Relevant bibliographies by topics / Genetic algorithm / Dissertations / Theses

Dissertations / Theses on the topic 'Genetic algorithm'

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Published: 4 June 2021
Last updated: 8 June 2024

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1

Harris, Steven C. "A genetic algorithm for robust simulation optimization." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178645751.

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2

Hayes, Christina Savannah Maria. "Generic properties of the infinite population genetic algorithm." Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/hayes/HayesC0806.pdf.

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3

Liakhovitch, Evgueni. "Genetic algorithm using restricted sequence alignments." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172598174.

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4

Bentley, Peter John. "Generic evolutionary design of solid objects using a genetic algorithm." Thesis, University of Huddersfield, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338599.

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This thesis investigates the novel idea of using a computer to create and optimise conceptual designs of a range of differently-shaped three-dimensional solid objects from scratch. An extensive literature review evaluates all related areas of research and reveals that no such system exists. The development of a generic evolutionary design system, using a genetic algorithm (GA) as its core, is then presented. The thesis describes a number of significant advances necessitated by the development of this system. Firstly, a new low-parameter spatial-partitioning representation of solid objects is introduced, which allows a wide range of solid objects to be appropriately defined and easily manipulated by a GA. Secondly, multiobjective optimisation is investigated to allow users to define design problems without fine-tuning large numbers of weights. As a result of this, the new concepts of acceptability, range-independence and importance are introduced and a new multiobjective ranking method is identified as being most appropriate. Thirdly, variable-length chromosomes in GAs are addressed, to allow the number of primitive shapes that define a design to be variable. This problem is overcome by the use of a new hierarchical crossover operator, which uses the new concept of a semantic hierarchy to reference chromosomes. Additionally, the thesis describes how the performance of the GA is improved by using an explicit mapping stage between genotypes and phenotypes, steady-state reproduction with preferential selection, and a new lifespan limiter. A library of modular evaluation software is also presented, which allows a user to define new design problems quickly and easily by picking combinations of modules to guide the evolution of designs. Finally, the feasibility of the generic evolutionary design of solid objects is demonstrated by presenting the successful evolution of both conventional and unconventional designs for fifteen different solid-object design tasks, e.g. tables, heatsinks, penta-prisms, boat hulls, aerodynamic cars.
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5

Chohan, Ossam. "University Scheduling using Genetic Algorithm." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3791.

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The automated timetabling and scheduling is one of the hardest problem areas. This isbecause of constraints and satisfying those constraints to get the feasible and optimizedschedule, and it is already proved as an NP Complete (1) [1]. The basic idea behind this studyis to investigate the performance of Genetic Algorithm on general scheduling problem underpredefined constraints and check the validity of results, and then having comparative analysiswith other available approaches like Tabu search, simulated annealing, direct and indirectheuristics [2] and expert system. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems and later analysis will prove this argument. The programis written in C++ and analysis is done by using variation in various parameters.
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6

Murugan, Anandaraj Soundarya Raja. "University Timetabling using Genetic Algorithm." Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3792.

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The field of automated timetabling and scheduling meeting all the requirementsthat we call constraints is always difficult task and already proved as NPComplete. The idea behind my research is to implement Genetic Algorithm ongeneral scheduling problem under predefined constraints and check the validityof results, and then I will explain the possible usage of other approaches likeexpert systems, direct heuristics, network flows, simulated annealing and someother approaches. It is observed that Genetic Algorithm is good solutiontechnique for solving such problems. The program written in C++ and analysisis done with using various tools explained in details later.
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7

Wen, Mengtao. "Optical design by genetic algorithm." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27080.

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The design of holographic diffusers and birefringent filters by using the genetic algorithm is studied in this thesis. A holographic diffuser is an optical device that distributes light from a light source with a desired spatial distribution pattern. For diffuse infrared wireless home networking, a holographic diffuser is used to diffuse the laser light to cover a large indoor area such as an office, and at the same time to solve the eye safety problem. In the thesis, a modified genetic algorithm is proposed to design holographic diffuser. The novel algorithm combines the conventional genetic algorithm and the simulated annealing algorithm, in which the simulated annealing algorithm is used to maintain a better diversity of chromosomes for the genetic algorithm. A better performance in locating the global minimum is demonstrated. A 4-level phase-only hologram that is designed by the modified genetic algorithm is fabricated on a quartz substrate. The fabricated hologram is experimentally verified. The results show that diffraction pattern generated by the fabricated hologram agrees well with the theoretically calculated pattern. (Abstract shortened by UMI.)
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8

Cai, Zesi. "Genetic Algorithm for Integrated SoftwarePipelining." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-76088.

