Увійти

Готові списки джерел за темами / Evolution Grammaticale

Добірка наукової літератури з теми "Evolution Grammaticale"

Автор: Grafiati

Опубліковано: 8 червня 2024

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Evolution Grammaticale".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Зміст

Статті в журналах
Дисертації
Книги
Частини книг
Тези доповідей конференцій

Статті в журналах з теми "Evolution Grammaticale":

1

Botne, Robert. "The Evolution of Future Tenses from Serial 'Say' Constructions in Central Eastern Bantu." Diachronica 15, no. 2 (January 1, 1998): 207–30. http://dx.doi.org/10.1075/dia.15.2.02bot.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

SUMMARY Future tense markers have been shown to arise from a variety of verbal sources, among them motion verbs and volitional verbs. In a small number of central eastern Bantu languages, a verb 'say' has developed into a future marker, a phenomenon not previously noted in the literature. In this study, the author presents a description and analysis of this grammatical shift, proposing two principal paths of evolution: decategorialization and auxiliation. RÉSUMÉ Les marques de temps futur proviennent d'une grande variété de sources verbales, parmi elles les verbes de mouveet et volition. Dans un petit nombre de langues bantoues du centre-est, on trouve un verbe 'dire' qui est devenu une marque de futur, un phénomène non-signalé dans la litterature jusqu'à présent. Dans l'étude actuelle l'auteur présente une description et une analyse de cette modification grammaticale, proposant deux voies principales d'évo-lu-tion: 'decategorialization' et 'auxiliation'. ZUSAMMENFASSUNG Tempusmarkierungen fur das Futur haben bekanntlich eine Reihe ver-schiedenener verbaler Ursprünge, u.a. Verben, die Bewegung oder eine Àb-sicht ausdrücken. In einer kleinen Anzahl von mittelöstlichen Bantusprachen z.B. hat sich des Verb, das gewöhnlich 'sprechen' ausdrückt, zu einem Futur-Markierungszeichen entwickelt, eine Erscheinung, die bisher nicht in der wis-senschaftlichen Literatur aufzufinden gewesen ist. In der vorliegenden Arbeit legt der Autor eine Beschreibung und Erklärung dieses grammtischen Wan-dels vor, in denen er zwei hauptsächliche Entwicklungslinien unterscheidet: 'Entkategorisierung' und 'Hilfszeitwortwerdung'.

2

O'Neill, M., and C. Ryan. "Grammatical evolution." IEEE Transactions on Evolutionary Computation 5, no. 4 (2001): 349–58. http://dx.doi.org/10.1109/4235.942529.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Nicolau, Miguel. "Understanding grammatical evolution: initialisation." Genetic Programming and Evolvable Machines 18, no. 4 (July 25, 2017): 467–507. http://dx.doi.org/10.1007/s10710-017-9309-9.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Bartoli, Alberto, Mauro Castelli, and Eric Medvet. "Weighted Hierarchical Grammatical Evolution." IEEE Transactions on Cybernetics 50, no. 2 (February 2020): 476–88. http://dx.doi.org/10.1109/tcyb.2018.2876563.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Ortega, Alfonso, Marina de la Cruz, and Manuel Alfonseca. "Christiansen Grammar Evolution: Grammatical Evolution With Semantics." IEEE Transactions on Evolutionary Computation 11, no. 1 (February 2007): 77–90. http://dx.doi.org/10.1109/tevc.2006.880327.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Dempsey, Ian, Michael O'Neill, and Anthony Brabazon. "Constant creation in grammatical evolution." International Journal of Innovative Computing and Applications 1, no. 1 (2007): 23. http://dx.doi.org/10.1504/ijica.2007.013399.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

