Bibliographies thématiques / Genetic programming, strategies, applications

Littérature scientifique sur le sujet « Genetic programming, strategies, applications »

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Publié le 11 mars 2023

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Articles de revues sur le sujet "Genetic programming, strategies, applications"

Ni, He, Fan Ming Zeng, Bo Yu et Feng Rui Sun. « The Convergence Mechanism and Strategies for Non-Elitist Genetic Programming ». Applied Mechanics and Materials 347-350 (août 2013) : 3850–60. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3850.

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Genetic programming is an evolutionary algorithm that proposed to solve the automatic computer program design problem by J.R.Koza in the 1990s. It has good universality and intelligence, and has been widely applied in the field of computer engineering. But genetic programming is essentially a stochastic optimization algorithm, lack theoretic basis on the convergence of algorithm, which limit the scope of its application in some extent. The convergence mechanism of non-elitist genetic programming was studied in this paper. A recursive estimation of the probability of population contains satisfactory solution with the evolution algebra was established by the analysis of operators characteristic parameters, then a sufficient condition of population converge in probability was derived from this estimation, and thereby some operational convergence strategies for many common evolution modes were provided.

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Zhang, Biaobiao, Yue Wu, Jiabin Lu et K. L. Du. « Evolutionary Computation and Its Applications in Neural and Fuzzy Systems ». Applied Computational Intelligence and Soft Computing 2011 (2011) : 1–20. http://dx.doi.org/10.1155/2011/938240.

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Li, He, et Naiyu Shi. « Application of Genetic Optimization Algorithm in Financial Portfolio Problem ». Computational Intelligence and Neuroscience 2022 (15 juillet 2022) : 1–9. http://dx.doi.org/10.1155/2022/5246309.

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In order to address the application of genetic optimization algorithms to financial investment portfolio issues, the optimal allocation rate must be high and the risk is low. This paper uses quadratic programming algorithms and genetic algorithms as well as quadratic programming algorithms, Matlab planning solutions for genetic algorithms, and genetic algorithm toolboxes to solve Markowitz’s mean variance model. The mathematical model for introducing sparse portfolio strategies uses the decomposition method of penalty functions as an algorithm for solving nonconvex sparse optimization strategies to solve financial portfolio problems. The merging speed of the quadratic programming algorithm is fast, and the merging speed depends on the selection of the initial value. The genetic algorithm performs very well in global searches, but local search capabilities are insufficient and the pace of integration into the next stage is slow. To solve this, using a genetic algorithm toolbox is quick and easy. The results of the experiments show that the final solution of the decomposition method of the fine function is consistent with the solution of the integrity of the genetic algorithm. 67% of the total funds will be spent on local car reserves and 33% on wine reserves. When data scales are small, quadratic programming algorithms and genetic algorithms can provide effective portfolio feedback, and the method of breaking down penalty functions to ensure the reliability and effectiveness of algorithm combinations is widely used in sparse financial portfolio issues.

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Sokolov, Artem, Darrell Whitley et Andre’ da Motta Salles Barreto. « A note on the variance of rank-based selection strategies for genetic algorithms and genetic programming ». Genetic Programming and Evolvable Machines 8, n^o 3 (31 juillet 2007) : 221–37. http://dx.doi.org/10.1007/s10710-007-9030-1.

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KABOUDAN, MAK. « EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS ». New Mathematics and Natural Computation 01, n^o 01 (mars 2005) : 79–107. http://dx.doi.org/10.1142/s1793005705000056.

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Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

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Niño, Elías, Carlos Ardila, Alfredo Perez et Yezid Donoso. « A Genetic Algorithm for Multiobjective Hard Scheduling Optimization ». International Journal of Computers Communications & ; Control 5, n^o 5 (1 décembre 2010) : 825. http://dx.doi.org/10.15837/ijccc.2010.5.2243.

