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Reimann, Axel. "Evolutionary algorithms and optimization." Doctoral thesis, [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=969093497.
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Ciftci, Erhan. "Evolutionary Algorithms In Design." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12607983/index.pdf.
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Evolutionary Structural Optimization (ESO) is a relatively new design tool used to improve and optimise the design of structures. In this method, a few elements of an initial design domain of finite elements are iteratively removed. Such a process is carried out repeatedly until an optimum design is achieved, or until a desired given area or volume is reached.
In structural design, there is the demand for the development of design tools and methods that includes optimization. This need is the reason behind the development of methods like Evolutionary Structural Optimization (ESO). It is also this demand that this thesis seeks to satisfy. This thesis develops and examines the program named EVO, with the concept of structural optimization in the ESO process. Taking into account the stiffness and stress constraints, EVO allows a realistic and accurate approach to optimising a model in any given environment.
Finally, in verifying the ESO algorithm&rsquos and EVO program&rsquo
s usefulness to the practical aspect of design, the work presented herein applies the ESO method to case studies. They concern the optimization of 2-D frames, and the optimization of 3-D spatial frames and beams with the prepared program EVO. Comparisons of these optimised models are then made to those that exist in literature.
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Loshchilov, Ilya. "Surrogate-Assisted Evolutionary Algorithms." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00823882.
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Les Algorithmes Évolutionnaires (AEs) ont été très étudiés en raison de leur capacité à résoudre des problèmes d'optimisation complexes en utilisant des opérateurs de variation adaptés à des problèmes spécifiques. Une recherche dirigée par une population de solutions offre une bonne robustesse par rapport à un bruit modéré et la multi-modalité de la fonction optimisée, contrairement à d'autres méthodes d'optimisation classiques telles que les méthodes de quasi-Newton. La principale limitation de AEs, le grand nombre d'évaluations de la fonction objectif, pénalise toutefois l'usage des AEs pour l'optimisation de fonctions chères en temps calcul. La présente thèse se concentre sur un algorithme évolutionnaire, Covariance Matrix Adaptation Evolution Strategy (CMA-ES), connu comme un algorithme puissant pour l'optimisation continue boîte noire. Nous présentons l'état de l'art des algorithmes, dérivés de CMA-ES, pour résoudre les problèmes d'optimisation mono- et multi-objectifs dans le scénario boîte noire. Une première contribution, visant l'optimisation de fonctions coûteuses, concerne l'approximation scalaire de la fonction objectif. Le meta-modèle appris respecte l'ordre des solutions (induit par la valeur de la fonction objectif pour ces solutions) ; il est ainsi invariant par transformation monotone de la fonction objectif. L'algorithme ainsi défini, saACM-ES, intègre étroitement l'optimisation réalisée par CMA-ES et l'apprentissage statistique de meta-modèles adaptatifs ; en particulier les meta-modèles reposent sur la matrice de covariance adaptée par CMA-ES. saACM-ES préserve ainsi les deux propriété clé d'invariance de CMA-ES~: invariance i) par rapport aux transformations monotones de la fonction objectif; et ii) par rapport aux transformations orthogonales de l'espace de recherche. L'approche est étendue au cadre de l'optimisation multi-objectifs, en proposant deux types de meta-modèles (scalaires). La première repose sur la caractérisation du front de Pareto courant (utilisant une variante mixte de One Class Support Vector Machone (SVM) pour les points dominés et de Regression SVM pour les points non-dominés). La seconde repose sur l'apprentissage d'ordre des solutions (rang de Pareto) des solutions. Ces deux approches sont intégrées à CMA-ES pour l'optimisation multi-objectif (MO-CMA-ES) et nous discutons quelques aspects de l'exploitation de meta-modèles dans le contexte de l'optimisation multi-objectif. Une seconde contribution concerne la conception d'algorithmes nouveaux pour l'optimi\-sation mono-objectif, multi-objectifs et multi-modale, développés pour comprendre, explorer et élargir les frontières du domaine des algorithmes évolutionnaires et CMA-ES en particulier. Spécifiquement, l'adaptation du système de coordonnées proposée par CMA-ES est couplée à une méthode adaptative de descente coordonnée par coordonnée. Une stratégie adaptative de redémarrage de CMA-ES est proposée pour l'optimisation multi-modale. Enfin, des stratégies de sélection adaptées aux cas de l'optimisation multi-objectifs et remédiant aux difficultés rencontrées par MO-CMA-ES sont proposées.
4
Maitre, Ogier. "GPGPU for Evolutionary Algorithms." Strasbourg, 2011. http://www.theses.fr/2011STRA6240.
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Les algorithmes évolutionnaires permettent de trouver des réponses satisfaisantes, mais non-nécessairement optimales à des problèmes complexes. La puissance de ces algorithmes est directement corrélée à la puissance de calcul disponible pour leur exécution. En effet, ces algorithmes réalisent une exploration en parallèle de l’espace de recherche, par le biais de l’évolution d’une population d’individus plus ou moins adaptés à la résolution du problème. La puissance de calcul disponible contraint la taille de la population et donc la capacité d’exploration ou d’exploitation qu’offre un algorithme. Parallèlement, nous assistons au développement des architectures de processeurs multi-cœurs. Ces processeurs peuvent contenir jusqu’à plusieurs centaines de cœurs dans une puce, mais possèdent des contraintes structurelles qui imposent une adaptation des algorithmes utilisés. Parmi ces processeurs se développent depuis 2007 les processeurs de type GPGPU (pour General Purpose Graphical Processing Unit), qui sont des versions généralisées de puces de rendu 3D. Ces processeurs possèdent jusqu’à plusieurs centaines de cœurs par puce et permettent d’obtenir des accélérations de plusieurs centaines de fois, sur certaines applications. Cette thèse détaille l’utilisation de telles architectures dans le cadre des algorithmes évolutionnaires. Plusieurs variantes de ces algorithmes sont étudiées, comme la AG / SE et la GP. Des implantations sur architectures mixtes et GPGPUs seules sont étudiées, par l’application à des problèmes jouets et réelsEvolutionary algorithms can find satisfactory, but not necessarily optimal solutions to complex problems. The power of these algorithms is directly related to the available computing power. Indeed, these algorithms perform a parallel exploration of the search space, through the evolution of a population of individuals more or less suited to the problem being solved. The available computing power has a direct impact on the population size and therefore the exploration/exploitation ability of a given algorithm. On the other hand, multicore processor architectures are being developed largely nowadays. These processors can contain up to hundreds of cores in a chip, but have structural constraints that impose an adaptation of the algorithms to be ported on. Among multicore processors, GPGPU-type (for General Purpose Graphical Processing Unit) processors, which are generalized versions of 3D rendering chips, have been industrially developed since 2007. These processors have up to hundreds of cores per chip and allow to obtain speedups of several hundred times, on some applications. This thesis details the use of such architectures in the context of evolutionary algorithms. Several variants of these algorithms are studied, such as GA / ES and GP. Implementation on mixed architectures and GPGPUs only were considered, using artificial and real-world application
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Rohlfshagen, Philipp. "Molecular Algorithms for Evolutionary Computation." Thesis, University of Birmingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522032.
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Smith, James Edward. "Self adaptation in evolutionary algorithms." Thesis, University of the West of England, Bristol, 1998. http://eprints.uwe.ac.uk/11046/.
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Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select appropriate choices of operators and parameters, thus aiding the implementation of genetic algorithms. The nature of the evolving search strategies are investigated and explained in terms of the known properties of the landscapes used, and it is suggested how observations of evolving strategies on unknown landscapes may be used to categorise them, and guide further changes in other facets of the genetic algorithm. This work provides a contribution towards the study of adaptation in Evolutionary Algorithms, and towards the design of robust search algorithms for “real world” problems.
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Williams, Kenneth Peter. "Evolutionary algorithms for automatic parallelization." Thesis, University of Reading, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265665.
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Shen, Liang. "Evolutionary algorithms with mixed strategy." Thesis, Aberystwyth University, 2016. http://hdl.handle.net/2160/f08f9fe9-f4d1-48cd-aa17-3218eb2f4f35.
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During the last several decades, many kinds of population based Evolutionary Algorithms have been developed and considerable work has been devoted to computational methods which are inspired by biological evolution and natural selection, such as Evolutionary Programming and Clonal Selection Algorithm. The objective of these algorithms is not only to find suitable adjustments to the current population and hence the solution, but also to perform the process efficiently. However, a parameter setting that was optimal at the beginning of the algorithm may become unsuitable during the evolutionary process. Thus, it is preferable to automatically modify the control parameters during the runtime process. The approach required could have a bias on the distribution towards appropriate directions of the search space, thereby maintaining sufficient diversity among individuals in order to enable further ability of evolution. This thesis has offered an initial approach to developing this idea. The work starts from a clear understanding of the literature that is of direct relevance to the aforementioned motivations. The development of this approach has been built upon the basis of the fundamental and generic concepts of evolutionary algorithms. The work has exploited and benefited from a range of representative evolutionary computational mechanisms. In particular, essential issues in evolutionary algorithms such as parameter control, including the general aspects of parameter tuning and typical means for implementing parameter control have been investigated. Both the hyperheuristic algorithm and the memetic algorithm have set up a comparative work for the present development. This work has developed several novel techniques that contribute towards the advancement of evolutionary computation and optimization. One such novel approach is to construct a mixed strategy based on the concept of local fitness landscape. It exploits the concepts of fitness landscape and local fitness landscape. Both theoretical description and experimental investigation of this local fitness landscape-based mixed strategy have been provided, and systematic comparisons with alternative approaches carried out. Another contribution of this thesis is the innovative application of mixed strategy. This is facilitated by encompassing two mutation operators into the mixed strategy, which are borrowed from classical differential evolution techniques. Such an improved method has been shown to be simple and easy for implementation. The work has been utilised to deal with the problem of protein folding in bioinformatics. It is demonstrated that the proposed algorithm possesses an appropriate balance between exploration and exploitation. The use of this improved algorithm is less likely to fall into local optimal, entailing a faster and better convergence in resolving challenging realistic application problems.