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The purpose of the thesis was to study the feasibility of using geneticalgorithm (GA) to do the integrated software pipelining (ISP). Different from phasedcode generation, ISP is a technique which integrates instruction selection, instructionscheduling, and register allocation together when doing code generation. ISP is able toprovide a lager solution space than phased way does, which means that ISP haspotential to generate more optimized code than phased code generation. However,integrated compiling costs more than phased compiling. GA is stochastic beam searchalgorithm which can accelerate the solution searching and find an optimized result.An experiment was designed for verifying feasibility of implementing GA for ISP(GASP). The implemented algorithm analyzed data dependency graphs of loop bodies,created genes for the graphs and evolved, generated schedules, calculated andevaluated fitness, and obtained optimized codes. The fitness calculation wasimplemented by calculating the maximum value between the smallest possibleresource initiation interval and the smallest possible recurrence initiation interval. Theexperiment was conducted by generating codes from data dependency graphsprovided in FFMPEG and comparing the performance between GASP and integerlinear programming (ILP). The results showed that out of eleven cases that ILP hadgenerated code, GASP performed close to ILP in seven cases. In all twelve cases thatILP did not have result, GASP did generate optimized code. To conclude, the studyindicated that GA was feasible of being implemented for ISP. The generated codesfrom GASP performed similar with the codes from ILP. And for the dependencygraphs that ILP could not solve in a limited time, GASP could also generate optimizedresults.
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9

Haroun, Paul. "Genetic algorithm and data visualization." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0017/MQ37125.pdf.

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10

Edelstein, Jeffrey. "Truckin' : the genetic algorithm way." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ59320.pdf.

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11

Law, Nga Lam. "Parameter-free adaptive genetic algorithm /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202007%20LAW.

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12

Vin, Emmanuelle. "Genetic algorithm applied to generalized cell formation problems." Doctoral thesis, Universite Libre de Bruxelles, 2010. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210160.

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The objective of the cellular manufacturing is to simplify the management of the

manufacturing industries. In regrouping the production of different parts into clusters,

the management of the manufacturing is reduced to manage different small

entities. One of the most important problems in the cellular manufacturing is the

design of these entities called cells. These cells represent a cluster of machines that

can be dedicated to the production of one or several parts. The ideal design of a

cellular manufacturing is to make these cells totally independent from one another,

i.e. that each part is dedicated to only one cell (i.e. if it can be achieved completely

inside this cell). The reality is a little more complex. Once the cells are created,

there exists still some traffic between them. This traffic corresponds to a transfer of

a part between two machines belonging to different cells. The final objective is to

reduce this traffic between the cells (called inter-cellular traffic).

Different methods exist to produce these cells and dedicated them to parts. To

create independent cells, the choice can be done between different ways to produce

each part. Two interdependent problems must be solved:

• the allocation of each operation on a machine: each part is defined by one or

several sequences of operations and each of them can be achieved by a set of

machines. A final sequence of machines must be chosen to produce each part.

• the grouping of each machine in cells producing traffic inside and outside the

cells.

In function of the solution to the first problem, different clusters will be created to

minimise the inter-cellular traffic.

In this thesis, an original method based on the grouping genetic algorithm (Gga)

is proposed to solve simultaneously these two interdependent problems. The efficiency

of the method is highlighted compared to the methods based on two integrated algorithms

or heuristics. Indeed, to form these cells of machines with the allocation

of operations on the machines, the used methods permitting to solve large scale

problems are generally composed by two nested algorithms. The main one calls the

secondary one to complete the first part of the solution. The application domain goes

beyond the manufacturing industry and can for example be applied to the design of

the electronic systems as explained in the future research.


Doctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished

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13

Gliesch, Alex Zoch. "A genetic algorithm for fair land allocation." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2018. http://hdl.handle.net/10183/174950.

