Dissertations / Theses on the topic 'Evolutionary computation'
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Stanek, Edward Jason. "Computation of evolutionary change." [Ames, Iowa : Iowa State University], 2009.
Find full textSchmidt, Christian. "Evolutionary computation in stochastic environments." Karlsruhe Univ.-Verl. Karlsruhe, 2007. http://www.uvka.de/univerlag/volltexte/2007/231/.
Full textRohlfshagen, Philipp. "Molecular Algorithms for Evolutionary Computation." Thesis, University of Birmingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522032.
Full textPryde, Meinwen. "Evolutionary computation and experimental design." Thesis, University of South Wales, 2001. https://pure.southwales.ac.uk/en/studentthesis/evolutionary-computation-and-experimental-design(acc0a9a5-aa01-4d4a-aa4e-836ee5190a48).html.
Full textKaucic, Massimiliano. "Evolutionary computation for trading systems." Doctoral thesis, Università degli studi di Trieste, 2008. http://hdl.handle.net/10077/3093.
Full textEvolutionary computations, also called evolutionary algorithms, consist of several heuristics, which are able to solve optimization tasks by imitating some aspects of natural evolution. They may use different levels of abstraction, but they are always working on populations of possible solutions for a given task. The basic idea is that if only those individuals of a population which meet a certain selection criteria reproduce, while the remaining individuals die, the population will converge to those individuals that best meet the selection criteria. If imperfect reproduction is added the population can begin to explore the search space and will move to individuals that have an increased selection probability and that hand down this property to their descendants. These population dynamics follow the basic rule of the Darwinian evolution theory, which can be described in short as the “survival of the fittest”. Although evolutionary computations belong to a relative new research area, from a computational perspective they have already showed some promising features such as: • evolutionary methods reveal a remarkable balance between efficiency and efficacy; • evolutionary computations are well suited for parameter optimisation; • this type of algorithms allows a wide variety of extensions and constraints that cannot be provided in traditional methods; • evolutionary methods are easily combined with other optimization techniques and can also be extended to multi-objective optimization. From an economic perspective, these methods appear to be particularly well suited for a wide range of possible financial applications, in particular in this thesis I study evolutionary algorithms • for time series prediction; • to generate trading rules; • for portfolio selection. It is commonly believed that asset prices are not random, but are permeated by complex interrelations that often translate in assets mispricing and may give rise to potentially profitable opportunities. Classical financial approaches, such as dividend discount models or even capital asset pricing theories, are not able to capture these market complexities. Thus, in the last decades, researchers have employed intensive econometric and statistical modeling that examine the effects of a multitude of variables, such as price- earnings ratios, dividend yields, interest rate spreads and changes in foreign exchange rates, on a broad and variegated range of stocks at the same time. However, these models often result in complex functional forms difficult to manage or interpret and, in the worst case, are solely able to fit a given time series but are useless to predict it. Parallelly to quantitative approaches, other researchers have focused on the impact of investor psychology (in particular, herding and overreaction) and on the consequences of considering informed signals from management and analysts, such as share repurchases and analyst recommendations. These theories are guided by intuition and experience, and thus are difficult to be translated into a mathematical environment. Hence, the necessity to combine together these point of views in order to develop models that examine simultaneously hundreds of variables, including qualitative informations, and that have user friendly representations, is urged. To this end, the thesis focuses on the study of methodologies that satisfy these requirements by integrating economic insights, derived from academic and professional knowledge, and evolutionary computations. The main task of this work is to provide efficient algorithms based on the evolutionary paradigm of biological systems in order to compute optimal trading strategies for various profit objectives under economic and statistical constraints. The motivations for constructing such optimal strategies are: i) the necessity to overcome data-snooping and supervisorship bias in order to learn to predict good trading opportunities by using market and/or technical indicators as features on which to base the forecasting; ii) the feasibility of using these rules as benchmark for real trading systems; iii) the capability of ranking quantitatively various markets with respect to their profitability according to a given criterion, thus making possible portfolio allocations. More precisely, I present two algorithms that use artificial expert trading systems to predict financial time series, and a procedure to generate integrated neutral strategies for active portfolio management. The first algorithm is an automated procedure that simultaneously selects variables and detect outliers in a dynamic linear model using information criteria as objective functions and diagnostic tests as constraints for the distributional properties of errors. The novelties are the automatic implementation of econometric conditions in the model selection step, making possible a better exploration