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Srikanth, Veturi. "Evolutionary algorithms for currency trading." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.619749.
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Karunarathne, Lalith. "Network coding via evolutionary algorithms." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/57047/.
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Network coding (NC) is a relatively recent novel technique that generalises network operation beyond traditional store-and-forward routing, allowing intermediate nodes to combine independent data streams linearly. The rapid integration of bandwidth-hungry applications such as video conferencing and HDTV means that NC is a decisive future network technology. NC is gaining popularity since it offers significant benefits, such as throughput gain, robustness, adaptability and resilience. However, it does this at a potential complexity cost in terms of both operational complexity and set-up complexity. This is particularly true of network code construction. Most NC problems related to these complexities are classified as non deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to solve them in polynomial time. This research concentrates on the multicast scenario, particularly: (a) network code construction with optimum network and coding resources; (b) optimising network coding resources; (c) optimising network security with a cost criterion (to combat the unintentionally introduced Byzantine modification security issue). The proposed solution identifies minimal configurations for the source to deliver its multicast traffic whilst allowing intermediate nodes only to perform forwarding and coding. In the method, a preliminary process first provides unevaluated individuals to a search space that it creates using two generic algorithms (augmenting path and linear disjoint path). An initial population is then formed by randomly picking individuals in the search space. Finally, the Multi-objective Genetic algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches search the population to identify minimal configurations. Genetic operators (crossover, mutation) contribute to include optimum features (e.g. lower cost, lower coding resources) into feasible minimal configurations. A fitness assignment and individual evaluation process is performed to identify the feasible minimal configurations. Simulations performed on randomly generated acyclic networks are used to quantify the performance of MOGA and VEGA.
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Lohpetch, Dome. "Evolutionary algorithms for financial trading." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2510.
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Genetic programming (GP) is increasingly popular as a research tool for applications in finance and economics. One thread in this area is the use of GP to discover effective technical trading rules. In a seminal article, Allen & Karjalainen (1999) used GP to find rules that were profitable, but were nevertheless outperformed by the simple “buy and hold” trading strategy. Many succeeding attempts have reported similar findings. This represents a clear example of a significant open issue in the field of GP, namely, generalization in GP [78]. The issue of generalisation is that GP solutions may not be general enough, resulting in poor performance on unseen data. There are a small handful of cases in which such work has managed to find rules that outperform buyand- hold, but these have tended to be difficult to replicate. Among previous studies, work by Becker & Seshadri (2003) was the most promising one, which showed outperformance of buy-and-hold. In turn, Becker & Seshadri’s work had made several modifications to Allen & Karjalainen’s work, including the adoption of monthly rather than daily trading. This thesis provides a replicable account of Becker & Seshadri’s study, and also shows how further modifications enabled fairly reliable outperformance of buy-and-hold, including the use of a train/test/validate methodology [41] to evolve trading rules with good properties of generalization, and the use of a dynamic form of GP [109] to improve the performance of the algorithm in dynamic environments like financial markets. In addition, we investigate and compare each of daily, weekly and monthly trading; we find that outperformance of buy-and-hold can be achieved even for daily trading, but as we move from monthly to daily trading the performance of evolved rules becomes increasingly dependent on prevailing market conditions. This has clarified that robust outperformance of B&H depends on, mainly, the adoption of a relatively infrequent trading strategy (e.g. monthly), as well as a range of factors that amount to sound engineering of the GP grammar and the validation strategy. Moreover, v we also add a comprehensive study of multiobjective approaches to this investigation with assumption from that, and find that multiobjective strategies provide even more robustness in outperforming B&H, even in the context of more frequent (e.g. weekly) trading decisions. Last, inspired by a number of beneficial aspects of grammatical evolution (GE) and reports on the successful performance of various kinds of its applications, we introduce new approach for (GE) with a new suite of operators resulting in an improvement on GE search compared with standard GE. An empirical test of this new GE approach on various kind of test problems, including financial trading, is provided in this thesis as well.
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Angeline, Peter John. "Evolutionary algorithms and emergent intelligence /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487847309052203.
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Wang, Rui. "Preference-inspired co-evolutionary algorithms." Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4920/.
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The simultaneous optimisation of many objectives (say, in excess of 3), in order to obtain a full and satisfactory set of trade-off solutions to support a posteriori decision-making, remains challenging. To solve many-objective optimisation problems (MaOPs), a novel class of algorithms, namely, preference-inspired co-evolutionary algorithms (PICEAs) is proposed based on a concept of co-evolving the common population of candidate solutions with a family of decision-maker preferences. Two realisations of PICEAs, i.e., PICEA-g and PICEA-w, are studied. PICEA-g co-evolves goal vectors with candidate solutions. The algorithm is demonstrated to perform better than or competitively with four of the best-in-class MOEAs on MaOP benchmark problems. PICEA-w co-evolves weight vectors with candidate solutions. PICEA-w performs better than or competitively with other leading decomposition based algorithms on MaOPs benchmark problems. Moreover, PICEA-w eliminates the need to specify appropriate weights in advance of performing the optimisation, which leads the algorithm to be less sensitive to the problem geometries. As performance of MOEAs is often affected by the associated parameter configurations, parameter sensitivities of both the PICEAs are empirically studied, and some suggestions on the settings of parameters are provided. This research also proposes a novel and unified approach, namely, interactive PICEA-g (iPICEA-g) for a priori or progressive multi-objective optimisation and decision-making. This approach is derived from PICEA-g by co-evolving goal vectors that are exclusively generated in regions of interest to a decision-maker. iPICEA-g, to the best of the author's knowledge, is the first approach that is simultaneously able to handle multiple preferences types such as aspirations, weights or even via visually brushing, and that is also able to support multiple regions of interest. The iPICEA-g is demonstrated to be effective on different benchmark problems as well as a real-world problem --aircraft control system design problem.
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Khmeleva, Elena. "Evolutionary algorithms for scheduling operations." Thesis, Sheffield Hallam University, 2016. http://shura.shu.ac.uk/15608/.
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While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods.
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Pelikan, Martin. "Hierarchical Bayesian optimization algorithm : toward a new generation of evolutionary algorithms /." Berlin [u.a.] : Springer, 2005. http://www.loc.gov/catdir/toc/fy053/2004116659.html.
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Karahan, Ibrahim. "Preference-based Flexible Multiobjective Evolutionary Algorithms." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609578/index.pdf.
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In this study,we develop an elitist multiobjective evolutionary algorithm for approximating the Pareto-optimal frontiers of multiobjective optimization problems. The algorithm converges the true Pareto-optimal frontier while keeping the solutions in the population well-spread over the frontier. Diversity of the solutions is maintained by the territory defi
ning property of the algorithm rather than using an explicit diversity preservation mechanism. This leads to substantial computational e&
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ciency. We test the algorithm on commonly used test problems and compare its performance against well-known benchmark algorithms. In addition to approximating the entire Pareto-optimal frontier,we develop a preference incorporation mechanism to guide the search towards the decision maker&
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s regions of interest. Based on this mechanism, we implement two variants of the algorithm. The &
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rst gathers all preference information before the optimization stage to &
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nd approximations of the desired regions. The second one is an interactive algorithm that focuses on the desired region by interacting with the decision maker during the solution process. Based on tests on 2- and 3-objective problems, we observe that both algorithms converge to the preferred regions.
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Fu, Xinye. "Building Evolutionary Clustering Algorithms on Spark." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219608.
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Evolutionary clustering (EC) is a kind of clustering algorithm to handle the noise of time-evolved data. It can track the truth drift of clustering across time by considering history. EC tries to make clustering result fit both current data and historical data/model well, so each EC algorithm defines snapshot cost (SC) and temporal cost (TC) to reflect both requests. EC algorithms minimize both SC and TC by different methods, and they have different ability to deal with a different number of cluster, adding/deleting nodes, etc.Until now, there are more than 10 EC algorithms, but no survey about that. Therefore, a survey of EC is written in the thesis. The survey first introduces the application scenario of EC, the definition of EC, and the history of EC algorithms. Then two categories of EC algorithms model-level algorithms and data-level algorithms are introduced oneby-one. What’s more, each algorithm is compared with each other. Finally, performance prediction of algorithms is given. Algorithms which optimize the whole problem (i.e., optimize change parameter or don’t use change parameter to control), accept a change of cluster number perform best in theory.EC algorithm always processes large datasets and includes many iterative data-intensive computations, so they are suitable for implementing on Spark. Until now, there is no implementation of EC algorithm on Spark. Hence, four EC algorithms are implemented on Spark in the project. In the thesis, three aspects of the implementation are introduced. Firstly, algorithms which can parallelize well and have a wide application are selected to be implemented. Secondly, program design details for each algorithm have been described. Finally, implementations are verified by correctness and efficiency experiments.Evolutionär clustering (EC) är en slags klustringsalgoritm för att hantera bruset av tidutvecklad data. Det kan spåra sanningshanteringen av klustring över tiden genom att beakta historien. EC försöker göra klustringsresultatet passar både aktuell data och historisk data / modell, så varje EC-algoritm definierar ögonblicks kostnad (SC) och tidsmässig kostnad (TC) för att reflektera båda förfrågningarna. EC-algoritmer minimerar både SC och TC med olika metoder, och de har olika möjligheter att hantera ett annat antal kluster, lägga till / radera noder etc.Hittills finns det mer än 10 EC-algoritmer, men ingen undersökning om det. Därför skrivs en undersökning av EC i avhandlingen. Undersökningen introducerar först applikationsscenariot för EC, definitionen av EC och historien om EC-algoritmer. Därefter introduceras två kategorier av EC-algoritmer algoritmer på algoritmer och algoritmer på datanivå en för en. Dessutom jämförs varje algoritm med varandra. Slutligen ges resultatprediktion av algoritmer. Algoritmer som optimerar hela problemet (det vill säga optimera förändringsparametern eller inte använda ändringsparametern för kontroll), acceptera en förändring av klusternummer som bäst utför i teorin.EC-algoritmen bearbetar alltid stora dataset och innehåller många iterativa datintensiva beräkningar, så de är lämpliga för implementering på Spark. Hittills finns det ingen implementering av EG-algoritmen på Spark. Därför implementeras fyra EC-algoritmer på Spark i projektet. I avhandlingen införs tre aspekter av genomförandet. För det första är algoritmer som kan parallellisera väl och ha en bred tillämpning valda att implementeras. För det andra har programdesigndetaljer för varje algoritm beskrivits. Slutligen verifieras implementeringarna av korrekthet och effektivitetsexperiment.
