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Dissertations / Theses on the topic 'Evolutionary computer algorithms'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

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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Abdul, Karim Mohamad Sharis. "Computer-aided aesthetics in evolutionary computer aided design." Thesis, Loughborough University, 2004. https://dspace.lboro.ac.uk/2134/27913.

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This thesis presents research into the possibility of developing a computerised system that can evaluate the aesthetics and engineering aspects of solid shapes. One of the research areas is also to include such an evaluation system into an existing evolutionary CAD system which utilizes the Genetic Algorithms (GAs) technology. An extensive literature survey has been carried out to better understand and clarify the vagueness and subjectivity of the concept of aesthetics, which leads to the work of defining and quantifying a set of aesthetic parameters. This research achieves its novelty in aimi

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4

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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5

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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6

Cryan, Mary Elizabeth. "Learning and approximation algorithms for problems motivated by evolutionary trees." Thesis, University of Warwick, 1999. http://wrap.warwick.ac.uk/61093/.

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In this thesis we consider some computational problems motivated by the biological problem of reconstructing evolutionary trees. In this thesis, we are concerned with the design and analysis of efficient algorithms for clearly defined combinatorial problems motived by this application area. We present results for two different kinds of problem. Our first problem is motivated by models of evolution that describe the evolution of biological species in terms of a stochastic process that alters the DNA of species. The particular stochastic model that we considered is called the Two-State General M

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7

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

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8

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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9

Ibrahim, Osman Ali Sadek. "Evolutionary algorithms and machine learning techniques for information retrieval." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/47696/.

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In the context of Artificial Intelligence research, Evolutionary Algorithms and Machine Learning (EML) techniques play a fundamental role for optimising Information Retrieval (IR). However, numerous research studies did not consider the limitation of using EML at the beginning of establishing the IR systems, while other research studies compared EML techniques by only presenting overall final results without analysing important experimental settings such as the training or evolving run-times against IR effectiveness obtained. Furthermore, most papers describing research on EML techniques in IR

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10

Garcia, Najera Abel. "Multi-Objective evolutionary algorithms for vehicle routing problems." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1069/.

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The Vehicle Routing Problem, which main objective is to find the lowest-cost set of routes to deliver goods to customers, has many applications in transportation services. In the past, costs have been mainly associated to the number of routes and the travel distance, however, in real-world problems there exist additional objectives. Since there is no known exact method to efficiently solve the problem in polynomial time, many heuristic techniques have been considered, among which, evolutionary methods have proved to be suitable for solving the problem. Despite this method being able to provide

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11

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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Guo, Qiang. "Evolutionary algorithms and hyper-heuristics for orthogonal packing problems." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/29311/.

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This thesis investigates two major classes of Evolutionary Algorithms, Genetic Algorithms (GAs) and Evolution Strategies (ESs), and their application to the Orthogonal Packing Problems (OPP). OPP are canonical models for NP-hard problems, the class of problems widely conceived to be unsolvable on a polynomial deterministic Turing machine, although they underlie many optimisation problems in the real world. With the increasing power of modern computers, GAs and ESs have been developed in the past decades to provide high quality solutions for a wide range of optimisation and learning problems. T

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13

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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Corus, Dogan. "Runtime analysis of evolutionary algorithms with complex fitness evaluation mechanisms." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/48421/.

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Evolutionary algorithms (EAs) are bio-inspired general purpose optimisation methods which are applicable to a wide range of problems. The performance of an EA can vary considerably according to the problem it tackles. Runtime analyses of EAs rigorously prove bounds on the expected computational resources required by the EA to solve a given problem. A crucial component of an EA is the way it evaluates the quality (i.e. fitness) of candidate solutions. Different fitness evaluation methods may drastically change the efficiency of a given EA. In this thesis, the effects of different fitness evalua

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15

Tati, Kiran Kumar Smilkstein Tina Harriet. "General purpose evolutionary algorithm testbed." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/5359.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on January 19, 2010). Thesis advisor: Dr. Tina Smilkstein. Includes bibliographical references.