of the solution space on one hand, and the use of evolutionary computations to efficiently generate a reduction procedure from a very large number of independent variables on the other hand. In the second algorithm, the novelty is given by the definition of evolutionary learning in financial terms and its use in a multi-objective genetic algorithm in order to generate technical trading systems. The last tool is based on a trading strategy on six assets, where future movements of each variable are obtained by an evolutionary procedure that integrates various types of financial variables. The contribution is given by the introduction of a genetic algorithm to optimize trading signals parameters and the way in which different informations are represented and collected. In order to compare the contribution of this work to “classical” techniques and theories, the thesis is divided into three parts. The first part, titled Background, collects Chapters 2 and 3. Its purpose is to provide an introduction to search/optimization evolutionary techniques on one hand, and to the theories that relate the predictability in financial markets with the concept of efficiency proposed over time by scholars on the other hand. More precisely, Chapter 2 introduces the basic concepts and major areas of evolutionary computation. It presents a brief history of three major types of evolutionary algorithms, i.e. evolution strategies, evolutionary programming and genetic algorithms, and points out similarities and differences among them. Moreover it gives an overview of genetic algorithms and describes classical and genetic multi-objective optimization techniques. Chapter 3 first presents an overview of the literature on the predictability of financial time series. In particular, the extent to which the efficiency paradigm is affected by the introduction of new theories, such as behavioral finance, is described in order to justify the market forecasting methodologies developed by practitioners and academics in the last decades. Then, a description of the econometric and financial techniques that will be used in conjunction with evolutionary algorithms in the successive chapters is provided. Special attention is paid to economic implications, in order to highlight merits and shortcomings from a practitioner perspective. The second part of the thesis, titled Trading Systems, is devoted to the description of two procedures I have developed in order to generate artificial trading strategies on the basis of evolutionary algorithms, and it groups Chapters 4 and 5. In particular, chapter 4 presents a genetic algorithm for variable selection by minimizing the error in a multiple regression model. Measures of errors such as ME, RMSE, MAE, Theil’s inequality coefficient and CDC are analyzed choosing models based on AIC, BIC, ICOMP and similar criteria. Two components of penalty functions are taken in analysis- level of significance and Durbin Watson statistics. Asymptotic properties of functions are tested on several financial variables including stocks, bonds, returns, composite prices indices from the US and the EU economies. Variables with outliers that distort the efficiency and consistency of estimators are removed to solve masking and smearing problems that they may cause in estimations. Two examples complete the chapter. In both cases, models are designed to produce short-term forecasts for the excess returns of the MSCI Europe Energy sector on the MSCI Europe index and a recursive estimation- window is used to shed light on their predictability performances. In the first application the data-set is obtained by a reduction procedure from a very large number of leading macro indicators and financial variables stacked at various lags, while in the second the complete set of 1-month lagged variables is considered. Results show a promising capability to predict excess sector returns through the selection, using the proposed methodology, of most valuable predictors. In Chapter 5 the paradigm of evolutionary learning is defined and applied in the context of technical trading rules for stock timing. A new genetic algorithm is developed by integrating statistical learning methods and bootstrap to a multi-objective non dominated sorting algorithm with variable string length, making possible to evaluate statistical and economic criteria at the same time. Subsequently, the chapter discusses a practical case, represented by a simple trading strategy where total funds are invested in either the S&P 500 Composite Index or in 3-month Treasury Bills. In this application, the most informative technical indicators are selected from a set of almost 5000 signals by the algorithm. Successively, these signals are combined into a unique trading signal by a learning method. I test the expert weighting solution obtained by the plurality voting committee, the Bayesian model averaging and Boosting procedures with data from the the S&P 500 Composite Index, in three market phases, up-trend, down- trend and sideways-movements, covering the period 2000–2006. In the third part, titled Portfolio Selection, I explain how portfolio optimization models may be constructed on the basis of evolutionary algorithms and on the signals produced by artificial trading systems. First, market neutral strategies from an economic point of view are introduced, highlighting their risks and benefits and focusing on their quantitative formulation. Then, a description of the GA-Integrated Neutral tool, a MATLAB set of functions based on genetic algorithms for active portfolio management, is given. The algorithm specializes in the parameter optimization of trading signals for an integrated market neutral strategy. The chapter concludes showing an application of the tool as a support to decisions in the Absolute Return Interest Rate Strategies sub-fund of Generali Investments.