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Dorn, Jason Liam. "Evolutionary Algorithms to Aid Watershed Management." NCSU, 2004. http://www.lib.ncsu.edu/theses/available/etd-12282004-235442/.
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Watershed management is a complex process involving multiple uses, diverse stakeholders, and a variety of computer-based hydrologic and hydraulic simulation models. Exploring for efficient solutions and making decisions about the best integrated management strategies to implement can be improved through the use of quantitative systems analytic techniques. In addition to identifying mathematically optimal solutions, these techniques should also be able to consider issues that may not be properly represented in the models or may be in conflict with one another. As the complexities of the system models grow, contemporary heuristic search methods, including evolutionary algorithms (EAs), are becoming increasingly common in quantitative analysis of such challenging decision-making problems. More research is needed to enhance and extend the capabilities of these newer search methods to meet the growing challenges. Further, these new systems analytic capabilities are best made accessible to practitioners through a generic computational framework that integrates the system simulation models with the suite of search techniques. Therefore, the purpose of this research is to develop new EA-based system analytic methods for addressing integrated watershed management problems and a computational framework within which their capabilities are enabled for watershed management applications. EA-based methods to generate good alternative solutions and for multiobjective optimization have been developed and tested, and their performances compare well with those of other procedures. These new methods were also demonstrated through successful applications to realistic problems in watershed management. These techniques were integrated into and implemented within a new computer-based decision support framework that supports the integration of the user?s preferred watershed models, methods to perform uncertainty and/or sensitivity analyses thereon, and multiple state-of-the-art optimization heuristic search procedures to identify good management strategies that meet the problem-specific (e.g., fiscal or environmental) objectives and constraints. The design of the software framework is described with a demonstration of its capabilities via a case study involving several scenarios of a watershed management problem.
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Weicker, Karsten. "Evolutionary algorithms and dynamic optimization problems /." Osnabrück : Der Andere Verl, 2003. http://www.gbv.de/dms/ilmenau/toc/365163716weick.PDF.
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Raj, Ashish. "Evolutionary Optimization Algorithms for Nonlinear Systems." DigitalCommons@USU, 2013. http://digitalcommons.usu.edu/etd/1520.
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Many real world problems in science and engineering can be treated as optimization problems with multiple objectives or criteria. The demand for fast and robust stochastic algorithms to cater to the optimization needs is very high. When the cost function for the problem is nonlinear and non-differentiable, direct search approaches are the methods of choice. Many such approaches use the greedy criterion, which is based on accepting the new parameter vector only if it reduces the value of the cost function. This could result in fast convergence, but also in misconvergence where it could lead the vectors to get trapped in local minima. Inherently, parallel search techniques have more exploratory power. These techniques discourage premature convergence and consequently, there are some candidate solution vectors which do not converge to the global minimum solution at any point of time. Rather, they constantly explore the whole search space for other possible solutions. In this thesis, we concentrate on benchmarking three popular algorithms: Real-valued Genetic Algorithm (RGA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). The DE algorithm is found to out-perform the other algorithms in fast convergence and in attaining low-cost function values. The DE algorithm is selected and used to build a model for forecasting auroral oval boundaries during a solar storm event. This is compared against an established model by Feldstein and Starkov. As an extended study, the ability of the DE is further put into test in another example of a nonlinear system study, by using it to study and design phase-locked loop circuits. In particular, the algorithm is used to obtain circuit parameters when frequency steps are applied at the input at particular instances.
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Korejo, Imtiaz Ali. "Adaptive mutation operators for evolutionary algorithms." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/10315.
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Evolutionary algorithms (EAs) are a class of stochastic search and optimization algorithms that are inspired by principles of natural and biological evolution. Although EAs have been found to be extremely useful in finding solutions to practically intractable problems, they suffer from issues like premature convergence, getting stuck to local optima, and poor stability. Recently, researchers have been considering adaptive EAs to address the aforementioned problems. The core of adaptive EAs is to automatically adjust genetic operators and relevant parameters in order to speed up the convergence process as well as maintaining the population diversity. In this thesis, we investigate adaptive EAs for optimization problems. We study adaptive mutation operators at both population level and gene level for genetic algorithms (GAs), which are a major sub-class of EAs, and investigate their performance based on a number of benchmark optimization problems. An enhancement to standard mutation in GAs, called directed mutation (DM), is investigated in this thesis. The idea is to obtain the statistical information about the fitness of individuals and their distribution within certain regions in the search space. This information is used to move the individuals within the search space using DM. Experimental results show that the DM scheme improves the performance of GAs on various benchmark problems. Furthermore, a multi-population with adaptive mutation approach is proposed to enhance the performance of GAs for multi-modal optimization problems. The main idea is to maintain multi-populations on different peaks to locate multiple optima for multi-modal optimization problems. For each sub-population, an adaptive mutation scheme is considered to avoid the premature convergence as well as accelerating the GA toward promising areas in the search space. Experimental results show that the proposed multi-population with adaptive mutation approach is effective in helping GAs to locate multiple optima for multi-modal optimization problems.
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Shenfield, Alex. "Grid enabled optimisation using evolutionary algorithms." Thesis, University of Sheffield, 2008. http://etheses.whiterose.ac.uk/3611/.
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Optimisation and decision support tools are vital in all areas of engineering. Many engineering design problems, from the design of a controller for aircraft stability to the development of automobile chassis, can be effectively addressed by using evolutionary algorithms to optimise computational models of the systems under consideration. Unfortunately, for non-trivial problems, capturing the dynamics of a system with high fidelity often results in a model that is very computationally expensive. However, this level of fidelity is needed for an engineer to have confidence in the final solutions produced by an optimiser. Evolutionary algorithms aggravate this problem by often requiring many thousands of candidate solutions to beevaluated, since they search a population of points, before finding a satisfactory final solution. However, evolutionary algorithms do exhibit a large degree of parallelism, making them well suited for exploiting the emerging paradigm of Grid computing in which engineers and scientists have transparent access to large amounts of compute resources 'on demand'. In this work, strategies to accelerate the process of optimisation in engineering design are investigated. One promising approach examined in this thesis is the use of computational Grids in engineering design optimisation to reduce the time needed to obtain useful results. This allows the optimisation of much higher fidelity models than was previously possible. Another promising strategy is in the computational steering of multi-objective evolutionary search methods, where decision making is closely integrated into the search process resulting in a decision maker having finer control over the algorithm than was possible before.
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Graham, Ian J. "Genetic algorithms for evolutionary product design." Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/6900.
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This thesis describes research into the development of a Computer Aided Design (CAD) tool that uses a Genetic Algorithm (GA) to generate and evolve original design concepts through human interaction. CAD technologies are firmly established in the later stages of design, and include many applications of Evolutionary Algorithms (EAs). The use of EAs as generative and search tools for conceptual design is less evident in fields other than abstract art, architecture and styling. This research gains its originality in aiming to assist designers early in the design process, by creating and evolving aesthetically interesting forms (objects). The integration of GA software with a solid modelling system has enabled the development of a prototype `Evolutionary Form Design' (EFD) system. Objects are defined using a genetic data structure and constructed from various geometric primitives and combinations of Boolean operators. The primitives interact in ways that are not easily predicted, often creating novel shapes that are unlikely to have been discovered through conventional means. Edge blending further adds to objects' complexity and visual appeal. Populations of objects are subjected to a `selective breeding' programme, directed through the user's allocation of scores, and may also be guided by simple geometric targets. These factors determine which objects are `fittest' and most likely to parent a new, hopefully improved generation of objects. The challenge has been to turn the concept into a genuinely useful tool, ensuring that desirable features are reproduced in subsequent populations. The key to achieving this is the way objects are recombined during reproduction. Work has included developing 4 novel routine for grouping the individual primitives that form objects using a Teamforming algorithm. Innovative, aesthetically interesting forms can be evolved intuitively and efficiently, providing inspiration and the initial models for original design concepts. Examples are given where the system'is used by undergraduates to generate seating designs, and by the author, to create virtual sculptures and a range of consumer product concepts.
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Chan, Kit Yan. "Experimental design techniques in evolutionary algorithms." Thesis, London South Bank University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434451.
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Nguyen, Trung Thanh. "Continuous dynamic optimisation using evolutionary algorithms." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/1296/.