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This paper proposes a genetic algorithm for multiobjective scheduling optimization based in the object oriented design with constrains on delivery times, process precedence and resource availability. Initially, the programming algorithm (PA) was designed and implemented, taking into account all constraints mentioned. This algorithm’s main objective is, given a sequence of production orders, products and processes, calculate its total programming cost and time. Once the programming algorithm was defined, the genetic algorithm (GA) was developed for minimizing two objectives: delivery times and total programming cost. The stages defined for this algorithm were: selection, crossover and mutation. During the first stage, the individuals composing the next generation are selected using a strong dominance test. Given the strong restrictions on the model, the crossover stage utilizes a process level structure (PLS) where processes are grouped by its levels in the product tree. Finally during the mutation stage, the solutions are modified in two different ways (selected in a random fashion): changing the selection of the resources of one process and organizing the processes by its execution time by level. In order to obtain more variability in the found solutions, the production orders and the products are organized with activity planning rules such as EDD, SPT and LPT. For each level of processes, the processes are organized by its processing time from lower to higher (PLU), from higher to lower (PUL), randomly (PR), and by local search (LS). As strategies for local search, three algorithms were implemented: Tabu Search (TS), Simulated Annealing (SA) and Exchange Deterministic Algorithm (EDA). The purpose of the local search is to organize the processes in such a way that minimizes the total execution time of the level. Finally, Pareto fronts are used to show the obtained results of applying each of the specified strategies. Results are analyzed and compared.

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Chernov, Ivan E., et Andrey V. Kurov. « APPLICATION OF GENETIC ALGORITHMS IN CRYPTOGRAPHY ». RSUH/RGGU Bulletin. Series Information Science. Information Security. Mathematics, n^o 1 (2022) : 63–82. http://dx.doi.org/10.28995/2686-679x-2022-1-63-82.

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Currently in the development of computer technologies that ensure information security and information protection, cryptographic methods of protection are widely used. The main tasks in cryptography are the development of new encryption features, difficult to break and repetitive ciphers. To solve that problem, falling into the class of NP-complete ones, algorithms based on natural principles have been used in recent years. These include genetic algorithms (GA), evolutionary methods, swarm intelligence algorithms. In models and algorithms of evolutionary computations, the construction of basic models and rules is implemented, according to which it can change (evolve). In recent years, evolutionary computing schemes have been proposed, including the genetic algorithm, genetic programming, evolutionary programming, and evolutionary strategies. The paper discusses the existing cryptography methods, basic concepts and methods of modern cryptography, the notion of a genetic algorithm, a universal hash function, as well as a hash detection method and a genetic hashing algorithm built on it. A genetic algorithm was implemented in the Golang language, modified for the current problem of finding the optimal hash functions. A detailed description of each stage of the algorithm execution is given. Also, within the framework of the research, a study of the function of the genetic algorithm itself and the genetic hashing algorithm was carried out, evaluating the convergence of the genetic algorithm depending on the input data, and evaluating the possible direction of further research.

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FREY, CLEMENS. « CO-EVOLUTION OF FINITE STATE MACHINES FOR OPTIMIZATION : PROMOTION OF DEVICES WHICH SEARCH GLOBALLY ». International Journal of Computational Intelligence and Applications 02, n^o 01 (mars 2002) : 1–16. http://dx.doi.org/10.1142/s1469026802000397.

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In this work a co-evolutionary approach is used in conjunction with Genetic Programming operators in order to find certain transition rules for two-step discrete dynamical systems. This issue is similar to the well-known artificial-ant problem. We seek the dynamic system to produce a trajectory leading from given initial values to a maximum of a given spatial functional.This problem is recast into the framework of input-output relations for controllers, and the optimization is performed on program trees describing input filters and finite state machines incorporated by these controllers simultaneously. In the context of Genetic Programming there is always a set of test cases which has to be maintained for the evaluation of program trees. These test cases are subject to evolution here, too, so we employ a so-called host-parasitoid model in order to evolve optimizing dynamical systems.Reinterpreting these systems as algorithms for finding the maximum of a functional under constraints, we have derived a paradigm for the automatic generation of adapted optimization algorithms via optimal control. We provide numerical examples generated by the GP-system MathEvEco. These examples refer to key properties of the resulting strategies and they include statistical evidence showing that for this problem of system identification the co-evolutionary approach is superior to standard Genetic Programming.