He, Pei, Colin G. Johnson, and HouFeng Wang. "Modeling grammatical evolution by automaton." Science China Information Sciences 54, no. 12 (December 2011): 2544–53. http://dx.doi.org/10.1007/s11432-011-4411-8.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Hugosson, Jonatan, Erik Hemberg, Anthony Brabazon, and Michael O’Neill. "Genotype representations in grammatical evolution." Applied Soft Computing 10, no. 1 (January 2010): 36–43. http://dx.doi.org/10.1016/j.asoc.2009.05.003.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Cathcart, Chundra, Gerd Carling, Filip Larsson, Niklas Johansson, and Erich Round. "Areal pressure in grammatical evolution." Diachronica 35, no. 1 (April 16, 2018): 1–34. http://dx.doi.org/10.1075/dia.16035.cat.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract This article investigates the evolutionary and spatial dynamics of typological characters in 117 Indo-European languages. We partition types of change (i.e., gain or loss) for each variant according to whether they bring about a simplification in morphosyntactic patterns that must be learned, whether they are neutral (i.e., neither simplifying nor introducing complexity) or whether they introduce a more complex pattern. We find that changes which introduce complexity show significantly less areal signal (according to a metric we devise) than changes which simplify and neutral changes, but we find no significant differences between the latter two groups. This result is compatible with a scenario where certain types of parallel change are more likely to be mediated by advergence and contact between proximate speech communities, while other developments are due purely to drift and are largely independent of intercultural contact.

10

YAMAMOTO, Risako, Qingshuang YE, Hideyuki SUGIURA, Yi ZUO, and Eisuke KITA. "Improvement of Grammatical Differential Evolution." Proceedings of The Computational Mechanics Conference 2016.29 (2016): 007. http://dx.doi.org/10.1299/jsmecmd.2016.29.007.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Дисертації з теми "Evolution Grammaticale":

1

Harper, Robin Thomas Ross Computer Science &amp Engineering Faculty of Engineering UNSW. "Enhancing grammatical evolution." Awarded by:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44843.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Grammatical Evolution (GE) is a method of utilising a general purpose evolutionary algorithm to ???evolve??? programs written in an arbitrary BNF grammar. This thesis extends GE as follows: GE as an extension of Genetic Programming (GP) A novel method of automatically extracting information from the grammar is introduced. This additional information allows the use of GP style crossover which in turn allows GE to perform identically to a strongly typed GP system as well as a non-typed (or canonical) GP system. Two test problems are presented one which is more easily solved by the GP style crossover and one which favours the tradition GE ???Ripple Crossover???. With this new crossover operator GE can now emulate GP (as well as retaining its own unique features) and can therefore now be seen as an extension of GP. Dynamically Defined Functions An extension to the BNF grammar is presented which allows the use of dynamically defined functions (DDFs). DDFs provide an alternative to the traditional approach of Automatically Defined Functions (ADFs) but have the advantage that the number of functions and their parameters do not need to be specified by the user in advance. In addition DDFs allow the architecture of individuals to change dynamically throughout the course of the run without requiring the introduction of any new form of operator. Experimental results are presented confirming the effectiveness of DDFs. Self-Selecting (or variable) crossover. A self-selecting operator is introduced which allows the system to determine, during the course of the run, which crossover operator to apply; this is tested over several problem domains and (especially where small populations are used) is shown to be effective in aiding the system to overcome local optima. Spatial Co-Evolution in Age Layered Planes (SCALP) A method of combining Hornby???s ALPS metaheuristic and a spatial co-evolution system used by Mitchell is presented; the new SCALP system is tested over three problem domains of increasing difficulty and performs extremely well in each of them.

2

Georgiou, Loukas. "Constituent grammatical evolution." Thesis, Bangor University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569460.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Evolutionary algorithms are a competent nature-inspired approach for complex computational problem solving. One recent development is Grammatical Evolution, a grammar-based evolutionary algorithm which uses genotypes of variable length binary strings and a unique genotype-to-phenotype mapping process based on a BNF grammar definition describing the output language that is able to create valid individuals of an arbitrary structure or programming language. This study surveys Grammatical Evolution, identifies its most important issues, investigates the competence of the algorithm in a series of agent-oriented benchmark problems, provides experimental results which cast doubt about its effectiveness and efficiency on problems involving the evolution of the behaviour of an agent, and presents Constituent Grammatical Evolution (CGE), a new innovative evolutionary automatic programming algorithm. CGE extends Grammatical Evolution by incorporating the concepts of constituent genes and conditional behaviour-switching. It builds from elementary and more complex building blocks a control program which dictates the behaviour of an agent and it is applicable to the class of problems where the subject of search is the behaviour of an agent in a given environment. Experimental results show that the new algorithm significantly improves Grammatical Evolution in all problems it has been benchmarked. Additionally, the investigation undertaken in this work required the development of a series of tools which are presented and described in detail. These tools provide an extendable open source and publicly available framework for experimentation in the area of evolutionary algorithms and their application in agent-oriented environments and complex systems.