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Evolutionary dynamic optimisation (EDO), or the study of applying evolutionary algorithms to dynamic optimisation problems (DOPs) is the focus of this thesis. Based on two comprehensive literature reviews on existing academic EDO research and real-world DOPs, this thesis for the first time identifies some important gaps in current academic research where some common types of problems and problem characteristics have not been covered. In an attempt to close some of these gaps, the thesis makes the following contributions: First, the thesis helps to characterise DOPs better by providing a new definition framework, two new sets of benchmark problems (for certain classes of continuous DOPs) and several new sets of performance measures (for certain classes of continuous DOPs). Second, the thesis studies continuous dynamic constrained optimisation problems (DCOPs), an important and common class of DOPs that have not been studied in EDO research. Contributions include developing novel optimisation approaches (with superior results to existing methods), analysing representative characteristics of DCOPs, identifying the strengths/weaknesses of existing methods and suggesting requirements for an algorithm to solve DCOPs effectively. Third, the thesis studies dynamic time-linkage optimisation problems (DTPs), another important and common class of DOPs that have not been well-studied in EDO research. Contributions include developing a new optimisation approach (with better results than existing methods in certain classes of DTPs), analysing the characteristics of DTPs and the strengths and weaknesses of existing EDO methods in solving certain classes of DTPs.
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Fagan, Francois. "A qualitative model of evolutionary algorithms." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/86224.
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Thesis (MSc)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Evolutionary Algorithms (EAs) are stochastic techniques, based on the idea of biological evolution, for finding near-optimal solutions to optimisation problems. Due to their generality and computational speed, they have been applied very successfully in a wide range of disciplines. However, as a consequence of their stochasticity and generality, very little has been rigorously established about their performance. Developing models for explaining and predicting algorithmic performance is, in fact, one of the most important challenges facing the field of optimisation. A qualitative version of such a model of EAs is developed in this thesis. There are two paradigms for explaining why EAs are expected to converge toward an optimum. The traditional explanation is that of Universal Darwinism, but an alternative explanation is that they are hill climbing algorithms which utilise all possible escape strategies — restarting local search, stochastic search and acceptance of non-improving solutions. The combination of the hill climbing property and the above escape strategies leads to a fast algorithm that is able to avoid premature convergence. Due to the difficulty in mathematically or empirically explaining the performance of EAs, terms such as exploitation, exploration, intensity and diversity are routinely employed for this purpose. Six prevalent views on exploitation and exploration are identified in the literature, each expressing a different facet of these notions. The coherence of these views is substantiated by their deducibility from the proposed novel definitions of exploitation and exploration. This substantiation is based on a novel hypothetical construct, namely that of a Probable Fitness Landscape (PFL), which both unifies and clarifies the surrounding terminology and our understanding of the performance of EAs. The PFL is developed into a qualitative model of EAs by extending it to the notion of an Ideal Probability Distribution (IPD). This notion, along with the criteria of diversity and computational speed, forms a method for judging the performance of EA operators. It is used to explain why the principal operators of EAs, namely mutation and selection, are effective. There are three main types of EAs, namely Genetic Algorithms (GAs), Evolution Strategies and Evolutionary Programming, each of which employ their own unique operators. Important facets of the crossover operator (which is particular to GAs) are identified, such as: opposite step vectors, genetic drift and ellipsoidal parent-centred probability distributions with variance proportional to the distance between parents. The shape of the crossover probability distribution motivates a comparison with a novel continuous approximation of mutation, which reveals very similar underlying distributions, although for crossover the distribution is adaptive whereas for mutation it is fixed. The PFL and IPD are used to analyse the crossover operator, the results of which are contrasted with the traditional explanations of the Schema Theorem and Building Block Hypothesis as well as the Evolutionary Progress Principle and Genetic Repair Hypothesis. It emerges that the facetwise nature of the PFL extracts more sound conclusions than the other explanations which, falsely, attempt to prove GAs to be superior.
AFRIKAANSE OPSOMMING: Evolusionere Algoritmes (EAs) is stogastiese tegnieke vir die bepaling van naby-optimale oplossings vir optimeringsprobleme wat gebaseer is op die beginsel van biologiese evolusie. As gevolg van hul algemene toepasbaarheid en hoe berekeningspoed, is hierdie algoritmes al met groot sukses in ’n wye verskeidenheid dissiplines toegepas. Die stogastiese aard en algemene toepasbaarheid van hierdie klas van algoritmes het egter tot gevolg dat baie min al oor hul werkverrigting formeel bewys is. Die ontwikkeling van modelle waarmee die doeltreffendheid van algoritmes verklaar en voorspel kan word, is trouens een van die grootste uitdagings in die studieveld van optimering. ’n Kwalitatiewe weergawe van so ’n model word in hierdie verhandeling vir EAs daargestel. Daar bestaan twee paradigmas vir die verklaring van waarom daar van EAs verwag word om na ’n optimum te konvergeer. Die tradisionele verklaring geskied aan die hand van Universele Darwinisme, maar ’n alternatiewe verklaring is dat hierdie algoritmes bergtop-soekend is en van alle moontlike ontsnapstrategiee gebruik maak — lokale soekstrategiee, stogastiese soekstrategiee en die aanvaarding van minderwaardige oplossings. Die kombinasie van die bergtop-soekende eienskap en die insluiting van die bogenoemde ontsnapstrategiee gee aanleiding tot vinnige algoritmes wat daartoe in staat is om voortydige konvergensie te vermy. Omdat dit moeilik is om die werkverrigting van EAs wiskundig of empiries te verklaar, word terminologie soos uitbuiting, verkenning, intensiteit en diversiteit roetinegewys vir hierdie doel ingespan. Ses heersende menings in die literatuur oor uitbuiting en verkenning word ge¨ıdentifiseer wat elkeen ’n ander faset van hierdie begrippe uitlig. Die samehang van hierdie menings word deur hul afleibaarheid uit nuwe definisies van uitbuiting en verkenning gedemonstreer. Hierdie demonstrasie is gebaseer op ’n nuwe hipotetiese konstruk, naamlik die van ’n Waarskynlike Fiksheidslandskap (WFL), wat beide die omliggende terminologie¨e en ons begrip van die werking van EAs enersyds verenig en andersyds verduidelik. Die begrip van ’n WFL word tot ’n kwantitatiewe model vir EAs ontwikkel deur dit tot die konstruk van ’n Ideale Waarskynlikheidsverdeling (IWV) uit te brei. Hierdie konsep word saam met die kriteria van diversiteit en berekeningspoed gebruik om ’n metode te ontwikkel waarmee die werkverrigting van EAs beoordeel kan word. Die IWV word gebruik om te verklaar waarom die hoofoperatore van EAs, naamlik mutasie en seleksie, doeltreffend is. Daar is drie tipes van EAs, naamlik Genetiese Algoritmes (GAs), Evolusionere Strategiee en Evolusionere Programmering, wat elk hul eie, unieke operatore bevat. Belangrike fasette van die oorgangsoperator (wat eie is aan GAs) word uitgelig, soos regoorstaande trapvektore, genetiese neiging en ellipsoıdale ouer-gesentreerde waarskynlikheidsverdelings met variansies wat eweredig is aan die afstand tussen ouers. Die vorm van die oorgangs-waarskynlikheidsverdeling gee aanleiding tot ’n vergelyking tussen die begrip van oorgang en ’n nuwe, kontinue benadering van mutasie. Daar word gevind dat die onderliggende verdelings baie soortgelyk is, alhoewel die oorgangsverdeling aanpasbaar is, terwyl die verdeling vir mutasie vas is. Die WFL en IWV word gebruik om die oorgangsoperator te analiseer en die resultate van hierdie analise word teenoor die tradisionele verklarings van die Skemastelling en Boublok-hipotese sowel as die Evolusionere Vooruitgangsbeginsel en die Genetiese Herstel-hipotese gekontrasteer. Dit blyk dat meer grondige gevolgtrekkings gemaak kan word uit die fasetgewyse aard van die WFL as uit ander verklarings wat valslik poog om die meer doeltreffende werkverrigting van GAs te demonstreer. Die gebruik van faset-gewyse en kwalitatiewe modelle word geregverdig deur hul sukses in terme van die verklaring van EA werkverrigting. Die argument word gemaak dat die beste rigting vir voortgesette navorsing oor EAs is om weg te bly van vergelykende studies en die afleiding van sogenaamde vergelykings van beweging, maar om eerder die ontwikkeling van wetenskaplikgefundeerde, faset-gewyse modelle vir algoritmiese werkverrigting na te streef.
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Cruz, Alfredo. "Evolutionary Algorithms for VLSI Test Automation." NSUWorks, 2002. http://nsuworks.nova.edu/gscis_etd/472.
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The generation of binary test patterns for VLSI devices belongs to the class of NP complete problems. As the complexity of VLSI circuits increases, the time to generate test vectors becomes computationally expensive. This dissertation focuses on an evolutionary algorithm (EA) approach for the generation of effective test vectors for single and multiple fault detection in VLSI circuits. EAs provide significant speedup while retaining good quality solutions through heuristic procedures. Although not guaranteed to find optimal solution, EAs are able to find very good solutions for a wide range of problems. Three basic steps are performed during the generation of the test vectors: selection, crossover, and mutation. In the selection step, a bias roulette-wheel and the steady state are used as the reproduction operators. The new candidate test vectors with the highest fitness function replace the old ones, once crossover and mutation occur. Using this scheme, population members steadily improve their fitness level with each generation. A new mutation operator is introduced that increases the Hamming distance among the candidate solutions for shrinkage faults in Programmable Logic Arrays (PLAs). The genetic operators (selection, crossover, and mutation) are applied to the CNF-satisfiability problem for the generation of test vectors for growth faults in PLAs. The CNF constraints satisfaction problem has several advantages over other approaches used for PLA testing. The method proposed eliminates the possibility of intersecting a redundant growth term with a valid candidate test vector. The genetic operator, unlike previous operators used in PLA test generation, does not use lookups or backtracking. That is, the CNF technique eliminates operations (such as backtracking and sharp (#) operation) that can become intractable with increasing PLA size. The proposed evolutionary algorithms based solution aims to address the shortcoming of the existing methods for VLSI testing. Deterministic procedures were used to allow the identification of untestable faults and to improve the fault coverage. This hybrid deterministic/genetic test generator helps improve fault effectiveness and reduce CPU time run. Experimental results have confirmed that the number of untestable faults identified contributed to test generation effectiveness .