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Edmondson, Michael C., L. Tang et A. Kern. « Software Development Modules for Microcontroller-Based System ». Advances in Science and Technology 56 (septembre 2008) : 45–51. http://dx.doi.org/10.4028/www.scientific.net/ast.56.45.

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Microcontrollers are small devices commonly used for control purposes over a wide range of applications. As the control strategies and the selection of hardware differ from one application to another, it is a common practice for engineers to develop the application programs based-on the selected hardware and the control methodologies. Such development process requires time for programming and testing, especially for large projects which need to interface and integrate with a number of different hardware and software. This paper presents an approach using the concept of soft development modules to shorten the application program development time for control systems using microcontrollers. A set of soft modules has been developed for a widely used microcontroller. The testing conducted on the serial communication and fuzzy logic control modules successfully produced application programs within a much shorter time, and reduced human programming errors. The application of soft development modules will allow system developers to focus on the system design level without the need to spend large amount of time on generic programming details. Such an approach provides a useful programming development platform for future applications using microcontrollers.

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GAGNÉ, CHRISTIAN, et MARC PARIZEAU. « GENERICITY IN EVOLUTIONARY COMPUTATION SOFTWARE TOOLS : PRINCIPLES AND CASE-STUDY ». International Journal on Artificial Intelligence Tools 15, n^o 02 (avril 2006) : 173–94. http://dx.doi.org/10.1142/s021821300600262x.

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This paper deals with the need for generic software development tools in evolutionary computations (EC). These tools will be essential for the next generation of evolutionary algorithms where application designers and researchers will need to mix different combinations of traditional EC (e.g. genetic algorithms, genetic programming, evolutionary strategies, etc.), or to create new variations of these EC, in order to solve complex real world problems. Six basic principles are proposed to guide the development of such tools. These principles are then used to evaluate six freely available, widely used EC software tools. Finally, the design of Open BEAGLE, the framework developed by the authors, is presented in more detail.

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Thèses sur le sujet "Genetic programming, strategies, applications"

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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2006/2007
In 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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2012/2013
Machine 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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Livres sur le sujet "Genetic programming, strategies, applications"

Kaisa, Miettinen, dir. Evolutionary algorithms in engineering and computer science : Recent advances in genetic algorithms, evolution strategies, evolutionary programming, genetic programming, and industrial applications. Chichester : Wiley, 1999.

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Gandomi, Amir H., Amir H. Alavi et Conor Ryan, dir. Handbook of Genetic Programming Applications. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20883-1.

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Michael, Affenzeller, dir. Genetic algorithms and genetic programming : Modern concepts and practical applications. Boca Raton : Chapman & Hall/CRC, 2009.

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Genetic and evolutionary computation : Medical applications. Chichester, U.K : Wiley, 2010.

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L, Karr C., et Freeman L. M, dir. Industrial applications of genetic algorithms. Boca Raton, FL : CRC Press, 1999.

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Maulik, Sunil. Molecular biotechnology : Therapeutic applications and strategies. New York : Wiley-Liss, 1997.

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David, North, et Mayfield Mark, dir. Object models : Strategies, patterns, and applications. 2^e éd. Upper Saddle River, N.J : Yourdon Press, 1997.

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David, North, et Mayfield Mark, dir. Object models : Strategies, patterns, and applications. Englewood Cliffs., N.J : Yourdon Press, 1995.

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Wong, Man Leung. Data mining using grammar based genetic programming and applications. Boston : Kluwer Academic, 2000.