3

Zhang, Andrew H. M. Eng Massachusetts Institute of Technology. "Structured Grammatical Evolution applied to program synthesis." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122995.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 27).
Grammatical Evolution (GE) is an evolutionary algorithm that is gaining popularity due to its ability to solve problems where it would be impossible to explore every solution within a realistic time. Structured Grammatical Evolution (SGE) was developed to overcome some of the shortcomings of GE, such as locality issues as well as wrapping around the genotype to complete the phenotype. In this paper, we apply SGE to program synthesis, where the computer must generate code to solve algorithmic problems. SGE was improved upon, because the current definition of SGE does not work. Given that the solution space is very large for possible codes, we aim to improve the efficiency of GE in converging to the correct solution. We present a method in which to remove cycles from a grammar for SGE, to be able to make sure that a genotype matches to a phenotype with reusing parts of the genotype, and analyze results to shed insight on future improvements.
by Andrew H. Zhang.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

4

Crochepierre, Laure. "Apprentissage automatique interactif pour les opérateurs du réseau électrique." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0112.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Dans le contexte de la transition énergétique et de l'augmentation des interconnexions entre les réseaux de transport d'électricité en Europe, les opérateurs du réseau français doivent désormais faire face à davantage de fluctuations et des dynamiques nouvelles sur le réseau. Pour garantir la sûreté de ce réseau, les opérateurs s'appuient sur des logiciels informatiques permettant de réaliser des simulations, ou de suivre l'évolution d'indicateurs créés manuellement par des experts grâce à leur connaissance du fonctionnement du réseau. Le gestionnaire de réseau de transport d'électricité français RTE (Réseau de Transport d'Electricité) s'intéresse notamment aux développements d'outils permettant d'assister les opérateurs dans leur tâche de surveillance des transits sur les lignes électriques. Les transits sont en effet des grandeurs particulièrement importantes pour maintenir le réseau dans un état de sécurité, garantissant la sûreté du matériel et des personnes. Cependant, les indicateurs utilisés ne sont pas faciles à mettre à jour du fait de l'expertise nécessaire pour les construire et les analyser. Pour répondre à la problématique énoncée, cette thèse a pour objet la construction d'indicateurs, sous la forme d'expressions symboliques, permettant d'estimer les transits sur les lignes électriques. Le problème est étudié sous l'angle de la Régression Symbolique et investigué à la fois par des approches génétiques d'Evolution Grammaticale et d'Apprentissage par Renforcement dans lesquelles la connaissance experte, explicite et implicite, est prise en compte. Les connaissances explicites sur la physique et l'expertise du domaine électrique sont représentées sous la forme d'une grammaire non-contextuelle délimitant l'espace fonctionnel à partir duquel l'expression est créée. Une première approche d'Evolution Grammaticale Interactive propose d’améliorer incrémentalement les expressions trouvées par la mise à jour d'une grammaire entre les apprentissages évolutionnaires. Les expressions obtenues sur des données réelles issues de l'historique du réseau sont validées par une évaluation de métriques d'apprentissages, complétée par une évaluation de leur interprétabilité. Dans un second temps, nous proposons une approche par renforcement pour chercher dans un espace délimité par une grammaire non-contextuelle afin de construire une expression symbolique pertinente pour des applications comportant des contraintes physiques. Cette méthode est validée sur des données de l'état de l'art de la régression symbolique, ainsi qu’un jeu de données comportant des contraintes physiques pour en évaluer l'interprétabilité. De plus, afin de tirer parti des complémentarités entre les capacités des algorithmes d'apprentissage automatique et de l'expertise des opérateurs du réseau, des algorithmes interactifs de Régression Symbolique sont proposés et intégrés dans des plateformes interactives. L'interactivité est employée à la fois pour mettre à jour la connaissance représentée sous forme grammaticale, analyser, interagir avec et commenter les solutions proposées par les différentes approches. Ces algorithmes et interfaces interactifs ont également pour but de prendre en compte de la connaissance implicite, plus difficile à formaliser, grâce à l'utilisation de mécanismes d'interactions basés sur des suggestions et des préférences de l’utilisateur
In the energy transition context and the increase in interconnections between the electricity transmission networks in Europe, the French network operators must now deal with more fluctuations and new network dynamics. To guarantee the safety of the network, operators rely on computer software that allows them to carry out simulations or to monitor the evolution of indicators created manually by experts, thanks to their knowledge of the operation of the network. The French electricity transmission network operator RTE (Réseau de Transport d'Electricité) is particularly interested in developing tools to assist operators in monitoring flows on power lines. Flows are notably important to maintain the network in a safe state, guaranteeing the safety of equipment and people. However, the indicators used are not easy to update because of the expertise required to construct and analyze them.In order to address the stated problem, this thesis aims at constructing indicators, in the form of symbolic expressions, to estimate flows on power lines. The problem is studied from the Symbolic Regression perspective and investigated using both Grammatical Evolution and Reinforcement Learning approaches in which explicit and implicit expert knowledge is taken into account. Explicit knowledge about the physics and expertise of the electrical domain is represented in the form of a Context-Free Grammar to limit the functional space from which an expression is created. A first approach of Interactive Grammatical Evolution proposes to incrementally improve found expressions by updating a grammar between evolutionary learnings. Expressions are obtained on real-world data from the network history, validated by an analysis of learning metrics and an interpretability evaluation. Secondly, we propose a reinforcement approach to search in a space delimited by a Context-Free Grammar in order to build a relevant symbolic expression to applications involving physical constraints. This method is validated on state-of-the-art Symbolic Regression benchmarks and also on a dataset with physical constraints to assess its interpretability.Furthermore, in order to take advantage of the complementarities between the capacities of machine learning algorithms and the expertise of network operators, interactive Symbolic Regression algorithms are proposed and integrated into interactive platforms. Interactivity allows updating the knowledge represented in grammatical form and analyzing, interacting with, and commenting on the solutions found by the different approaches. These algorithms and interactive interfaces also aim to take into account implicit knowledge, which is more difficult to formalize, through interaction mechanisms based on suggestions and user preferences