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Ansell, D. W. "Antenna performance optimisation using evolutionary algorithms." Thesis, Cranfield University, 2010. http://dspace.lib.cranfield.ac.uk/handle/1826/4661.
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This thesis investigates the novel idea of using evolutionary algorithms to optimise control and design aspects of active array antenna systems. Active arrays differ from most mechanically scanned antennas in that they offer the ability to control the shape of their radiation pattern. As active arrays consist of a multiplicity of transmit and receive modules (TRMs), the task of optimally controlling them in order to generate a desired radiation pattern becomes difficult. The control problem is especially true of conformal (non-planar) array antennas that require additional phase control to achieve good radiation pattern performance. This thesis describes a number of significant advances in the optimisation of array antenna performance. Firstly a genetic algorithm (GA) is shown to be effective at optimising both planar and conformal antenna performance. A number of examples are used to illustrate and promote the basic optimisation concept. Secondly, in this thesis the techniques are advanced to apply multiobjective evolutionary optimisation algorithms to array performance optimisation. It is shown that Evolutionary Algorithms allow users to simultaneously optimise many aspects of array performance without the need to fine-tune a large number of weights. The multiple-objective analysis methods shown demonstrate the advantages to be gained by holding knowledge of the Pareto optimal solution set. Thirdly, this thesis examines the problems of optimising the design of large (many element) array antennas. Larger arrays are often divided into smaller sub-arrays for manufacturing reasons and to promote formation of difference beam patterns for monopulse operation. In the past, the partitioning has largely been left to trial-and-error or simple randomisation techniques. This thesis describes a new and novel approach for optimally subdividing both planar and conformal array antennas as well as improving gain patterns in a single optimisation process. This approach contains a new method of partitioning array antennas, inspired from a biological process and is also presented and optimised using evolutionary algorithms. Additionally, the technique can be applied to any size or shape of array antenna, with the processing load dependent on the number of subarrays, rather than the number of elements. Finally, the success of these new techniques is demonstrated by presenting a range of performance optimised examples of planar and conformal array antenna installations including examples of optimally evolved subarray partitions.
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Nwamba, 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.
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Thesis (M.S.)--Missouri University of Science and Technology, 2009.Vita. 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).
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Morrison, Ronald W. "Designing evolutionary algorithms for dynamic environments /." Berlin ; New York ; Paris : Springer, 2004. http://www.springeronline.com/sgw/cda/frontpage/0,11855,1-102-22-29182350-0,00.html?changeHeader=true.
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Johnson, Colin G. "A design framework for evolutionary algorithms." Thesis, University of Kent, 2003. https://kar.kent.ac.uk/13944/.
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Sauerland, Volkmar [Verfasser]. "Algorithm Engineering for some Complex Practise Problems : Exact Algorithms, Heuristics and Hybrid Evolutionary Algorithms / Volkmar Sauerland." Kiel : Universitätsbibliothek Kiel, 2012. http://d-nb.info/1026442745/34.
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Pryde, 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.
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This thesis describes the investigations undertaken to produce a novel hybrid optimisation technique that combines both global and local searching to produce good solutions quickly. Many evolutionary computation and experimental design methods are considered before genetic algorithms and evolutionary operation are combined to produce novel optimisation algorithms. A novel piece of software is created to run two and three factor evolutionary operation experiments. A range of new hybrid small population genetic algorithms are created that contain evolutionary operation in all generations (static hybrids) or contain evolutionary operation in a controlled number of generations (dynamic hybrids). A large number of empirical tests are carried out to determine the influence of operators and the performance of the hybrids over a range of standard test functions. For very small populations, twenty or less individuals, stochastic universal sampling is demonstrated to be the most suitable method of selection. The performance of very small population evolutionary operation hybrid genetic algorithms is shown to improve with larger generation gaps on simple functions and on more complex functions increasing the generation gap does not deteriorate performance. As a result of the testing carried out for this study a generation gap of 0.7 is recommended as a starting point for empirical searches using small population genetic algorithms and their hybrids. Due to the changing presence of evolutionary operation, the generation gap has less influence on dynamic hybrids compared to the static hybrids. The evolutionary operation, local search element is shown to positively influence the performance of the small population genetic algorithm search. The evolutionary operation element in the hybrid genetic algorithm gives the greatest improvement in performance when present in the middle generations or with a progressively greater presence. A recommendation for the information required to be reported for benchmarking genetic algorithm performance is also presented. This includes processor, platform, software information as well as genetic algorithm parameters such as population size, number of generations, crossover method and selection operators and results of testing on a set of standard test functions.
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Jayachandran, Jayakanth. "Improving resiliency using graph based evolutionary algorithms." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Jayachandran_09007dcc807d6ba6.pdf.
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Thesis (M.S.)--Missouri University of Science and Technology, 2010.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed July 19, 2010) Includes bibliographical references (p. 56-62).
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Ozsayin, Burcu. "Multi-objective Combinatorial Optimization Using Evolutionary Algorithms." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/2/12610866/index.pdf.
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Due to the complexity of multi-objective combinatorial optimization problems (MOCO), metaheuristics like multi-objective evolutionary algorithms (MOEA) are gaining importance to obtain a well-converged and well-dispersed Pareto-optimal frontier approximation. In this study, of the well-known MOCO problems, single-dimensional multi-objective knapsack problem and multi-objective assignment problem are taken into consideration. We develop a steady-state and elitist MOEA in order to approximate the Pareto-optimal frontiers. We utilize a territory concept in order to provide diversity over the Pareto-optimal frontiers of various problem instances. The motivation behind the territory definition is to attach the algorithm the advantage of fast execution by eliminating the need for an explicit diversity preserving operator. We also develop an interactive preference incorporation mechanism to converge to the regions that are of special interest for the decision maker by interacting with him/her during the optimization process.
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Paulden, Timothy John. "Combinatorial spanning tree representations for evolutionary algorithms." Thesis, University of Exeter, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.486767.
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The research presented in this thesis lies at the interface between two distinct' fields: combinatorial mathematics and evolutionary algorithm design. We examine a number of combinatorial spanning tree representations, and develop theoretical and empirical results to quantify the intrinsic properties of each representation, focusing on properties that encapsulate the representation's suitability for evolutionary search. In Part I of the thesis, we focus on a selectIon of Cayley codes - namely, the Priifer Code, the Blob Code, and the family of Dandelion-like codes (which includes the Dandelion Code, Happy Code, MHappy Code, and Theta Code). Each of these representations is bijective, and so the efficacy of evolutionary search primarily depends on the representation's locality. We develop a number of results which demonstrate that Dandelion-like codes possess highly desirable locality properties (including bounded locality), while those of the Blob Code and Priifer. Code are inferior. We also present linear-time decoding and'encoding algorithms for the various codes, many of which have not previously appeared in the evolutionary algorithms literature. In Part II, the Theta Code is adapted to give bijective spanning tree representations on graph topologies other than the complete graph. These extended representations retain the desirable properties of the Thet'a Code, including its high locality. We then formulate general principles for developing extended representations of this kind. Finally, in Part III, we study the Edge-Window-Decoder (EWD) representation. We find that the EWD representation has several desirable properties for evolutionary search (includi.ng bounded locality), but possesses an intrinsic bi~ towards path-like trees. We also present a number of theoretical advances, including the first method for generating EWD strings uniformly at random, and a new technique for characterising representational bias using the Wiener index.
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Dick, Grant, and n/a. "Spatially-structured niching methods for evolutionary algorithms." University of Otago. Department of Information Science, 2008. http://adt.otago.ac.nz./public/adt-NZDU20080902.161336.
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Traditionally, an evolutionary algorithm (EA) operates on a single population with no restrictions on possible mating pairs. Interesting changes to the behaviour of EAs emerge when the structure of the population is altered so that mating between individuals is restricted. Variants of EAs that use such populations are grouped into the field of spatially-structured EAs (SSEAs).
Previous research into the behaviour of SSEAs has primarily focused on the impact space has on the selection pressure in the system. Selection pressure is usually characterised by takeover times and the ratio between the neighbourhood size and the overall dimension of space. While this research has given indications into where and when the use of an SSEA might be suitable, it does not provide a complete coverage of system behaviour in SSEAs. This thesis presents new research into areas of SSEA behaviour that have been left either unexplored or briefly touched upon in current EA literature.
The behaviour of genetic drift in finite panmictic populations is well understood. This thesis attempts to characterise the behaviour of genetic drift in spatially-structured populations. First, an empirical investigation into genetic drift in two commonly encountered topologies, rings and torii, is performed. An observation is made that genetic drift in these two configurations of space is independent of the genetic structure of individuals and additive of the equivalent-sized panmictic population. In addition, localised areas of homogeneity present themselves within the structure purely as a result of drifting. A model based on the theory of random walks to absorbing boundaries is presented which accurately characterises the time to fixation through random genetic drift in ring topologies.
A large volume of research has gone into developing niching methods for solving multimodal problems. Previously, these techniques have used panmictic populations. This thesis introduces the concept of localised niching, where the typically global niching methods are applied to the overlapping demes of a spatially structured population. Two implementations, local sharing and local clearing are presented and are shown to be frequently faster and more robust to parameter settings, and applicable to more problems than their panmictic counterparts.
Current SSEAs typically use a single fitness function across the entire population. In the context of multimodal problems, this means each location in space attempts to discover all the optima. A preferable situation would be to use the inherent spatial properties of an SSEA to localise optimisation of peaks. This thesis adapts concepts from multiobjective optimisation with environmental gradients and applies them to multimodal problems. In addition to adapting to the fitness landscape, individuals evolve towards their preferred environmental conditions. This has the effect of separating individuals into regions that concentrate on different optima with the global fitness function. The thesis also gives insights into the expected number of individuals occupying each optima in the problem.
