which did not noticeably impair Tp$\sp{\rm R}$. Resistance was abolished, however, by a deletion of amino acid residues 2-21.
A structural gene was synthesized which contains many unique restriction sites and encodes a Tp$\sp{\rm R}$ DHFR which is 10 aa shorter at the N-terminus relative to natural type II DHFRs. High copy number vectors which utilize strong promoters to transcribe the dhfr gene and the primer for plasmid replication were constructed to overproduce the mini DHFR and a full-length derivative.
Site-directed mutagenesis was used to test the importance of glu-58 and thr-48 in a putative folate binding site of the mini and full-length DHFRs. The introduction of thr-58 and/or glu-48 destroyed in vivo function of the DHFRs. Tp$\sp{\rm R}$ was retained in the full length gln-58 R388 DHFR, but not in the mini gln-58 DHFR. Through random mutagenesis a mini Tp$\sp{\rm R}$gln-58 R388 DHFR was obtained that contained a duplication of leu-pro-ser, at the N-terminus. Successive additions of the leu-pro-ser triplet to the N-terminus appear to stabilize the functional form of the mini DHFR.
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Shukla, Anshu. "Benchmarking and Scheduling Strategies for Distributed Stream Processing." Thesis, 2017. http://etd.iisc.ernet.in/2005/3984.
Full textAbstract:
The velocity dimension of Big Data refers to the need to rapidly process data that arrives continuously as streams of messages or events. Distributed Stream Processing Systems (DSPS) refer to distributed programming and runtime platforms that allow users to define a composition of dataflow logic that are executed on distributed resources over streams of incoming messages.
A DSPS uses commodity clusters and Cloud Virtual Machines (VMs) for its execution. In order to meet the required performance for these applications, the DSPS needs to schedule these dataßows efficiently over the resources. Despite their growing use, resource scheduling for DSPSÕs tends to be done in an ad hoc manner, favoring empirical and reactive approaches, rather than a model-driven and analytical approach. Such empirical strategies may arrive at an approximate schedule for the dataflow that needs further tuning to meet the quality of service.
We propose a model-based scheduling approach that makes use of performance profiles and benchmarks developed for tasks in the dataßow to plan both the resource allocation and the resource mapping that together form the schedule planning process. We propose the Model Based Allocation (MBA) and the Slot Aware Mapping (SAM) approaches that efectively utilize knowledge of the performance model of logic tasks to provide an efficient and predictable scheduling behavior. We implemented and validate these algorithms using the popular open source Apache Storm DSPS for several micro and application dataflows. The results show that our model-driven approach is able to reduce the amount of required resources (VMs) by 30% − 50% relative to existing techniques. Also we see that our strategies o↵er a predictable behavior that ensures that the expected and actual rates supported and resources used match closely. This can enable deterministic schedule planning even under dynamic conditions.
Besides this static scheduling, we also examine the ability to dynamically consolidate tasks onto fewer VMs when the load on the dataßow decreases or the VMs get fragmented. We propose reliable task migration models for Apache Storm dataßows that are able to rapidly move the task assignment in the cluster, and resume the dataflow execution without any message loss.