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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.<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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17

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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18

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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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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Aguirre, Ortega Oswaldo. "Multi-objective network reliability optimization using evolutionary algorithms." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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21

Aggarwal, Varun. "Analog circuit optimization using evolutionary algorithms and convex optimization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40525.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 83-88).<br>In this thesis, we analyze state-of-art techniques for analog circuit sizing and compare them on various metrics. We ascertain that a methodology which improves the accuracy of sizing without increasing the run time or the designer effort is a contribution. We argue that the accuracy of geometric programming can be improved without adversely influencing the run time or increasing the designer's effort. This is facilitated by dec

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22

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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23

Carter, Richard G. "An investigation into tournament poker strategy using evolutionary algorithms." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2392.

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Poker has become the subject of an increasing amount of study in the computational intelligence community. The element of imperfect information presents new and greater challenges than those previously posed by games such as checkers and chess. Advances in computer poker have great potential, since reasoning under conditions of uncertainty is typical of many real world problems. To date the focus of computer poker research has centred on the development of ring game players for limit Texas hold’em. For a computer to compete in the most prestigious poker events, however, it will be required to

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Mambrini, Andrea. "Theory grounded design of genetic programming and parallel evolutionary algorithms." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5928/.

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Evolutionary algorithms (EAs) have been successfully applied to many problems and applications. Their success comes from being general purpose, which means that the same EA can be used to solve different problems. Despite that, many factors can affect the behaviour and the performance of an EA and it has been proven that there isn't a particular EA which can solve efficiently any problem. This opens to the issue of understanding how different design choices can affect the performance of an EA and how to efficiently design and tune one. This thesis has two main objectives. On the one hand we wi

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Ong, Shyhwang, and Anton Täcklind. "Performance differences between multi-objective evolutionary algorithms in different environments." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186336.

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The time required to find the optimal solution to a problem increases exponentially as thesize and amount of parameters increases. Evolutionary algorithms tackle this problemheuristically by generating better solutions over time. When there is more than oneobjective in a problem, algorithms must generate multiple solutions to fit any preference inspecific objectives. As the amount of objectives increases, the effort required to generategood sets of solutions increases.This study investigated how increasing the amount of objectives impacted fourmulti-objective evolutionary algorithms differentl

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Mbagwu, Otitochi (Otitochi E. ). "Design and implementation of evolutionary computation algorithms for volunteer compute networks." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91846.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June 2014.<br>Cataloged from PDF version of thesis. "May 23, 2014."<br>Includes bibliographical references (page 37).<br>We implemented a distributed evolutionary computation system titled EvoGPJ Star (EGS) and deployed the system onto Boinc, a volunteer computing network (VCN). Evolutionary computation is computationally expensive and VCN allows more cost-effective cluster computing since resources are donated. In addition, we believe that the design similarities between EGS and

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Yang, Yini. "Training Neural Networks with Evolutionary Algorithms for Flash Call Verification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-283039.

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Evolutionary algorithms have achieved great performance among a wide range of optimization problems. In this degree project, the network optimization problem has been reformulated and solved in an evolved way. A feasible evolutionary framework has been designed and implemented to train neural networks in supervised learning scenarios. Under the structure of evolutionary algorithms, a well-defined fitness function is applied to evaluate network parameters, and a carefully derived form of approximate gradients is used for updating parameters. Performance of the framework has been tested by train

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Magnus, Cristyn. "Evolutionary sound a non-symbolic approach to creating sonic art with genetic algorithms /." Diss., [La Jolla, Calif.] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3396736.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from 1st page of PDF file (viewed Mar. 9, 2010). Available via ProQuest Digital Dissertations. Vita. Includes 3 compact discs containing audio examples for the dissertation. Includes bibliographical references: P. 137-142.

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Skolicki, Zbigniew Maciej. "An analysis of island models in evolutionary computation." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2954.

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Thesis (Ph. D.)--George Mason University, 2007.<br>Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: Kenneth A. De Jong. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. Vita: p. 422. Includes bibliographical references (p. 413-421). Also available in print.

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Gamalielsson, Jonas. "Models for Protein Structure Prediction by Evolutionary Algorithms." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-623.