Gli “algoritmi evolutivi”, noti anche come “evolutionary computations” comprendono varie tecniche di ottimizzazione per la risoluzione di problemi, mediante alcuni aspetti suggeriti dall’evoluzione naturale. Tali metodologie sono accomunate dal fatto che non considerano un’unica soluzione alla volta, bens`ı trattano intere popolazioni di possibili soluzioni per un dato problema. L’idea sottostante `e che, se un algoritmo fa evolvere solamente gli individui di una data popolazione che soddisfano a un certo criterio di selezione, e lascia morire i restanti, la popolazione converger`a agli individui che meglio soddisfano il criterio di selezione. Con una selezione non ottimale, cio`e una che ammette pure soluzioni sub-ottimali, la popolazione rappresenter` a meglio l’intero spazio di ricerca e sar`a in grado di individuare in modo pi`u consistente gli individui migliori da far evolvere. Queste dinamiche interne alle popolazioni seguono i principi Darwiniani dell’evoluzione, che si possono sinteticamente riassumere nella dicitura “la sopravvivenza del più adatto”. Sebbene gli algoritmi evolutivi siano un’area di ricerca relativamente nuova, dal punto di vista computazionale hanno dimostrato alcune caratteristiche interessanti fra cui le seguenti: • permettono un notevole equilibrio tra efficienza ed efficacia; • sono particolarmente indicati per la configurazione dei parametri in problemi di ottimizzazione; • consentono una flessibilit`a nella definizione matematica dei problemi e dei vincoli che non si trova nei metodi tradizionali; • possono facilmente essere integrati con altre tecniche di ottimizzazione ed essere essere modificati per risolvere problemi multi-obiettivo. Dal un punto di vista economico, l’applicazione di queste procedure pu`o risultare utile specialmente in campo finanziario. In particolare, nella mia tesi ho studiato degli algoritmi evolutivi per • la previsione di serie storiche finanziarie; • la costruzione di regole di trading; • la selezione di portafogli. Da un punto di vista pi`u ampio, lo scopo di questa ricerca `e dunque l’analisi dell’evoluzione e della complessit`a dei mercati finanziari. In tal senso, dal momento che i prezzi non seguono andamenti puramente casuali, ma sono governati da un insieme molto articolato di eventi correlati, i modelli e le teorie classiche, come i dividend discount model e le varie capital asset pricing theories, non sono pi`u sufficienti per determinare potenziali opportunit`a di profitto. A tal fine, negli ultimi decenni, alcuni ricercatori hanno sviluppato una vasta gamma di modelli econometrici e statistici in grado di esaminare contemporaneamente le relazioni e gli effetti di centinaia di variabili, come ad esempio, price-earnings ratios, dividendi, differenziali fra tassi di interesse e variazioni dei tassi di cambio, per una vasta gamma di assets. Comunque, questo approccio, che fa largo impiego di strumenti di calcolo, spesso porta a dei modelli troppo complicati per essere gestiti o interpretati, e, nel peggiore dei casi, pur essendo ottimi per descrivere situazioni passate, risultano inutili per fare previsioni. Parallelamente a questi approcci quantitativi, si `e manifestato un grande interesse sulla psicologia degli investitori e sulle conseguenze derivanti dalle opinioni di esperti e analisti nelle dinamiche del mercato. Questi studi sono difficilmente traducibili in modelli matematici e si basano principalmente sull’intuizione e sull’esperienza. Da qui la necessit` a di combinare insieme questi due punti di vista, al fine di sviluppare modelli che siano in grado da una parte di trattare contemporaneamente un elevato numero di variabili in modo efficiente e, dall’altra, di incorporare informazioni e opinioni qualitative. La tesi affronta queste tematiche integrando le conoscenze economiche, sia accademiche che professionali, con gli algoritmi evolutivi. Pi`u pecisamente, il principale obiettivo di questo lavoro `e lo sviluppo di algoritmi efficienti basati sul paradigma dell’evoluzione dei sistemi biologici al fine di determinare strategie di trading ottimali in termini di profitto e di vincoli economici e statistici. Le ragioni che motivano lo studio di tali strategie ottimali sono: i) la necessit`a di risolvere i problemi di data-snooping e supervivorship bias al fine di ottenere regole di investimento vantaggiose utilizzando indicatori di mercato e/o tecnici per la previsione; ii) la possibilità di impiegare queste regole come benchmark per sistemi di trading reali; iii) la capacit`a di individuare gli asset pi`u vantaggiosi in termini di profitto, o di altri criteri, rendendo possibile una migliore allocazione di risorse nei portafogli. In particolare, nella tesi descrivo due algoritmi che impiegano sistemi di trading artificiali per predire serie storiche finanziarie e una procedura di calcolo per strategie integrate neutral market per la gestione attiva di