5

Deodhar, Sushamna Shriniwas. "Using Grammatical Evolution Decision Trees for Detecting Gene-Gene Interactions in Genetic Epidemiology." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-10302009-181439/.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

A major goal of human genetics is the discovery and validation of genetic polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic models. This etiological complexity, coupled with rapid advances in genotyping technology present enormous theoretical and practical concerns for statistical and computational analysis. Specifically, the challenge presented by epistasis, or gene-gene interactions, has sparked the development of a multitude of statistical techniques over the years. Subsequently, pattern matching and machine learning approaches have been explored to overcome the limitations of traditional computational methods. Grammatical Evolution Neural Networks (GENN) uses grammatical evolution to optimize neural network architectures and better detect and analyze gene-gene interactions. Motivated by good results shown by GENN to identify epistasis in complex datasets, we have developed a new method of Grammatical Evolution Decision Trees (GEDT). GEDT replaces the black-box approach of neural networks with the white-box approach of decision trees improving understandability and interpretability. We provide a detailed technical understanding of coupling Grammatical Evolution with Decision Tress using Backus Naur Form (BNF) grammar. Further, the GEDT system has been analyzed for power results on simulated datasets. Finally, we show the results of using GEDT on two different epistatis models and discuss the direction it would take in the future.

6

Neupane, Aadesh. "Emergence of Collective Behaviors in Hub-Based Colonies using Grammatical Evolution and Behavior Trees." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/8827.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Animals such as bees, ants, birds, fish, and others are able to efficiently perform complex coordinated tasks like foraging, nest-selection, flocking and escaping predators without centralized control or coordination. These complex collective behaviors are the result of emergence. Conventionally, mimicking these collective behaviors with robots requires researchers to study actual behaviors, derive mathematical models, and implement these models as algorithms. Since the conventional approach is very time consuming and cumbersome, this thesis uses an emergence-based method for the efficient evolution of collective behaviors. Our method, Grammatical Evolution algorithm for Evolution of Swarm bEhaviors (GEESE), is based on Grammatical Evolution (GE) and extends the literature on using genetic methods to generate collective behaviors for robot swarms. GEESE uses GE to evolve a primitive set of human-provided rules, represented in a BNF grammar, into productive individual behaviors represented by Behavior Tree (BT). We show that GEESE is generic enough, given an initial grammar, that it can be applied to evolve collective behaviors for multiple problems with just a minor change in objective function. Our method is validated as follows: First, GEESE is compared with state-of-the-art genetic algorithms on the canonical Santa Fe Trail problem. Results show that GEESE outperforms the state-of-the-art by a)~providing better solutions given sufficient population size while b)~utilizing fewer evolutionary steps. Second, GEESE is used to evolve collective swarm behavior for a foraging task. Results show that the evolved foraging behavior using GEESE outperformed both hand-coded solutions as well as solutions generated by conventional Grammatical Evolution. Third, the behaviors evolved for single-source foraging task were able to perform well in a multiple-source foraging task, indicating a type of robustness. Finally, with a minor change to the objective function, the same BNF grammar used for foraging can be shown to evolve solutions to the nest-maintenance and the cooperative transport tasks.