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<p>Evolutionary algorithms (EAs) have been shown to be competent at solving complex, multimodal optimisation problems in applications where the search space is large and badly understood. EAs are therefore among the most promising classes of algorithms for solving the Protein Structure Prediction Problem (PSPP). The PSPP is how to derive the 3D-structure of a protein given only its sequence of amino acids. This dissertation defines, evaluates and shows limitations of simplified models for solving the PSPP. These simplified models are off-lattice extensions to the lattice HP model which has bee

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Le, Khoi Nguyen. "A study of evolutionary multiobjective algorithms and their application to knapsack and nurse scheduling problems." Thesis, University of Nottingham, 2011. http://eprints.nottingham.ac.uk/13116/.

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Evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number o

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Andrade, Carlos Eduardo de 1981. "Evolutionary algorithms for some problems in telecommunications = Algoritmos evolutivos para alguns problemas em telecomunicações." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275653.

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Orientadores: Flavio Keidi Miyazawa, Mauricio Guilherme de Carvalho Resende<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-27T21:53:09Z (GMT). No. of bitstreams: 1 Andrade_CarlosEduardode_D.pdf: 4654702 bytes, checksum: 566cb3ea8fc876147ffa6df2ec8482b3 (MD5) Previous issue date: 2015<br>Resumo: Nos últimos anos, as redes de telecomunicação tem experienciado um grande aumento no fluxo de dados. Desde a utilização massiva de vídeo sob demanda até o incontável número de dispositivos móveis trocando texto e vídeo, o tráfego

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Johansson, Ted. "Tactical Simulation in Air-To-Air Combat : Evolutionary Algorithms and Behavior Tree Framework." Thesis, Luleå tekniska universitet, Datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-71334.

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Heinze, Glenn. "Application of evolutionary algorithm strategies to entity relationship diagrams /." View PDF document on the Internet, 2004. http://library.athabascau.ca/scisthesis/Heinze.pdf.

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Vulli, Srinivasa Shivakar. "An artificial life approach to evolutionary computation: from mobile cellular algorithms to artificial ecosystems." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Vulli_09007dcc807d69aa.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. 57-60).

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Schwartz, Bonnie Jo. "An Evolutionary Programming Algorithm for Automatic Chromatogram Alignment." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1175715183.

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Smart, Otis Lkuwamy. "Evolutionary algorithms and frequent itemset mining for analyzing epileptic oscillations." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/22709.

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Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2007.<br>Committee Chair: Vachtsevanos, George J.; Committee Co-Chair: Litt, Brian; Committee Member: Butera, Robert J.; Committee Member: Echauz, Javier; Committee Member: Howard, Ayanna M.; Committee Member: Williams, Douglas B.

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Nystrand, Anders. "Unsupervised Imitation In Evolved Robots." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-976.

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<p>Imitation learning has been studied from a large range of disciplines, including adaptive robotics. In adaptive robotics the focus is often on how robots can learn tasks by imitating experts. In order to build robots able to imitate a number of problems must be solved, including: How does the robot know when and what to imitate? How does the robot link the recognition of observed actions to the execution of the same actions? This thesis presents an approach using unsupervised imitation where artificial evolution is used to find solutions to the problems. The approach is tested in a number o

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Urquhart, Neil B. "Solving real-world routing problems using evolutionary algorithms and multi-agent-systems." Thesis, Edinburgh Napier University, 2003. http://researchrepository.napier.ac.uk/Output/2748.

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This thesis investigates the solving of routing problems using Evolutionary Algorithms (EAs). Routing problems are known to be hard and may possess complex search spaces. Evolutionary algorithms are potentially powerful tools for finding solutions within complex search spaces. The problem investigated is the routing of deliveries to households within an urban environment; the most common instance of this problem is that of daily postal deliveries. A representation known as Street Based Routing (SBR) is presented. This is a problem representation that makes use of the real world groupings of st

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Rohling, Gregory Allen. "Multiple Objective Evolutionary Algorithms for Independent, Computationally Expensive Objectives." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4835.

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This research augments current Multiple Objective Evolutionary Algorithms with methods that dramatically reduce the time required to evolve toward a region of interest in objective space. Multiple Objective Evolutionary Algorithms (MOEAs) are superior to other optimization techniques when the search space is of high dimension and contains many local minima and maxima. Likewise, MOEAs are most interesting when applied to non-intuitive complex systems. But, these systems are often computationally expensive to calculate. When these systems require independent computations to evaluate each object

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Jakobsson, Henrik. "Inversion of an Artificial Neural Network Mapping by Evolutionary Algorithms with Sharing." Thesis, University of Skövde, Department of Computer Science, 1998. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-165.