portafogli. Il primo algoritmo `e una procedura automatica che seleziona le variabili e simultaneamente determina gli outlier in un modello dinamico lineare utilizzando criteri informazionali come funzioni obiettivo e test diagnostici come vincoli per le caratteristiche delle distribuzioni degli errori. Le novit`a del metodo sono da una parte l’implementazione automatica di condizioni econometriche nella fase di selezione, consentendo una migliore analisi dello EVOLUTIONARY COMPUTATIONS FOR TRADING SYSTEMS 3 spazio delle soluzioni, e dall’altra parte, l’introduzione di una procedura di riduzione evolutiva capace di riconoscere in modo efficiente le variabili pi`u informative. Nel secondo algoritmo, le novità sono costituite dalla definizione dell’apprendimento evolutivo in termini finanziari e dall’applicazione di un algoritmo genetico multi-obiettivo per la costruzione di sistemi di trading basati su indicatori tecnici. L’ultimo metodo proposto si basa su una strategia di trading su sei assets, in cui le dinamiche future di ciascuna variabile sono ottenute impiegando una procedura evolutiva che integra diverse tipologie di variabili finanziarie. Il contributo è dato dall’impiego di un algoritmo genetico per ottimizzare i parametri negli indicatori tecnici e dal modo in cui le differenti informazioni sono presentate e collegate. La tesi `e organizzata in tre parti. La prima parte, intitolata Background, comprende i Capitoli 2 e 3, ed è intesa a fornire un’introduzione alle tecniche di ricerca/ottimizzazione su base evolutiva da una parte, e alle teorie che si occupano di efficienza e prevedibilit`a dei mercati finanziari dall’altra. Più precisamente, il Capitolo 2 introduce i concetti base e i maggiori campi di studio della computazione evolutiva. In tal senso, si dà una breve presentazione storica di tre dei maggiori tipi di algoritmi evolutivi, ciò e le strategie evolutive, la programmazione evolutiva e gli algoritmi genetici, evidenziandone caratteri comuni e differenze. Il capitolo si chiude con una panoramica sugli algoritmi genetici e sulle tecniche classiche e genetiche di ottimizzazione multi-obiettivo. Il Capitolo 3 affronta nel dettaglio la problematica della prevedibilit`a delle serie storiche finanziarie mettendo in luce, in particolare, quanto il paradigma dell’efficienza sia influenzato dalle pi`u recenti teorie finanziarie, come ad esempio la finanza comportamentale. Lo scopo è quello di dare una giustificazione su basi teoriche per le metodologie di previsione sviluppate nella tesi. Segue una descrizione dei metodi econometrici e di analisi tecnica che nei capitoli successivi verrano impiegati assieme agli algoritmi evolutivi. Una particolare attenzione è data alle implicazioni economiche, al fine di evidenziare i loro meriti e i loro difetti da un punto di vista pratico. La seconda parte, intitolata Trading Systems, raggruppa i Capitoli 4 e 5 ed è dedicata alla descrizione di due procedure che ho sviluppato per generare sistemi di trading artificiali sulla base di algoritmi evolutivi. In particolare, il Capitolo 4 presenta un algortimo genetico per la selezione di variabili attraverso la minimizzazione dell’errore in un modello di regressione multipla. Misure di errore, quali il ME, il RMSE, il MAE, il coefficiente di Theil e il CDC sono analizzate a partire da modelli selezionati sulla scorta di criteri informazionali, come ad esempio AIC, BIC, ICOMP. A livello di vincoli diagnostici, ho considerato una funzione di penalità a due componenti e la statistica di Durbin Watson. Il programma impiega variabili finanziarie di vario tipo, come rendimenti di titoli, bond e prezzi di indici composti ottenuti dalle economie Statunitense ed Europea. Nel caso le serie storiche 4 MASSIMILIANO KAUCIC considerate presentino outliers che distorcono l’efficienza e la consistenza degli stimatori, l’algoritmo `e in grado di individuarle e rimuoverle dalla serie, risolvendo il problema di masking and smearing. Il capitolo si conclude con due applicazioni, in cui i modelli sono progettati per produrre previsioni di breve periodo per l’extra rendimento del settore MSCI Europe Energy sull’indice MSCI Europe e una procedura di tipo recursive estimation-window è utilizzata per evidenziarne le performance previsionali. Nel primo esempio, l’insieme dei dati `e ottenuto estraendo le variabili di interesse da un considerevole numero di indicatori di tipo macro e da variabili finanziarie ritardate rispetto alla variabile dipendente. Nel secondo esempio ho invece considerato l’intero insieme di variabili ritardate di 1 mese. I risultati mostrano una notevole capacità previsiva per l’extra rendimento, individuando gli indicatori maggiormente informativi. Nel Capitolo 5, il concetto di apprendimento evolutivo viene definito ed applicato alla costruzione di regole di trading su indicatori tecnici per lo stock timing. In tal senso, ho sviluppato un