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Jürgen, Borlak, dir. Handbook of toxicogenomics : Strategies and applications. Weinheim : Wiley-VCH, 2005.

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Chapitres de livres sur le sujet "Genetic programming, strategies, applications"

Gypteau, Jeremie, Fernando E. B. Otero et Michael Kampouridis. « Generating Directional Change Based Trading Strategies with Genetic Programming ». Dans Applications of Evolutionary Computation, 267–78. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3_22.

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Swan, Jerry, Krzysztof Krawiec et Neil Ghani. « Polytypic Genetic Programming ». Dans Applications of Evolutionary Computation, 66–81. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55792-2_5.

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Smith, Stephen L., James Alfred Walker et Julian F. Miller. « Medical Applications of Cartesian Genetic Programming ». Dans Cartesian Genetic Programming, 309–36. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17310-3_11.

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Dracopoulos, Dimitris C. « Genetic Algorithms and Genetic Programming for Control ». Dans Evolutionary Algorithms in Engineering Applications, 329–43. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03423-1_19.

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Jackson, David. « Evolving Defence Strategies by Genetic Programming ». Dans Lecture Notes in Computer Science, 281–90. Berlin, Heidelberg : Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31989-4_25.

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Aley, Rob. « Strategies for High-Performance Applications ». Dans Pro Functional PHP Programming, 103–45. Berkeley, CA : Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2958-3_5.

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Monsieurs, Patrick, et Eddy Flerackers. « Reducing Bloat in Genetic Programming ». Dans Computational Intelligence. Theory and Applications, 471–78. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45493-4_48.

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Louis, Sushil J., et Gan Li. « Combining robot control strategies using genetic algorithms with memory ». Dans Evolutionary Programming VI, 431–41. Berlin, Heidelberg : Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0014831.

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Schniederjans, Marc J. « Goal Programming Model Formulation Strategies ». Dans Goal Programming : Methodology and Applications, 21–44. Boston, MA : Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-2229-4_2.

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Johnson, Colin G. « Genetic Programming with Guaranteed Constraints ». Dans Applications and Science in Soft Computing, 95–100. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-45240-9_14.

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Actes de conférences sur le sujet "Genetic programming, strategies, applications"

Ono, Keiko, et Yoshiko Hanada. « Assembling bloat control strategies in genetic programming for image noise reduction ». Dans 2014 14th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE, 2014. http://dx.doi.org/10.1109/isda.2014.7066279.

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Bokhari, Syed, et Oliver Theel. « Design of Scenario-based Application-optimized Data Replication Strategies through Genetic Programming ». Dans 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008955301200129.

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Funie, Andreea-Ingrid, Mark Salmon et Wayne Luk. « A Hybrid Genetic-Programming Swarm-Optimisation Approach for Examining the Nature and Stability of High Frequency Trading Strategies ». Dans 2014 13th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2014. http://dx.doi.org/10.1109/icmla.2014.11.

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Raeder, Mateus, Dalvan Griebler, Lucas Baldo et Luiz Gustavo Fernandes. « Performance Prediction of Parallel Applications with Parallel Patterns using Stochastic Methods ». Dans Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2011. http://dx.doi.org/10.5753/wscad.2011.17273.

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One of the main problems in the high performance computing area is to find the best strategy to parallelize an application. In this context, the use of analytical methods to evaluate the performance behavior before the real implementation of such applications seems to be an interesting alternative and can help to identify better directions for the implementation strategies. In this work, the Stochastic Automata Network (SAN) formalism is adopted to model and evaluate the performance of parallel applications. The methodology used is based on the construction of generic SAN models to describe classical parallel programming patterns, like Master/Slave, Pipeline and Divide and Conquer. Those models are adapted to represent cases of a real application through the definition of input parameters values. Finally, we present a comparison between the results of the SAN models and a real application, aiming at verifying the accuracy of the adopted technique.