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<p>Inversion of the artificial neural network mapping is a relatively unexplored field of science. By inversion we mean that a search is conducted to find what input patterns that corresponds to a specific output pattern according to the analysed network. In this report, an evolutionary algorithm is proposed to conduct the search for input patterns. The hypothesis is that the inversion with the evolutionary search-method will result in multiple, separate and equivalent input patterns and not get stuck in local optima which possibly would cause the inversion to result in erroneous answer. Besid

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Muthuswamy, Shanthi. "Discrete particle swarm optimization algorithms for orienteering and team orienteering problems." Diss., Online access via UMI:, 2009.

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Al-Ouran, Rami. "Motif Selection: Identification of Gene Regulatory Elements using Sequence CoverageBased Models and Evolutionary Algorithms." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1449003717.

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DeBruyne, Sandra DeBruyne. "Bio-Inspired Evolutionary Algorithms for Multi-Objective Optimization Applied to Engineering Applications." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1542282067378143.

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Hardarson, Gisli. "The Effects of Using Results from Inversion by Evolutionary Algorithms to Retrain Artificial Neural Networks." Thesis, University of Skövde, Department of Computer Science, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-411.

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<p>The aim of inverting artificial neural networks (ANNs) is to find input patterns that are strongly classified as a predefined class. In this project an ANN is inverted by an evolutionary algorithm. The network is retrained by using the patterns extracted by the inversion as counter-examples, i.e. to classify the patterns as belonging to no class, which is the opposite of what the network previously did. The hypothesis is that the counter-examples extracted by the inversion will cause larger updates of the weights of the ANN and create a better mapping than what is caused by retraining using

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Aldogan, Deniz. "Memetic Algorithms For Timetabling Problems In Private Schools." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606218/index.pdf.

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The aim of this study is to introduce a real-world timetabling problem that exists in some private schools in Turkey and to solve such problem instances utilizing memetic algorithms. Being a new type of problem and for privacy reasons, there is no real data available. Hence for benchmarking purposes, a random data generator has been implemented. Memetic algorithms (MAs) combining genetic algorithms and hill-climbing are applied to solve synthetic problem instances produced by this generator. Different types of recombination and mutation operators based on the hierarchical structure of the

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47

Herrington, Hira B. "A Heuristic Evolutionary Method for the Complementary Cell Suppression Problem." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/28.

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Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research. Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a

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Saka, Esin. "A Comparative Study Of Tree Encodings For Evolutionary Computing." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606317/index.pdf.

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One of the most important factors on the success of evolutionary algorithms (EAs) about trees is the representation of them. The representation should exhibit efficiency, locality and heritability to enable effective evolutionary computing. Neville proposed three different methods for encoding labeled trees. The first one is similar with Pr&uuml<br>fer&#039<br>s encoding. In 2001, it is reported that, the use of Pr&uuml<br>fer numbers is a poor representation of spanning trees for evolutionary search, since it has low locality for random trees. In the thesis Neville&#039<br>s other two encodin

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Abdel, Raheem Mohamed. "Electimize a new evolutionary algorithm for optimization with applications in construction engineering." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4833.

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Optimization is considered an essential step in reinforcing the efficiency of performance and economic feasibility of construction projects. In the past few decades, evolutionary algorithms (EAs) have been widely utilized to solve various types of construction-related optimization problems due to their efficiency in finding good solutions in relatively short time periods. However, in many cases, these existing evolutionary algorithms failed to identify the optimal solution to several optimization problems. As such, it is deemed necessary to develop new approaches in order to help identify bett

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Chan, Heather Y. "Gene Network Inference and Expression Prediction Using Recurrent Neural Networks and Evolutionary Algorithms." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2648.

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We demonstrate the success of recurrent neural networks in gene network inference and expression prediction using a hybrid of particle swarm optimization and differential evolution to overcome the classic obstacle of local minima in training recurrent neural networks. We also provide an improved validation framework for the evaluation of genetic network modeling systems that will result in better generalization and long-term prediction capability. Success in the modeling of gene regulation and prediction of gene expression will lead to more rapid discovery and development of therapeutic medici

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