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Dissertations / Theses on the topic 'Evolutionary Algorithms'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

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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2

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
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3

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
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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 algorith
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5

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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6

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 a
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7

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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8

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 p
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9

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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10

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 complex
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11

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 n
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12

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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13

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 candida
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14

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 solutio
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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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16

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 de&amp<br>#64257<br>ning property of the algorithm rather than using an explicit diversity preservation mechanism. This leads to substantial computational e&amp<br>#64259<br>ciency. We test the algorithm on commonly used test problems and compare
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17

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 abou
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18

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
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19

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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20

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
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21

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
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22

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
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23

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
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24

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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25

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 de
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26

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.<br>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 c
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27

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
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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 ach
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29

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.<br>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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31

Johnson, Colin G. "A design framework for evolutionary algorithms." Thesis, University of Kent, 2003. https://kar.kent.ac.uk/13944/.

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32

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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33

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
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34

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.<br>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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35

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 Pare
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36

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
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37

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 t
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38

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 rel
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Whitacre, James M. Chemical Sciences &amp 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. Usi
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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 monitori
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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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 116-122).<br>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 d
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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.<br>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
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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.<br>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
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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 structur
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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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48

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 s
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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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50

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 fit
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