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Birk, Lothar, Gu¨nther F. Clauss et June Y. Lee. « Practical Application of Global Optimization to the Design of Offshore Structures ». Dans ASME 2004 23rd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2004. http://dx.doi.org/10.1115/omae2004-51225.

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The paper presents improved methods and new results on the introduction of formal optimization strategies into the design of offshore structures. The hull design stage is singled out from the overall design process and automated by introducing parametric shape generation, numeric hydrodynamic analysis and assessment tools as well as Nonlinear Programming algorithms for process control. The investigation compares the performance of three different optimization algorithms within a shape optimization framework. The classical deterministic Sequential Quadratic Programming method competes with two so called global optimization algorithms: The popular Genetic Algorithm and the more exotic Adaptive Simulated Annealing. The applications show that significant improvements of seakeeping qualities are obtained in either case. As expected, the global methods require definitely more computation time than the deterministic algorithm. Furthermore the global methods do not always produce better results, which makes a careful choice of the optimization algorithm mandatory. Guidelines for an efficient application are given in the conclusions.

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« Session details : Evolution strategies, evolutionary programming : poster ». Dans GECCO06 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2006. http://dx.doi.org/10.1145/3249649.

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« Session details : Evolutionary strategies and evolutionary programming ». Dans GECCO05 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2005. http://dx.doi.org/10.1145/3249407.

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« Session details : Evolution strategies, evolutionary programming : papers ». Dans GECCO06 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2006. http://dx.doi.org/10.1145/3249648.

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Alonso, César L., José Luis Montaña et Cruz Enrique Borges. « Evolution Strategies for Constants Optimization in Genetic Programming ». Dans 2009 21st IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2009. http://dx.doi.org/10.1109/ictai.2009.35.

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Rapports d'organisations sur le sujet "Genetic programming, strategies, applications"

Spector, Lee. Multi-Type Self Adaptive Genetic Programming for Complex Applications. Fort Belvoir, VA : Defense Technical Information Center, mars 2005. http://dx.doi.org/10.21236/ada432974.

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Lapidot, Moshe, Linda Hanley-Bowdoin, Jane E. Polston et Moshe Reuveni. Geminivirus-resistant Tomato Plants : Combining Transgenic and Conventional Strategies for Multi-viral Resistance. United States Department of Agriculture, décembre 2010. http://dx.doi.org/10.32747/2010.7592639.bard.

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Begomoviruses, which constitute one genus of the Geminiviridae family, are single-stranded DNA viruses that infect many dicotyledonous crops important to large agricultural industries as well as to subsistence growers. Although all begomoviruses are transmitted by whiteflies (Bemisia tabaci), they have proven difficult to manage even with heavy insecticide applications. The begomovirus, Tomato yellow leaf curl virus (TYLCV), has been a problem in tomato production in Israel since the 1950s and in the United States since 1997. Approximately 89 begomoviruses have now been reported to infect tomato. Crop losses due to begomoviruses such as TYLCV and Tomato mottle virus (ToMoV), are limiting factors in tomato cultivation in Israel, the U.S., and many tomato-growing regions throughout the world. To overcome these limitations, we proposed a two-step strategy that combines transgenic and conventional resistance in order to develop tomato plants that are resistant to multiple begomoviruses. In the first step, we have developed transgenic tomato plants expressing trans-dominant interfering mutants Rep and C3 from TYLCV and ToMoV, and tested whether these plants are resistant to infection by these two viruses. In the second step we have tested whether pyramiding transgenic and conventional resistance is superior to either strategy alone. The specific objectives of the proposal were: 1. Design and test trans-dominant interfering constructs for TYLCV and ToMoV Rep and C3 in transient replication interference assays. 2. Generate and test transgenic tomato plants expressing mutant Rep and C3 in resistance assays. 3. Generate and test conventional resistant lines that also express mutant Rep and C3. Two viral replication interfering constructs, expressing the trans-dominant interfering mutants Rep and C3, were designed and constructed during this project. One construct, pNSB1630 was based on TYLCV sequences and the other, pNSB1682, based on ToMoV sequences. The TYLCV transformation construct was tested in a protoplasts replication assay, and was found to inhibit TYLCV replication. The ToMoV transformation construct is yet to be tested in a protoplast assay. Both transformation vectors, pNSB1630 and pNSB1682, were used to transform four different tomato lines, and generate transgenic plants. The tomato lines used for transformation were: FL7613, MM, TY172, TY199. FL7613 and MM are susceptible to both TYLCV and ToMoV. TY172 and TY199 are breeding lines developed at Volcani Center. TY172 is resistant to TYLCV but susceptible to ToMoV, while TY199 is resistant to both TYLCV and ToMoV. When transgenic T1 plants expressing the pNSB1630 constructed were screened for TYLCV resistance, it was found that these plants showed very low level of TYLCV resistance, if any. However, some of these lines showed high level of resistance to ToMoV. Only five transgenic T1 lines expressing the pNSB1682 construct were tested (so far) for resistance to ToMoV. It was found that all five lines express very high level of resistance to ToMoV. Although we haven’t finished (yet) the screen of all the transgenic lines, it is already clear that we were able to successfully combine genetic resistance for TYLCV with transgenic resistance to ToMoV.