The SSEAs and related models developed in this thesis are of interest to both researchers and end-users of evolutionary computation. From the end-user�s perspective, the developed SSEAs require less a priori knowledge of a given problem domain in order to operate effectively, so they can be more readily applied to difficult, poorly-defined problems. Also, the theoretical findings of this thesis provides a more complete understanding of evolution within spatially-structured populations, which is of interest not only to evolutionary computation practitioners, but also to researchers in the fields of population genetics and ecology.
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Hayward, Kevin. "Application of evolutionary algorithms to engineering design." University of Western Australia. School of Mechanical Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0018.
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The efficiency of the mechanical design process can be improved by the use of evolutionary algorithms. Evolutionary algorithms provide a convenient and robust method to search for appropriate design solutions. Difficult non-linear problems are often encountered during the mechanical engineering design process. Solutions to these problems often involve computationally-intensive simulations. Evolutionary algorithms tuned to work with a small number of solution iterations can be used to automate the search for optimal solutions to these problems. An evolutionary algorithm was designed to give reliable results after a few thousand iterations; additionally the scalability and the ease of application to varied problems were considered. Convergence velocity of the algorithm was improved considerably by altering the mutation-based parameters in the algorithm. Much of this performance gain can be attributed to making the magnitude of the mutation and the minimum mutation rates self-adaptive. Three motorsport based design problems were simulated and the evolutionary algorithm was applied to search for appropriate solutions. The first two, a racing-line generator and a suspension kinematics simulation, were investigated to highlight properties of the evolutionary algorithm: reliability; solution representation; determining variable/performance relationships; and multiple objectives were discussed. The last of these problems was the lap-time simulation of a Formula SAE vehicle. This problem was solved with 32 variables, including a number of major conceptual differences. The solution to this optimisation was found to be significantly better than the 2004 UWA Motorsport vehicle, which finished 2nd in the 2005 US competition. A simulated comparison showed the optimised vehicle would score 62 more points (out of 675) in the dynamic events of the Formula SAE competition. Notably the optimised vehicle had a different conceptual design to the actual UWA vehicle. These results can be used to improve the design of future Formula SAE vehicles. The evolutionary algorithm developed here can be used as an automated search procedure for problems where performance solutions are computationally intensive.
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Whitacre, 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.
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The objective of Evolutionary Computation is to solve practical problems (e.g.optimization, data mining) by simulating the mechanisms of natural evolution. This thesis addresses several topics related to adaptation and self-organization in evolving systems with the overall aims of improving the performance of Evolutionary Algorithms (EA), understanding its relation to natural evolution, and incorporating new mechanisms for mimicking complex biological systems. Part I of this thesis presents a new mechanism for allowing an EA to adapt its behavior in response to changes in the environment. Using the new approach, adaptation of EA behavior (i.e. control of EA design parameters) is driven by an analysis of population dynamics, as opposed to the more traditional use of fitness measurements. Comparisons with a number of adaptive control methods from the literature indicate substantial improvements in algorithm performance for a range of artificial and engineering design problems. Part II of this thesis involves a more thorough analysis of EA behavior based on the methods derived in Part 1. In particular, several properties of EA population dynamics are measured and compared with observations of evolutionary dynamics in nature. The results demonstrate that some large scale spatial and temporal features of EA dynamics are remarkably similar to their natural counterpart. Compatibility of EA with the Theory of Self-Organized Criticality is also discussed. Part III proposes fundamentally new directions in EA research which are inspired by the conclusions drawn in Part II. These changes involve new mechanisms which allow selforganization of the EA to occur in ways which extend beyond its common convergence in parameter space. In particular, network models for EA populations are developed where the network structure is dynamically coupled to EA population dynamics. Results indicate strong improvements in algorithm performance compared to cellular Genetic Algorithms and non-distributed EA designs. Furthermore, topological analysis indicates that the population network can spontaneously evolve to display similar characteristics to the interaction networks of complex biological systems.
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Moraglio, Alberto. "Towards a geometric unification of evolutionary algorithms." Thesis, University of Essex, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.446045.
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Picardi, Chiara. "Characterization of neurological disorders using evolutionary algorithms." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/21702/.
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The life expectancy increasing, in the last few decades, leads to a large diffusion of neurodegenerative age-related diseases such as Parkinson’s disease. Neurodegenerative diseases are part of the huge category of neurological disorders, which comprises all the disorders affecting the central nervous system. These conditions have a terrible impact on life quality of both patients and their families, but also on the costs associated to the society for their diagnosis and management. In order to reduce their impact on individuals and society, new better strategies for the diagnosis and monitoring of neurological disorders need to be considered. The main aim of this study is investigating the use of artificial intelligence techniques as a tool to help the doctors in the diagnosis and the monitoring of two specific neurological disorders (Parkinson’s disease and dystonia), for which no objective clinical assessments exist. The evolutionary algorithms are chosen as the artificial intelligence technique to evolve the best classifiers. The classifiers evolved by the chosen technique are then compared with those evolved by two popular well-known techniques: artificial neural network and support vector machine. All the evolved classifiers are not only able to distinguish among patients and healthy subjects but also among different subgroups of patients. For Parkinson’s disease: two different cognitive impairment subgroups of patients are considered, with the aim of an early diagnosis and a better monitoring. For dystonia: two kinds of dystonia patients are considered (organic and functional) to have a better insight in the division of the two groups. The results obtained for Parkinson’s disease are encouraging and evidenced some differences among the cognitive impairment subgroups. Dystonia results are not satisfactory at this stage, but the study presents some limitations that could be overcome in future work.
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Pacula, Maciej. "Evolutionary algorithms for compiler-enabled program autotuning." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66313.
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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 116-122).
PetaBricks [4, 21, 7, 3, 5] is an implicitly parallel programming language which, through the process of autotuning, can automatically optimize programs for fast QoS-aware execution on any hardware. In this thesis we develop and evaluate two PetaBricks autotuners: INCREA and SiblingRivalry. INCREA, based on a novel bottom-up evolutionary algorithm, optimizes programs offline at compile time. SiblingRivalry improves on INCREA by optimizing online during a program's execution, dynamically adapting to changes in hardware and the operating system. Continuous adaptation is achieved through racing, where half of available resources are devoted to always-on learning. We evaluate INCREA and SiblingRivalry on a large number of real-world benchmarks, and show that our autotuners can significantly speed up PetaBricks programs with respect to many non-tuned and mis-tuned baselines. Our results indicate the need for a continuous learning loop that can optimize efficiently by exploiting online knowledge of a program's performance. The results leave open the question of how to solve the online optimization problem on all cores, i.e. without racing.
by Maciej Pacula.
M.Eng.
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Baruani, Atumbe Jules. "Network engineering using multi-objective evolutionary algorithms." Thesis, Stellenbosch : Stellenbosch University, 2007. http://hdl.handle.net/10019.1/21548.
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Thesis (MSc)--University of Stellenbosch, 2007.ENGLISH ABSTRACT: We use Evolutionary Multi-Objective Optimisation (EMOO) algorithms to optimise objective functions that reflect situations in communication networks. These include functions that optimise Network Engineering (NE) objective functions in core, metro and wireless sensor networks. The main contributions of this thesis are threefold. Routing and Wavelength Assignment (RWA) for IP backbone networks. Routing and Wavelength Assignment (RWA) is a problem that has been widely addressed by the optical research community. A recent interest in this problem has been raised by the need to achieve routing optimisation in the emerging generation multilayer networks where data networks are layered above a Dense Wavelength Division Multiplexing (DWDM) network. We formulate the RWA as both a single and a multi-objective optimisation problem which are solved using a two-step solution where (1) a set of paths are found using genetic optimisation and (2) a graph coloring approach is implemented to assign wavelengths to these paths. The experimental results from both optimisation scenarios reveal the impact of (1) the cost metric used which equivalently defines the fitness function (2) the algorithmic solution adopted and (3) the topology of the network on the performance achieved by the RWA procedure in terms of path quality and wavelength assignment. Optimisation of Arrayed Waveguide Grating (AWG) Metro Networks. An Arrayed Waveguide Grating (AWG) is a device that can be used as a multiplexer or demultiplexer in WDM systems. It can also be used as a drop-and-insert element or even a wavelength router. We take a closer look at how the hardware and software parameters of an AWG can be fine tuned in order to maximise throughput and minimise the delay. We adopt a multi-objective optimisation approach for multi-service AWG-based single hop metro WDM networks. Using a previously proposed multi-objective optimisation model as a benchmark, we propose several EMOO solutions and compare their efficiency by evaluating their impact on the performance achieved by the AWG optimisation process. Simulation reveals that (1) different EMOO algorithms can exhibit different performance patterns and (2) good network planning and operation solutions for a wide range of traffic scenarios can result from a well selected EMOO algorithm. Wireless Sensor Networks (WSNs) Topology (layout) Optimisation. WSNs have been used in a number of application areas to achieve vital functions in situations where humans cannot constantly be available for certain tasks such as in hostile areas like war zones, seismic sensing where continuous inspection and detection are needed, and many other applications such as environment monitoring, military operations and surveillance. Research and practice have shown that there is a need to optimise the topology (layout) of such sensors on the ground because the position on which they land may affect the sensing efficiency. We formulate the problem of layout optimisation as a multi-objective optimisation problem consisting of maximising both the coverage (area) and the lifetime of the wireless sensor network. We propose different algorithmic evolutionary multi-objective methods and compare their performance in terms of Pareto solutions. Simulations reveal that the Pareto solutions found lead to different performance patterns and types of layouts.