algoritmo che integra metodi di apprendimento statistico e di boostrap con un particolare algoritmo multi-obiettivo. La procedura derivante è in grado di valutare contemporaneamente criteri economici e statistici. Per descrivere il suo funzionamento, ho considerato un semplice esempio di trading in cui tutto il capitale è investito in un indice (che nel caso trattato è l’indice S&P 500 Composite) o in un titolo a basso rischio (nell’esempio, i Treasury Bills a 3 mesi). Il segnale finale di trading `e il risultato della selezione degli indicatori tecnici pi`u informativi a partire da un insieme di circa 5000 indicatori e la loro conseguente integrazione mediante un metodo di apprendimento (il plurality voting committee, il bayesian model averaging o il Boosting). L’analisi è stata condotta sull’intervallo temporale dal 2000 al 2006, suddiviso in tre sottoperiodi: il primo rappresenta l’indice in una fase al rialzo, il secondo in una fase al ribasso e il terzo sottoperiodo considera il mercato in una fase di trend non chiara. Nella terza parte, intitolata Portfolio Selection, spiego come si possano costruire modelli di ottimizzazione di portafogli sfruttando le tecniche della computazione evolutiva e i segnali prodotti da sistemi di trading artificiali. A tal fine, dapprima descrivo il significato economico delle strategie neutral market, evidenziandone rischi e benefici, successivamente introduco il GAIntegrated Neutral tool, un insieme di funzioni MATLAB che ho scritto per la gestione attiva di portafogli impiegando algoritmi evolutivi. La procedura calcola la configurazone ottimale dei segnali di trading per una strategia integrata neutral market. Il capitolo si conclude mostrando un’applicazione del tool come supporto alle decisioni nel fondo Absolute Return Interest Rate Strategies di Generali Investments.
XXI Ciclo
1979
Nguyen, Duy Huu Manh. "Analysing electricity markets with evolutionary computation." University of Western Australia. School of Electrical, Electronic and Computer Engineering, 2002. http://theses.library.uwa.edu.au/adt-WU2003.0018.
Full textSaks, Philip. "Evolutionary Computation in Financial Decision Making." Thesis, University of Essex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495562.
Full textRostami, Shahin. "Preference focussed many-objective evolutionary computation." Thesis, Manchester Metropolitan University, 2014. http://e-space.mmu.ac.uk/347083/.
Full textBashir, Hassan Abdullahi. "Global optimization with hybrid evolutionary computation." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/global-optimization-with-hybrid-evolutionary-computation(0392a891-dfae-4063-baf1-992cd0dc7df2).html.
Full textKramer, Oliver. "Self-adaptive heuristics for evolutionary computation." Berlin Heidelberg Springer, 2008. http://d-nb.info/991461002/34.
Full textHolly, Matthew James. "An evolutionary computation framework for microarchitecture design /." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=78379.
Full textThis study presents a software framework for designing microarchitecture. As a case study, a genetic algorithm is used to automatically synthesize two-level indirect branch predictors. The evolved designs routinely deliver enhanced performance as compared to the equivalent, highly optimized structures created by hand. The primary drawback associated with this method is the excessive design complexity induced by unconstrained evolution. Several methods of incorporating simplicity of design into the evolutionary process are investigated. A unified fitness metric, combining both simplicity and performance, leads to the evolution of simple and effective designs.
AMARAL, JOSE FRANCO MACHADO DO. "SYNTHESIS OF FUZZY SYSTEMS THROUGH EVOLUTIONARY COMPUTATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3550@1.
Full textSíntese de Sistemas Fuzzy por Computação Evolucionária propõe uma metodologia de projeto para o desenvolvimento de sistemas fuzzy fundamentada em técnicas de computação evolucionária. Esta metodologia contempla as etapas de concepção do sistema fuzzy e a implementação em hardware do circuito eletrônico que o representa. A concepção do sistema é realizada num ambiente de projeto no qual sua base de conhecimento - composta da base de regras e demais parâmetros característicos - é evoluída, por intermédio de simulação, através do emprego de um novo algoritmo de três estágios que utiliza Algoritmos Genéticos. Esta estratégia enfatiza a interpretabilidade e torna a criação do sistema fuzzy mais simples e eficiente para o projetista, especialmente quando comparada com o tradicional ajuste por tentativa e erro. A implementação em hardware do circuito é realizada em plataforma de desenvolvimento baseada em Eletrônica Evolucionária. Um conjunto de circuitos, denominados de blocos funcionais, foi desenvolvido e evoluído com sucesso para viabilizar a construção da estrutura final do sistema fuzzy.