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Kahabuka, Catherine, Salum Mshamu, Nrupa Jani et Kamden Hoffmann. Midterm evaluation of USAID Tulonge Afya Project. Population Council, août 2020. http://dx.doi.org/10.31899/sbsr2020.1010.

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This report examines findings from a mid-term evaluation conducted by Breakthrough RESEARCH, a USAID-funded research and evaluation project. The purpose of this evaluation is to provide recommendations to enhance the effectiveness of USAID Tulonge Afya’s strategies and activities as the program enters year three. The evaluation identified successes, promising strategies, and interventions that can be sustained throughout the life of the project and scaled-up to other regions and districts. The evaluation also helps to identify facilitating and limiting factors for implementing a complex, integrated social and behavior change communication (SBCC) program, with applications for the future of this project as well as future integrated SBCC programming globally.

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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences : Tools and Learning Resources. Inter-American Development Bank, février 2022. http://dx.doi.org/10.18235/0003982.

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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

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Ron, Eliora, et Eugene Eugene Nester. Global functional genomics of plant cell transformation by agrobacterium. United States Department of Agriculture, mars 2009. http://dx.doi.org/10.32747/2009.7695860.bard.

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The aim of this study was to carry out a global functional genomics analysis of plant cell transformation by Agrobacterium in order to define and characterize the physiology of Agrobacterium in the acidic environment of a wounded plant. We planed to study the proteome and transcriptome of Agrobacterium in response to a change in pH, from 7.2 to 5.5 and identify genes and circuits directly involved in this change. Bacteria-plant interactions involve a large number of global regulatory systems, which are essential for protection against new stressful conditions. The interaction of bacteria with their hosts has been previously studied by genetic-physiological methods. We wanted to make use of the new capabilities to study these interactions on a global scale, using transcription analysis (transcriptomics, microarrays) and proteomics (2D gel electrophoresis and mass spectrometry). The results provided extensive data on the functional genomics under conditions that partially mimic plant infection and – in addition - revealed some surprising and significant data. Thus, we identified the genes whose expression is modulated when Agrobacterium is grown under the acidic conditions found in the rhizosphere (pH 5.5), an essential environmental factor in Agrobacterium – plant interactions essential for induction of the virulence program by plant signal molecules. Among the 45 genes whose expression was significantly elevated, of special interest is the two-component chromosomally encoded system, ChvG/I which is involved in regulating acid inducible genes. A second exciting system under acid and ChvG/Icontrol is a secretion system for proteins, T6SS, encoded by 14 genes which appears to be important for Rhizobium leguminosarum nodule formation and nitrogen fixation and for virulence of Agrobacterium. The proteome analysis revealed that gamma aminobutyric acid (GABA), a metabolite secreted by wounded plants, induces the synthesis of an Agrobacterium lactonase which degrades the quorum sensing signal, N-acyl homoserine lactone (AHL), resulting in attenuation of virulence. In addition, through a transcriptomic analysis of Agrobacterium growing at the pH of the rhizosphere (pH=5.5), we demonstrated that salicylic acid (SA) a well-studied plant signal molecule important in plant defense, attenuates Agrobacterium virulence in two distinct ways - by down regulating the synthesis of the virulence (vir) genes required for the processing and transfer of the T-DNA and by inducing the same lactonase, which in turn degrades the AHL. Thus, GABA and SA with different molecular structures, induce the expression of these same genes. The identification of genes whose expression is modulated by conditions that mimic plant infection, as well as the identification of regulatory molecules that help control the early stages of infection, advance our understanding of this complex bacterial-plant interaction and has immediate potential applications to modify it. We expect that the data generated by our research will be used to develop novel strategies for the control of crown gall disease. Moreover, these results will also provide the basis for future biotechnological approaches that will use genetic manipulations to improve bacterial-plant interactions, leading to more efficient DNA transfer to recalcitrant plants and robust symbiosis. These advances will, in turn, contribute to plant protection by introducing genes for resistance against other bacteria, pests and environmental stress.