7

Eilert, Pernilla. "Learning behaviour trees for simulated fighter pilots in airborne reconnaissance missions : A grammatical evolution approach." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156165.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Fighter pilots often find themselves in situations where they need to make quick decisions. Therefore an intelligent decision support system that suggests how the fighter pilot should act in a specific situation is vital. The aim of this project is to investigate and evaluate grammatical evolution paired with behaviour trees to develop a decision support system. This support system should control a simulated fighter pilot during an airborne reconnaissance mission. This thesis evaluates the complexity of the evolved trees and the performance, and robustness of the algorithm. Key factors were identified for a successful system: scenario, fitness function, initialisation technique and control parameters. The used techniques were decided based on increasing performance of the algorithm and decreasing complexity of the tree structures. The initialisation technique, the genetic operators and the selection functions performed well but the fitness function needed more work. Most of the experiments resulted in local maxima. A desired solution could only be found if the initial population contained an individual with a BT succeeding the mission. However, the implementation behaved as expected. More and longer simulations are needed to draw a conclusion of the performance based on robustness, when testing the evolved BT:s on different scenarios. Several methods were studied to decrease the complexity of the trees and the experiments showed a promising variation of complexity through the generations when the best fitness was fixed. A feature was added to the algorithm, to promote lower complexity when equal fitness value. The results were poor and implied that pruning would be a better fit after the simulations. Nevertheless, this thesis suggests that it is suitable to implement a decision support system based on grammatical evolution paired with behaviour trees as framework.

8

De, Silva Anthony Mihirana. "Grammar based feature generation for time-series prediction." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/10278.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This thesis proposes a systematic way for generating suitable features using context-free grammar. The notion of grammar families as a compact representation to generate a broad class of features is exploited. Implementation issues and ways to overcome them are explained in detail. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The widely and commonly employed features in practice (in previous work) for electricity and financial time-series are explored. These features are considered as a basis for comparison with the features generated and selected by the proposed framework. Other model-based approaches and naive approaches are also used as benchmarks. It is shown that the generated features can improve results, while requiring no domain-specific knowledge. The proposed method is used to determine suitable features to use in predicting previously unexplored foreign exchange client trade volume and the capabilities of the approach in automatically engineering appropriate features is highlighted. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself.

9

Mehrmand, Arash. "A Factorial Experiment on Scalability of Search-based Software Testing." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4224.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Software testing is an expensive process, which is vital in the industry. Construction of the test-data in software testing requires the major cost and knowing which method to use in order to generate the test data is very important. This paper discusses the performance of search-based algorithms (preferably genetic algorithm) versus random testing, in software test-data generation. A factorial experiment is designed so that, we have more than one factor for each experiment we make. Although many researches have been done in the area of automated software testing, this research differs from all of them due to sample programs (SUTs) which are used. Since the program generation is automatic as well, Grammatical Evolution is used to guide the program generations. They are not goal based, but generated according to the grammar we provide, with different levels of complexity. Genetic algorithm is first applied to programs, then we apply random testing. Based on the results which come up, this paper recommends one method to use for software testing, if the SUT has the same conditions as we had in this study. SUTs are not like the sample programs, provided by other studies since they are generated using a grammar.

10

Noorian, Farzad. "Risk Management using Model Predictive Control." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14282.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%.