Synthesis of Fuzzy Systems through Evolutionary Computation proposes a methodology for the design of fuzzy systems based on evolutionary computation techniques. A three-stage evolutionary algorithm that uses Genetic Algorithms (GAs) evolves the knowledge base of a fuzzy system - rule base and parameters. The evolutionary aspect makes the design simpler and more efficient, especially when compared with traditional trial and error methods. The method emphasizes interpretability so that the resulting strategy is clearly stated. An Evolvable Hardware (EHW) platform for the synthesis of analog electronic circuits is proposed. This platform, which can be used for the implementation of the designed fuzzy system, is based on a Field Programmable Analog Array (FPAA). A set of evolved circuits called functional blocks allows the implementation of the fuzzy system.
Adamu, Adamu. "Evolutionary computation for high frequency trading systems." Thesis, University of Essex, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537917.
Full textDukkipati, Ambedkar. "ACE-Model: A Conceptual Evolutionary Model For Evolutionary Computation And Artificial Life." Thesis, Indian Institute of Science, 2002. https://etd.iisc.ac.in/handle/2005/3920.
Full textDukkipati, Ambedkar. "ACE-Model: A Conceptual Evolutionary Model For Evolutionary Computation And Artificial Life." Thesis, Indian Institute of Science, 2002. http://hdl.handle.net/2005/47.
Full textSkolicki, Zbigniew Maciej. "An analysis of island models in evolutionary computation." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2954.
Full textTitle from PDF t.p. (viewed Jan. 22, 2008). Thesis director: Kenneth A. De Jong. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Vita: p. 422. Includes bibliographical references (p. 413-421). Also available in print.
ONG, Bun Theang. "Studies on Automatic Termination Criteria for Evolutionary Computation." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/157482.
Full textDahlberg, Leslie. "Evolutionary Computation in Continuous Optimization and Machine Learning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35674.
Full textSchmidt, Christian [Verfasser]. "Evolutionary computation in stochastic environments / von Christian Schmidt." Karlsruhe : Univ.-Verl. Karlsruhe, 2007. http://d-nb.info/984871349/34.
Full textXue, Feng, and 薛峰. "Evolutionary computation of geodesic paths in CAD/CAM." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226978.
Full textGoh, Cindy S. F. "An evolutionary computation enabled automatic circuit synthesis tool." Thesis, University of Glasgow, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443434.
Full textCreaser, Paul. "Application of evolutionary computation techniques to missile guidance." Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
Full textJia, Guanbo. "Community detection in complex networks using evolutionary computation." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7483/.
Full textXue, Feng. "Evolutionary computation of geodesic paths in CAD/CAM /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23457429.
Full textWhitacre, James M. Chemical Sciences & Engineering Faculty of Engineering UNSW. "Adaptation and self-organization in evolutionary algorithms." Awarded by:University of New South Wales. Chemical Sciences & Engineering, 2007. http://handle.unsw.edu.au/1959.4/40444.
Full textMomm, Henrique Garcia. "Evolutionary computation for information extraction from remotely sensed imagery /." Full text available from ProQuest UM Digital Dissertations, 2008. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1850452231&SrchMode=1&sid=2&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1279562359&clientId=22256.
Full textTypescript. Vita. "May 2008." Major professor: Greg Easson Includes bibliographical references (leaves145-154). Also available online via ProQuest to authorized users.
Affenzeller, Michael. "Population genetics and evolutionary computation : theoretical and practical aspects /." Linz : Trauner, 2005. http://www.gbv.de/dms/ilmenau/toc/490631479affen.PDF.
Full textHastings, Erin. "AUTOMATIC GRAPHICS AND GAME CONTENT GENERATION THROUGH EVOLUTIONARY COMPUTATION." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2643.
Full textPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science PhD
Sharifi, Soroosh. "Application of evolutionary computation to open channel flow modelling." Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/478/.
Full textRastegari, Samaneh. "Intelligent network intrusion detection using an evolutionary computation approach." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2015. https://ro.ecu.edu.au/theses/1760.
Full textOliveira, Pedro paulo Balbi de. "An empirical exploration of computations with a cellular-automata-based artificial life." Thesis, University of Sussex, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.240429.
Full textWilkerson, Joshua Lee. "Co-evolutionary automated software correction: a proof of concept." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2008. http://scholarsmine.mst.edu/thesis/pdf/Wilkerson_09007dcc80642bb4.pdf.
Full textVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed June 18, 2009) Includes bibliographical references (p. 62-64).
Bush, Brian O. "Development of a fuzzy system design strategy using evolutionary computation." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178656308.
Full textGaribay, Ivan. "THE PROTEOMICS APPROACH TO EVOLUTIONARY COMPUTATION: AN ANALYSIS OF PR." Doctoral diss., University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3822.
Full textPh.D.