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O objetivo de projetos de reforma agrária é redistribuir terras de grandes latifúndios para terrenos menores, com destino à agricultura familiar. Um dos principais problemas do Instituto Nacional de Colonização e Reforma Agrária (INCRA) é subdividir uma parcela grande de terra em lotes menores que são balanceados com relação a certos atributos. Este problema é difícil por que precisa considerar diversas restrições legais e éticas. As soluções atuais são auxiliadas por computador, mas manuais, demoradas e suscetíveis a erros, tipicamente produzindo lotes retangulares de áreas similares mas que são injustos com relação a critérios como aptidão do solo ou acesso a recursos hidrográficos. Nesta dissertação, nós propomos um algoritmo genético para gerar subdivisões justas de forma automática. Nós apresentamos um algoritmo construtivo guloso randomizado baseado em locação-alocação para gerar soluções iniciais, assim como operadores de mutação e recombinação que consideram especificidades do problema. Experimentos com 5 instâncias reais e 25 instâncias geradas artificialmente confirmam a efetividade dos diferentes componentes do método proposto, e mostram que ele gera soluções mais balanceadas que as atualmente usadas na prática.
The goal of agrarian reform projects is the redistribution of farmland from large latifundia to smaller, often family farmers. One of the main problems the Brazilian National Institute of Colonization and Agrarian Reform (INCRA) has to solve is to subdivide a large parcel of land into smaller lots that are balanced with respect to certain attributes. This problem is difficult since it considers several constraints originating from legislation as well as ethical considerations. Current solutions are computer-assisted, but manual, time-consuming and error-prone, leading to rectangular lots of similar areas which are unfair with respect to soil aptitude and access to hydric resources. In this thesis, we propose a genetic algorithm to produce fair land subdivisions automatically. We present a greedy randomized constructive heuristic based on location-allocation to generate initial solutions, as well as mutation and recombination operators that consider specifics of the problem. Experiments on 5 real-world and 25 artificial instances confirm the effectiveness of the different components of our method, and show that it leads to fairer solutions than those currently applied in practice.
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14

Maciel, Cristiano Baptista Faria. "A memetic algorithm for logistics network design problems." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/8601.

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Mestrado em Decisão Económica e Empresarial
Neste trabalho, um algoritmo memético é desenvolvido com o intuito de ser aplicado a uma rede logística, com três níveis, múltiplos períodos, seleção do meio de transporte e com recurso a outsourcing. O algoritmo memético pode ser aplicado a uma rede logística existente, no sentido de otimizar a sua configuração ou, se necessário, pode ser utilizado para criar uma rede logística de raiz. A produção pode ser internalizada e é permitido o envio direto de produtos para os clientes. Neste problema, as capacidades das diferentes infraestruturas podem ser expandidas ao longo do período temporal. Caso se trate uma infraestrutura já existente, após uma expansão, já não pode ser encerrada. Sempre que se abre uma nova infraestrutura, a mesma também não pode ser encerrada. A heurística é capaz de determinar o número e localizações das infraestrutura a operar, as capacidades e o fluxo de mercadoria na rede logística.
This thesis describes a memetic algorithm applied to the design of a three-echelon logistics network over multiple periods with transportation mode selection and outsourcing. The memetic algorithm can be applied to an existing supply chain in order to obtain an optimized configuration or, if required, it can be used to define a new logistics network. In addition, production can be outsourced and direct shipments of products to customer zones are possible. In this problem, the capacity of an existing or new facility can be expanded over the time horizon. In this case, the facility cannot be closed. Existing facilities, once closed, cannot be reopened. New facilities cannot be closed, once opened. The heuristic is able to determine the number and locations of facilities (i.e. plants and warehouses), capacity levels as well as the flow of products throughout the supply chain.
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15

Taskinoglu, Evren Eyup. "A Genetic Algorithm For Structural Optimization." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/2/12607958/index.pdf.

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In this study, a design procedure incorporating a genetic algorithm (GA) is developed for optimization of structures. The objective function considered is the total weight of the structure. The objective function is minimized subjected to displacement and strength requirements. In order to evaluate the design constraints, finite element analysis are performed either by using conventional finite element solvers (i.e. MSC/NASTRAN®
) or by using in-house codes. The application of the algorithm is shown by a number of design examples. Several strategies for reproduction, mutation and crossover are tested. Several conclusions drawn from the research results are presented.
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16

Sen, Caner. "Tsunami Source Inversion Using Genetic Algorithm." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612939/index.pdf.