AFRIKAANSE OPSOMMING: Ons gebruik ”Evolutionary Multi-Objective Optimisation (EMOO)” algoritmes om teiken funksies, wat egte situasies in kommunikasie netwerke voorstel, te optimiseer. Hierdie sluit funksies in wat ”Network Engineering” teiken funksies in kern, metro en wireless sensor netwerke optimiseer. Die hoof doelwitte van hierdie tesis is dus drievuldig. RWA vir IP backbone netwerke ”Routing and Wavelength Assignment (RWA)” is ’n probleem wat al menigte kere in die optiese navorsings kringe aangespreek is. Belangstelling in hierdie veld het onlangs ontstaan a.g.v. die aanvraag na die optimisering van routering in die opkomende generasie van veelvuldige vlak netwerke waar data netwerke in ’n vlak ho¨er as ’n ”Dense Wavelength Division Multiplexing (DWDM)” netwerk gele is. Ons formuleer die RWA as beide ’n enkele and veelvuldige teiken optimiserings probleem wat opgelos word deur ’n 2-stap oplossing waar (1) ’n stel roetes gevind word deur genetiese optimisering te gebruik en (2) ’n grafiek kleuring benadering geimplementeer word om golflengtes aan hierdie roetes toe te ken. Die eksperimentele resultate van beide optimiserings gevalle vertoon die impak van (1) die koste on wat gebruik word wat die ekwalente fitness funksie definieer , (2) die algoritmiese oplossing wat gebruik word en (3) die topologie van die netwerk op die werkverrigting van die RWA prosedure i.t.v. roete kwaliteit en golflengte toekenning. Optimisering van AWG Metro netwerk ’n ”Arrayed Waveguide Grating (AWG)” is ’n toestel wat gebruik kan word as ’n multipleksor of demultipleksor in WDM sisteme. Dit kan ook gebruik word as ’n val-en-inplaas element of selfs ’n golflengte router. Kennis word ingestel na hoe die hardeware en sagteware parameters van ’n AWG ingestel kan word om die deurset tempo te maksimeer en vertragings te minimiseer. Ons neem ’n multi-teiken optimiserings benadering vir multi diens, AWG gebaseerde, enkel skakel, metro WDM netwerke aan. Deur ’n vooraf voorgestelde multi teiken optimiserings model as ”benchmark” te gebruik, stel ons ’n aantal EMOO oplossings voor en vergelyk ons hul effektiwiteit deur hul impak op die werkverrigting wat deur die AWG optimiserings proses bereik kan word, te vergelyk. Simulasie modelle wys dat (1) verskillende EMOO algoritmes verskillende werkverrigtings patrone kan vertoon en (2) dat goeie netwerk beplanning en werking oplossings vir ’n wye verskeidenheid van verkeer gevalle kan plaasvind a.g.v ’n EMOO algoritme wat reg gekies word. ”Wireless Sensor Network” Topologie Optimisering WSNs is al gebruik om belangrike funksies te verrig in ’n aantal toepassings waar menslike beheer nie konstant beskikbaar is nie, of kan wees nie. Voorbeelde van sulke gevalle is oorlog gebiede, seismiese metings waar aaneenlopende inspeksie en meting nodig is, omgewings meting, militˆere operasies en bewaking. Navorsing en praktiese toepassing het getoon dat daar ’n aanvraag na die optimisering van die topologie van sulke sensors is, gebaseer op gronde van die feit dat die posisie waar die sensor beland, die effektiwiteit van die sensor kan affekteer. Ons formuleer die probleem van uitleg optimisering as ’n veelvuldige vlak optimiserings probleem wat bestaan uit die maksimering van beide die bedekkings area en die leeftyd van die wireless sensor netwerk. Ons stel verskillende algoritmiese, evolutionˆere, veelvuldige vlak oplossings voor en vergelyk hul werkverrigting i.t.v Pareto oplossings. Simulasie modelle wys dat die Pareto oplossings wat gevind word lei na verskillende werkverrigtings patrone en uitleg tipes.
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Kruger, Markus Gustav. "On evolutionary algorithms for effective quantum computing." Thesis, Stellenbosch : Stellenbosch University, 2012. http://hdl.handle.net/10019.1/20095.
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Thesis (MSc)--Stellenbosch University, 2012.ENGLISH ABSTRACT: The goal of this thesis is to present evolutionary algorithms, and demonstrate their applicability in quantum computing. As an introduction to evolutionary algorithms, it is applied to the simple but still challenging (from a computational viewpoint) Travelling Salesman Problem (TSP). This example is used to illustrate the e ect of various parameters like selection method, and maximum population size on the accuracy and e ciency of the evolutionary algorithms. For the sample problem, the 48 continental state capitals of the USA, solutions are evolved and compared to the known optimal solution. From this investigation tournament selection was shown to be the most e ective selection method, and that a population of 200 individuals per generation gave the most e ective convergence rates. In the next part of the thesis, evolutionary algorithms are applied to the generation of optimal quantum circuits for the following cases: The identity transformation : Picked for its simplicity as a test of the correct implementation of the evolutionary algorithm. The results of this investigation showed that the solver program functions correctly and that evolutionary algorithms can indeed nd valid solutions for this kind of problem. The work by Ding et al. [16] on optimal circuits for the two-qubit entanglement gate, controlled-S gate as well as the three qubit entanglement gate are solved by means of EA and the results compared. In all cases similar circuits are produced in fewer generations than the application of Ding et al. [16]. The three qubit quantum Fourier transform gate was also attempted, but no convergence was attained. The quantum teleportation algorithm is also investigated. Firstly the nature of the transformation that leads to quantum teleportation is considered. Next an e ective circuit is sought using evolutionary algorithms. The best result is one gate longer than Brassard [11], and seven gates longer than Yabuki [61].
AFRIKAANSE OPSOMMING: Die doel van hierdie tesis is om evolusionêre algoritmes te ondersoek en hulle toepaslikheid op kwantumkomputasie te demonstreer. As 'n inleiding tot evolusionêre algoritmes is die eenvoudige, maar steeds komputasioneel uitdagende handelsreisigerprobleem ondersoek. Die invloed van die keuse van 'n seleksie metode, sowel as die invloed van die maksimum aantal individue in 'n generasie op die akkuraatheid en e ektiwiteit van die algoritmes is ondersoek. As voorbeeld is die 48 kontinentale hoofstede van die state van die VSA gekies. Die oplossings wat met evolusionêre algoritmes verkry is, is met die bekende beste oplossings vergelyk. Die resultate van hierdie ondersoek was dat toernooi seleksie die mees e ektiewe seleksie metode is, en dat 200 individue per generasie die mees e ektiewe konvergensie tempo lewer. Evolusionêre algoritmes word vervolgens toegepas om optimale oplossings vir die volgende kwantumalgoritmes te genereer: Die identiteitstransformasie: Hierdie geval is gekies as 'n eenvoudige toepassing met 'n bekende oplossing. Die resultaat van hierdie toepassing van die program was dat dit korrek funksioneer, en vinnig by die korrekte oplossings uitkom. Vervolgens is daar ondersoek ingestel na vier van die gevalle wat in Ding et al. [16] bespreek word. Die spesi eke transformasies waarna gekyk is, is 'n optimale stroombaan vir twee kwabis verstrengeling, 'n beheerde-S hek, 'n drie kwabis verstrengelings hek, en 'n drie kwabis kwantum Fourier transform hek. In die eerste drie gevalle stem die oplossings ooreen met die van Ding et al. [16], en is die konvergensie tempo vinniger. Daar is geen oplossing vir die kwantum Fourier transform verkry nie. Laastens is daar na die kwantumteleportasiealgoritme gekyk. Die eerste stap was om te kyk na die transformasie wat in hierdie geval benodig word, en daarna is gepoog om 'n e ektiewe stroombaan te evolueer. Die beste resultaat was een hek langer as Brassard [11], en sewe hekke langer as Yabuki [61].
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Kirkland, Oliver. "Multi-objective evolutionary algorithms for data clustering." Thesis, University of East Anglia, 2014. https://ueaeprints.uea.ac.uk/51331/.
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In this work we investigate the use of Multi-Objective metaheuristics for the data-mining task of clustering. We �first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform investigations into the performance of different Multi-Objective clustering algorithms. In the context of clustering, a robust measure for evaluating clustering solutions is an important component of an algorithm. These Cluster Quality Measures (CQMs) should rely solely on the structure of the clustering solution. A robust CQM should have three properties: it should be able to reward a \good" clustering solution; it should decrease in value monotonically as the solution quality deteriorates and, it should be able to evaluate clustering solutions with varying numbers of clusters. We review existing CQMs and present an experimental evaluation of their robustness. We find that measures based on connectivity are more robust than other measures for cluster evaluation. We then introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Objective optimisation in clustering is desirable because it permits the incorporation of multiple measures of cluster quality. Since the definition of what constitutes a good clustering is far from clear, it is beneficial to develop algorithms that allow for multiple CQMs to be accommodated. The selection of the clustering quality measures to use as objectives for MOCA is informed by our previous work with internal evaluation measures. We explain the implementation details and perform experimental work to establish its worth. We compare MOCA with k-means and find some promising results. We�find that MOCA can generate a pool of clustering solutions that is more likely to contain the optimal clustering solution than the pool of solutions generated by k-means. We also perform an investigation into the performance of different implementations of MOEA algorithms for clustering. We�find that representations of clustering based around centroids and medoids produce more desirable clustering solutions and Pareto fronts. We also �find that mutation operators that greatly disrupt the clustering solutions lead to better exploration of the Pareto front whereas mutation operators that modify the clustering solutions in a more moderate way lead to higher quality clustering solutions. We then perform more specific investigations into the performance of mutation operators focussing on operators that promote clustering solution quality, exploration of the Pareto front and a hybrid combination. We use a number of techniques to assess the performance of the mutation operators as the algorithms execute. We confirm that a disruptive mutation operator leads to better exploration of the Pareto front and mutation operators that modify the clustering solutions lead to the discovery of higher quality clustering solutions. We find that our implementation of a hybrid mutation operator does not lead to a good improvement with respect to the other mutation operators but does show promise for future work.