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Hodges, Thomas K., et David Gidoni. Regulated Expression of Yeast FLP Recombinase in Plant Cells. United States Department of Agriculture, septembre 2000. http://dx.doi.org/10.32747/2000.7574341.bard.

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Research activities in both our laboratories were directed toward development of control of the FLP/frt recombination system for plants. As described in the text of the research proposal, the US lab has been engaged in developing regulatory strategies such as tissue-specific promoters and the steroid-inducible activation of the FLP enzyme while the main research activities in Israel have been directed toward the development and testing of a copper-regulated expression of flp recombinase in tobacco (this is an example of a promoter activation by metal ions). The Israeli lab hat additionally completed experiments of previous studies regarding factors affecting the efficiency of recombinase activity using both a gain-of-function assay (excisional-activation of a gusA marker) and loss of function assay (excision of a rolC marker) in tobacco. Site-specific recombinase systems, in particular the FLP/frt and R/RS systems of yeast and the Cre/lox system of bacteriophage P1, have become an essential component of targeted genetic transformation procedures both in animal and plant organisms. To provide more flexibility in transgene excisions by the recombinase systems as well as gene targeting, and to widen possible applications, the development of controlled or regulated recombination systems is highly desirable and was therefore the subject of this research proposal. There are a few possible mechanisms to regulate expression of a recombinase system. They include: 1) control of the recombination system by having the target sites (e.g. frt) in one plant and the flp recombinase gene in another, and bringing the two together by cross fertilization. 2) regulation of promoter activities by external stimuli such as temperature, chemicals, metal ions, etc. 3) regulation of promoter activities by internal signals, i.e. cell- or tissue-specific, or developmental regulation. 4) regulation of enzyme activity by providing cofactors essential for biochemical reactions to take place such as steroid molecules in conjunction with a steroid ligand-binding protein (domains). During the course of this research our major emphasis have been focused toward studying the feasibility of hybrid seed production in Arabidopsis, using FLP/frt. Male-sterility was induced using the antisence of a pollen- and tapetum-specific gene, bcp1, isolated from Arabidopsis. The sterility inducing gene was flanked by frt sites. Upon cross pollination of flowers of male-sterile plants with pollen from FLP-containing plants, viable seeds were produced, and the progeny hybrid plants developed normally. The major achievement from this work is the first demonstration of using a site-specific recombinase to restore fertility in male-sterile plants (see attached paper, Luo et al., Plant J 2000; 23:423-430). The implication from this finding is that site-specific recombination systems can be applied in crop plants as a useful alternative method for hybrid seed production.

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