School of Computer Science
Engineering and Computer Science
Computer Science
Orero, Shadrack Otieno. "Power systems generation scheduling and optimisation using evolutionary computation techniques." Thesis, Brunel University, 1996. http://bura.brunel.ac.uk/handle/2438/4869.
Full textShackelford, Mark. "Collaborative evolutionary computation for large, multi-project programme resource scheduling." Thesis, University of Reading, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.402810.
Full textALMEIDA, LUCIANA FALETTI. "THE OPTIMIZATION OF PETROLEUM FIELD EXPLORATION ALTERNATIVES USING EVOLUTIONARY COMPUTATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2003. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3522@1.
Full textEsta dissertação investiga um sistema baseado em algoritmos genéticos e algoritmos culturais, aplicado ao processo de desenvolvimento de um campo de petróleo. O desenvolvimento de um campo de petróleo consiste, neste caso, da disposição de poços num reservatório petrolífero, já conhecido e delimitado, que permita maximizar o Valor Presente Líquido. Uma disposição de poços define a quantidade e posição de poços produtores e injetores e do tipo de poço (horizontalou vertical) a serem empregados no processo de exploração. O objetivo do trabalho é avaliar o desempenho de Algoritmos Genéticos e Algoritmos Culturais como métodos de apoio à decisão na otimização de alternativas de produção em reservatórios petrolíferos. Determinar a localização de novos poços de petróleo em um reservatório é um problema complexo que depende de propriedades do reservatório e critérios econômicos, entre outros fatores. Para que um processo de otimização possa ser aplicado nesse problema, é necessário definir uma função objetivo a ser minimizada ou maximizada pelo processo. No problema em questão, a função objetivo a ser maximizada é o Valor Presente Líquido (VPL). Para se estabelecer o VPL, subtrai-se os gastos com a exploração do valor correspondente ao volume de petróleo estimado da reserva. Devido à complexidade do perfil de produção de petróleo, exige-se a utilização de simuladores de reservatório para esta estimativa. Deste modo, um simulador de reservatórios é parte integrante da função de avaliação. O trabalho de pesquisa foi desenvolvido em quatro etapas: um estudo sobre a área de exploração de petróleo; um estudo dos modelos da inteligência computacional empregados nesta área; a definição e implementação de um modelo genético e cultural para o desenvolvimento de campo petrolífero e o estudo de caso. O estudo sobre a área de exploração de campo de petróleo envolveu a teoria necessária para a construção da função objetivo. No estudo sobre as técnicas de inteligência computacional definiu-se os conceitos principais sobre Algoritmo Genético e Algoritmo Cultural empregados nesta dissertação. A modelagem de um Algoritmo Genético e Cultural constitui no emprego dos mesmos, para que dado um reservatório petrolífero, o sistema tenha condições de reconhecê-lo e desenvolvê-lo, ou seja, encontrar a configuração (quantidade, localização e tipo de poços) que atinja um maior Valor Presente Líquido. Os resultados obtidos neste trabalho indicam a viabilidade da utilização de Algoritmos Genéticos e Algoritmos Culturais no desenvolvimento de campos de petróleo.
This dissertation investigates a system based in genetic algorithms and cultural algorithms, applied to the development process of a petroleum field. The development of a petroleum field consists in the placement of wells in an already known and delimited petroleum reservoir, which allows maximizing the Net Present Value. A placement of wells defines the quantity and position of the producing wells, the injecting wells, and the wells type (horizontal or vertical) to be used in the exploration process. The objective of this work is to evaluate the performance of Genetic Algorithms and Cultural Algorithms as decision support methods on the optimization of production alternatives in petroleum reservoirs. Determining the new petroleum wells location in a reservoir is a complex problem that depends on the properties of the reservoir and on economic criteria, among other factors. In order to an optimization process to be applied to this problem, it s necessary to define a target function to be minimized or maximized by the process. In the given problem, the target function to be maximized is the Net Present Value (NPV). In order to establish the NPV, the exploration cost correspondent to the estimated reservoir petroleum volume is deducted. The complexity of the petroleum s production profile implies on the use of reservoirs simulators for this estimation. In this way, a reservoir simulator is an integrant part of the evaluation function. The research work was developed in four phases: a study about the petroleum exploration field; a study about the applied computational intelligence models in this area; the definition and implementation of a genetic and cultural model for the development of petroliferous fields and the case study. The study about the petroleum exploration field involved all the necessary theory for the building of the target function. In the study about the computational intelligence techniques, the main concepts about the Genetic Algorithms and Cultural Algorithms applied in this dissertation were defined. The modeling of Genetic and Cultural Algorithms consisted in applying them so that, given a petroleum reservoir, the system is capable of evolve and find configurations (quantity, location and wells type) that achieve greater Net Present Values. The results obtained in this work, indicate that the use of Genetic Algorithms and Cultural Algorithms in the development of petroleum fields is a promising alternative.