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Khan, Wali. "Hybrid multiobjective evolutionary algorithms based on decomposition." Thesis, University of Essex, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549297.
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Smiley, Aref. "EVOLUTIONARY OPTIMIZATION OF ATRIAL FIBRILLATION DIAGNOSTIC ALGORITHMS." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1407025535.
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Jalalian, Hamid Reza. "Decomposition evolutionary algorithms for noisy multiobjective optimization." Thesis, University of Essex, 2016. http://repository.essex.ac.uk/16828/.
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Multi-objective problems are a category of optimization problem that contain more than one objective function and these objective functions must be optimized simultaneously. Should the objective functions be conflicting, then a set of solutions instead of a single solution is required. This set is known as Pareto optimal. Multi-objective optimization problems arise in many real world applications where several competing objectives must be evaluated and optimal solutions found for them, in the presence of trade offs among conflicting objectives. Maximizing returns while minimizing the risk of stock market investments, or maximizing performance whilst minimizing fuel consumption and hazardous gas emission when buying a car are typical examples of real world multi-objective optimization problems. In this case a number of optimal solutions can be found, known as non-dominated or Pareto optimal solutions. Pareto optimal solutions are reached when it is impossible to improve one objective without making the others worse. Classical ways to address this problem used direct or gradient based methods that rendered them insufficient or computationally expensive for large scale or combinatorial problems. Other difficulties attended the classical methods, such as problem knowledge, which may not be available, or sensitivity to some problem features. For example, finding solutions on the entire Pareto optimal set can only be guaranteed for convex problems. Classical methods for generating the Pareto front set aggregate the objectives into a single or parametrized function before search. Thus, several runs and parameter settings are performed to achieve a set of solutions that approximate the Pareto optimals. Subsequently new methods have been developed, based on computer experiments with meta-heuristic algorithms. Most of these meta-heuristics implement some sort of stochastic search method, amongst which the 'Evolutionary Algorithm' is garnering much attention. It possesses several characteristics that make it a desirable method for confronting multi-objective problems. As a result, a number of studies in recent decades have developed or modified the MOEA for different purposes. This algorithm works with a population of solutions which are capable of searching for multiple Pareto optimal solutions in a single run. At the same time, only the fittest individuals in each generation are offered the chance for reproduction and representation in the next generation. The fitness assignment function is the guiding system of MOEA. Fitness value represents the strength of an individual. Unfortunately, many real world applications bring with them a certain degree of noise due to natural disasters, inefficient models, signal distortion or uncertain information. This noise affects the performance of the algorithm's fitness function and disrupts the optimization process. This thesis explores and targets the effect of this disruptive noise on the performance of the MOEA. In this thesis, we study the noisy MOP and modify the MOEA/D to improve its performance in noisy environments. To achieve this, we will combine the basic MOEA/D with the 'Ordinal Optimization' technique to handle uncertainties. The major contributions of this thesis are as follows. First, MOEA/D is tested in a noisy environment with different levels of noise, to give us a deeper understanding of where the basic algorithm fails to handle the noise. Then, we extend the basic MOEA/D to improve its noise handling by employing the ordinal optimization technique. This creates MOEA/D+OO, which will outperform MOEA/D in terms of diversity and convergence in noisy environments. It is tested against benchmark problems of varying levels of noise. Finally, to test the real world application of MOEA/D+OO, we solve a noisy portfolio optimization with the proposed algorithm. The portfolio optimization problem is a classic one in finance that has investors wanting to maximize a portfolio's return while minimizing risk of investment. The latter is measured by standard deviation of the portfolio's rate of return. These two objectives clearly make it a multi-objective problem.
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Utamima, Amalia. "Evolutionary Algorithms to Solve Agricultural Routing Planning." Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/82468.
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This doctoral thesis aims to develop effective Evolutionary Algorithms that can be competitively applied to Agricultural Routing Planning (ARP) and to formulate an extension of the ARP. The outcomes of this research will impact on the research community with the development of new algorithms as well as the dissemination of findings. This study is significant as it is expected to improve the management of agricultural machinery, to minimise the total cost and the settling time for completing field operations, and to produce better routing plans.
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Yang, Jing. "Designing Superior Evolutionary Algorithms via Insights From Black-Box Complexity Theory." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLX054/document.
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Il a été observé que l'exécution des heuristiques de recherche aléatoire dépend d'un ou de plusieurs paramètres. Un certain nombre de résultats montrent un avantage des paramètres dynamiques, c'est-à-dire que les paramètres de l'algorithme sont modifiés au cours de son exécution. Dans ce travail, nous montrons que la complexité de la boîte noire sans biais de la classe de fonction de référence OneMax est $n ln(n) - cn pm o(n)$ pour une constante $c$ comprise entre $0.2539$ et $0.2665$. L'exécution peut être réalisé avec un algorithme simple de type-(1+1) utilisant une puissance de mutation fitness dépendant. Une fois traduite dans le cas du budget fixe, notre algorithme trouve des solutions plus proches de l'optimum de 13% que celles des meilleurs algorithmes connus.Basé sur la puissance de mutation optimale analysée pour OneMaX, nous montrons qu'un choix auto-ajusté du nombre de bits à retourner atteint le même temps d'exécution (excepté $o(n)$ termes inférieurs) et le même (asymptotique) 13% amélioration de la fitness-distance par rapport au RLS. Le mécanisme d'ajustement doit apprendre de manière adaptative la puissance de mutation actuellement optimale des itérations précédentes. Cela vise à la fois à exploiter le fait que des problèmes généralement différents peuvent nécessiter des puissances de mutation différentes et que, pour un problème fixe, différentes puissances peuvent devenir optimales à différentes étapes du processus d'optimisation.Nous étendons ensuite notre stratégie d'auto-ajustement aux algorithmes évolutifs basés sur la population dans des espaces discrets de recherche. Grosso modo, il consiste à créer la moitié de la descendance avec un taux de mutation qui est deux fois plus élevé que le taux de mutation actuel et l'autre moitié avec la moitié du taux actuel. Le taux de mutation est ensuite mis à jour au taux utilisé dans cette sous-population qui contient la meilleure descendance. Nous analysons comment l'algorithme d'évolution $(1+lambda)$ avec ce taux de mutation auto-ajustable optimise la fonction de test OneMax. Nous montrons que cette version dynamique de $(1+lambda)$~EA trouve l'optimum dans un temps d'optimisation attendu (nombre d'évaluations de la fitness) de $O(nlambda/loglambda+nlog n)$. Le temps est asymptotiquement plus petit que le temps d'optimisation de l'EA classique $(1+lambda)$. Des travaux antérieurs montrent que cette performance est la meilleure possible parmi tous les algorithmes de boîtes noires sans biais unaire basés sur des mutations $lambda$-parallèles.Nous proposons et analysons également une version auto-réglage de l'algorithme évolutionnaire $(1,lambda)$ dans lequel le taux de mutation actuel fait partie de l'individu et donc également sujet à mutation. Une analyse d'exécution rigoureuse sur la fonction de référence OneMax révèle qu'un simple schéma de mutation pour le taux conduit à un temps d'optimisation attendu du meilleur $O(nlambda/loglambda+nlog n)$. Notre résultat montre que l'auto-réglage dans le calcul évolutif peut trouver automatiquement des paramètres optimaux complexes. En même temps, cela prouve qu'un schéma d'auto-ajustement relativement compliqué pour le taux de mutation peut être remplacé par notre schéma endogène simpleIt has been observed that the runtime of randomized search heuristics depend on one or more parameters. A number of results show an advantage of dynamic parameter settings, that is, the parameters of the algorithm are changed during its execution. In this work, we prove that the unary unbiased black-box complexity of the OneMax benchmark function class is $n ln(n) - cn pm o(n)$ for a constant $c$ which is between $0.2539$ and $0.2665$. This runtime can be achieved with a simple (1+1)-type algorithm using a fitness-dependent mutation strength. When translated into the fixed-budget perspective, our algorithm finds solutions which are roughly 13% closer to the optimum than those of the best previously known algorithms.Based on the analyzed optimal mutation strength for OneMax, we show that a self-adjusting choice of the number of bits to be flipped attains the same runtime (apart from $o(n)$ lower-order terms) and the same (asymptotic) 13% fitness-distance improvement over RLS. The adjusting mechanism is to adaptively learn the currently optimal mutation strength from previous iterations. This aims both at exploiting that generally different problems may need different mutation strengths and that for a fixed problem different strengths may become optimal in different stages of the optimization process.We then extend our self-adjusting strategy to population-based evolutionary algorithms in discrete search spaces. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring. We analyze how the $(1+lambda)$ evolutionary algorithm with this self-adjusting mutation rate optimizes the OneMax test function. We prove that this dynamic version of the $(1+lambda)$~EA finds the optimum in an expected optimization time (number of fitness evaluations) of $O(nlambda/loglambda+nlog n)$. This time is asymptotically smaller than the optimization time of the classic $(1+lambda)$ EA. Previous work shows that this performance is best-possible among all $lambda$-parallel mutation-based unbiased black-box algorithms.We also propose and analyze a self-adaptive version of the $(1,lambda)$ evolutionary algorithm in which the current mutation rate is part of the individual and thus also subject to mutation. A rigorous runtime analysis on the OneMax benchmark function reveals that a simple local mutation scheme for the rate leads to an expected optimization time of the best possible $O(nlambda/loglambda+nlog n)$. Our result shows that self-adaptation in evolutionary computation can find complex optimal parameter settings on the fly. At the same time, it proves that a relatively complicated self-adjusting scheme for the mutation rate can be replaced by our simple endogenous scheme