Більше джерел

Книги з теми "Evolution Grammaticale":

1

O’Neill, Michael, and Conor Ryan. Grammatical Evolution. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Ryan, Conor, Michael O'Neill, and JJ Collins, eds. Handbook of Grammatical Evolution. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78717-6.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Dempsey, Ian, Michael O’Neill, and Anthony Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00314-1.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

O'Neill, Michael. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Boston, MA: Kluwer Academic Publishers, 2004.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

O'Neill, Michael. Grammatical evolution: Evolutionary automatic programming in an arbitrary language. Boston: Kluwer Academic Publishers, 2003.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

O'Neill, Michael. Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Boston, MA: Springer US, 2003.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Brancato, Francesco. Creazione ed evoluzione: La grammatica di un dialogo possibile. Troino (Enna): Città aperta, 2009.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

O'Neill, Michael, Conor Ryan, and JJ Collins. Handbook of Grammatical Evolution. Springer, 2018.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Handbook of Grammatical Evolution. Springer, 2019.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

10

O'Neill, Michael, Ian Dempsey, and Anthony Brabazon. Foundations in Grammatical Evolution for Dynamic Environments. Springer, 2010.

Знайти повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Більше джерел

Частини книг з теми "Evolution Grammaticale":

1

O’Neil, Michael, and Conor Ryan. "Grammatical Evolution." In Grammatical Evolution, 33–47. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_4.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

O’Neil, Michael, and Conor Ryan. "Introduction." In Grammatical Evolution, 1–4. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_1.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

O’Neil, Michael, and Conor Ryan. "Survey Of Evolutionary Automatic Programming." In Grammatical Evolution, 5–21. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_2.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

O’Neil, Michael, and Conor Ryan. "Lessons From Molecular Biology." In Grammatical Evolution, 23–32. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_3.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

O’Neil, Michael, and Conor Ryan. "Four Examples of Grammatical Evolution." In Grammatical Evolution, 49–62. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_5.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

O’Neil, Michael, and Conor Ryan. "Analysis of Grammatical Evolution." In Grammatical Evolution, 63–77. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_6.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

O’Neil, Michael, and Conor Ryan. "Crossover in Grammatical Evolution." In Grammatical Evolution, 79–98. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_7.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

O’Neil, Michael, and Conor Ryan. "Extensions & Applications." In Grammatical Evolution, 99–128. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_8.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

O’Neil, Michael, and Conor Ryan. "Conclusions & Future Work." In Grammatical Evolution, 129–32. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0447-4_9.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

10

De Silva, Anthony Mihirana, and Philip H. W. Leong. "Grammatical Evolution." In SpringerBriefs in Applied Sciences and Technology, 25–33. Singapore: Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-287-411-5_3.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Тези доповідей конференцій з теми "Evolution Grammaticale":

1

Ryan, Conor. "Grammatical evolution." In the 11th annual conference companion. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1570256.1570408.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Ryan, Conor M. "Grammatical evolution." In the 2007 GECCO conference companion. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1274000.1274126.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Timperley, Christopher, and Susan Stepney. "Reflective Grammatical Evolution." In Artificial Life 14: International Conference on the Synthesis and Simulation of Living Systems. The MIT Press, 2014. http://dx.doi.org/10.7551/978-0-262-32621-6-ch013.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Medvet, Eric. "Hierarchical grammatical evolution." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3075972.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Timperley, Christopher, and Susan Stepney. "Reflective Grammatical Evolution." In Artificial Life 14: International Conference on the Synthesis and Simulation of Living Systems. The MIT Press, 2014. http://dx.doi.org/10.1162/978-0-262-32621-6-ch013.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Ryan, Conor. "Grammatical evolution tutorial." In the 12th annual conference comp. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1830761.1830900.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Dempsey, Ian, Michael O'Neill, and Anthony Brabazon. "Meta-grammar constant creation with grammatical evolution by grammatical evolution." In the 2005 conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1068009.1068289.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Murphy, Eoin, Michael O'Neill, Edgar Galvan-Lopez, and Anthony Brabazon. "Tree-adjunct grammatical evolution." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586497.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

OGURA, MIEKO, and WILLIAM S.-Y. WANG. "EVOLUTION OF GRAMMATICAL FORMS." In Proceedings of the 8th International Conference (EVOLANG8). WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814295222_0032.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Medvet, Eric, Fabio Daolio, and Danny Tagliapietra. "Evolvability in grammatical evolution." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3071178.3071298.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.