Collins, Zachary. "Hardware Trojans in FPGA Device IP: Solutions Through Evolutionary Computation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1554217182155068.
Full textJob, Dominic Edward. "Case-based reasoning and evolutionary computation techniques for FPGA programming." Thesis, Edinburgh Napier University, 2001. http://researchrepository.napier.ac.uk/Output/4272.
Full textMohd, Yusoh Zeratul Izzah. "Composite SaaS resource management in cloud computing using evolutionary computation." Thesis, Queensland University of Technology, 2013. https://eprints.qut.edu.au/63280/1/Zeratul_Mohd_Yusoh_Thesis.pdf.
Full textNwamba, André Chidi. "Automated offspring sizing in evolutionary algorithms." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Nwamba_09007dcc8068c83d.pdf.
Full textVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed August 10, 2009) Includes bibliographical references (p. 49-51).
Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.
Find full textLei, Chu San. "Systems organised as networks : representation and problem solving with evolutionary computation." Thesis, University of Macau, 1997. http://umaclib3.umac.mo/record=b1445381.
Full textTorbey, Elie. "Control/data flow graph synthesis using evolutionary computation and behavioral estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ37080.pdf.
Full textMbagwu, Otitochi (Otitochi E. ). "Design and implementation of evolutionary computation algorithms for volunteer compute networks." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91846.
Full textCataloged from PDF version of thesis. "May 23, 2014."
Includes bibliographical references (page 37).
We implemented a distributed evolutionary computation system titled EvoGPJ Star (EGS) and deployed the system onto Boinc, a volunteer computing network (VCN). Evolutionary computation is computationally expensive and VCN allows more cost-effective cluster computing since resources are donated. In addition, we believe that the design similarities between EGS and our chosen VCN (Boinc) would allow for easy integration of the two systems. EGS follows a centralized design pattern, with multiple engines communicating with a central coordinator and case server. The coordinator synchronizes up engines to run experiments and also stores and distributes individual solutions among engines. The engine-coordinator model creates a scalable (engines can be easily added) and robust (can continue to operate if nodes fail) system. For our experiment we chose rule-based classification. We saw the distributed EGS solutions (standard and Boinc) outperform the single-engine system. Deploying the system to Boinc revealed some design conflicts between Boinc and EGS experimentation. These conflicts stemmed from the asynchronous and asymmetric nature of VCNs.
by Otitochi Mbagwu.
M. Eng.
Torbey, Elie Carleton University Dissertation Engineering Electronics. "Control/data flow graph synthesis using evolutionary computation and behavioral estimation." Ottawa, 1999.
Find full textTurley, Marianne Cecelia. "Investigating alternative ecological theories using multiple criteria assessment with evolutionary computation /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6366.
Full textSen, Sevil. "Evolutionary computation techniques for intrusion detection in mobile ad hoc networks." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/998/.
Full textNeves, José Carlos Clemente. "Sexy Evolutionary Computation." Master's thesis, 2013. http://hdl.handle.net/10316/35698.
Full textThe impact of Natural Selection overshadowed Darwin's Sexual Selection theory. However, over the past few decades it begun to draw the attention of researchers from several di erent elds and the amount of supporting evidence of its role in evolution rapidly increased. Today, although the dynamics aren't fully understood, the importance of sexual selection in evolution is undebatable. The enthusiasm around this eld was not followed by evolutionary computation. On the one hand, canonical evolutionary computation systems were already well-established when sexual selection re-surfaced. On the other, so far, attempts to incorporate sexual selection approaches in evolutionary computation, particularly when applied to optimization problems, have encountered several di culties and no generic tools and approaches applicable to a wide variety of problems exist. This dissertation constitutes a step towards changing this situation. Based on an embracing survey of the state of the art and following a natureinspired approach, a popular evolutionary computation framework is expanded through the incorporation of Mate Choice mechanisms { enabling the application of sexual selection models to a wide variety of problems with little e ort. The approach is tested on symbolic regression benchmark problems. The analysis of such problems indicates that sexual selection is able to outperform conventional approaches in complex problem instances. Additional testing and analysis focused on understanding how sexual selection contributes to the evolution. The experimental results show that the evolved mate choice functions are able to select mating partners in meaningful ways, contributing to the evolutionary success of the descendants.
"Computation of evolutionary change." IOWA STATE UNIVERSITY, 2009. http://pqdtopen.proquest.com/#viewpdf?dispub=3352259.
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