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Tsunami forecasting methodology developed by the United States National Oceanic and Atmospheric Administration&rsquo
s Center for Tsunami Research is based on the concept of a pre-computed tsunami database which includes tsunami model results from Mw 7.5 earthquakes called tsunami source functions. Tsunami source functions are placed along the subduction zones of the oceans of the world in several rows. Linearity of tsunami propagation in an open ocean allows scaling and/or combination of the pre-computed tsunami source functions. An offshore scenario is obtained through inverting scaled and/or combined tsunami source functions against Deep-ocean Assessment and Reporting of Tsunami (DART) buoy measurements. A graphical user interface called Genetic Algorithm for INversion (GAIN) was developed in MATLAB using general optimization toolbox to perform an inversion. The 15 November 2006 Kuril and 27 February 2010 Chile tsunamis are chosen as case studies. One and/or several DART buoy measurement(s) is/are used to test different error minimization functions with/without earthquake magnitude as constraint. The inversion results are discussed comparing the forecasting model results with the tide gage measurements.
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17

Ma, Jiya. "A Genetic Algorithm for Solar Boat." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.

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Genetic algorithm has been widely used in different areas of optimization problems. Ithas been combined with renewable energy domain, photovoltaic system, in this thesis.To participate and win the solar boat race, a control program is needed and C++ hasbeen chosen for programming. To implement the program, the mathematic model hasbeen built. Besides, the approaches to calculate the boundaries related to conditionhave been explained. Afterward, the processing of the prediction and real time controlfunction are offered. The program has been simulated and the results proved thatgenetic algorithm is helpful to get the good results but it does not improve the resultstoo much since the particularity of the solar driven boat project such as the limitationof energy production
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18

Williams, Tom. "A genetic algorithm test bed implementation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0017/MQ52677.pdf.

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19

West, Kent. "Mechanical design using the Genetic Algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ60513.pdf.

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20

WANG, MIN. "Description and Application of Genetic Algorithm." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2362.

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Genetic Algorithm (GA) as a class of Evolutionary Algorithm (EA) is a search algorithm based on the mechanics of natural selection and natural genetics. This dissertation presents the description, solving procedures and application of GA. The definitions of selection, crossover and mutation operators are given in details and an application based on GA in Time Table Problem (TTP) is performed in a new way. Due to its high capability of overall search, GA is particularly appropriate for solving timetabling and scheduling problems. TTP (Time Table Problem) which belongs to NP-hard problem is a special problem concerning resource management. In this dissertation, a new chromosome coding is designed in order to solve TTP more effectively. And the result presented by MATLAB will converge to a steady condition.
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21

Yan, Kai. "Genetic algorithm assisted CDMA multiuser detection." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343015.

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22

Johnson, Maury E. "Planning Genetic Algorithm: Pursuing Meta-knowledge." NSUWorks, 1999. http://nsuworks.nova.edu/gscis_etd/611.

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This study focuses on improving business planning by proposing a series of artificial intelligence techniques to facilitate the integration of decision support systems and expert system paradigms. The continued evolution of the national information infrastructure, open systems interconnectivity, and electronic data interchange lends toward the future plausibility of the inclusion of a back-end genetic algorithm approach. By using a back-end genetic algorithm, meta-planning knowledge could be collected, extended to external data sources, and utilized to improve business decision making.
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23

El-Nainay, Mustafa Y. "Island Genetic Algorithm-based Cognitive Networks." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28297.

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The heterogeneity and complexity of modern communication networks demands coupling network nodes with intelligence to perceive and adapt to different network conditions autonomously. Cognitive Networking is an emerging networking research area that aims to achieve this goal by applying distributed reasoning and learning across the protocol stack and throughout the network. Various cognitive node and cognitive network architectures with different levels of maturity have been proposed in the literature. All of them adopt the idea of coupling network devices with sensors to sense network conditions, artificial intelligence algorithms to solve problems, and a reconfigurable platform to apply solutions. However, little further research has investigated suitable reasoning and learning algorithms. In this dissertation, we take cognitive network research a step further by investigating the reasoning component of cognitive networks. In a deviation from previous suggestions, we suggest the use of a single flexible distributed reasoning algorithm for cognitive networks. We first propose an architecture for a cognitive node in a cognitive network that is general enough to apply to future networking challenges. We then introduce and justify our choice of the island genetic algorithm (iGA) as the distributed reasoning algorithm. Having introduced our cognitive node architecture, we then focus on the applicability of the island genetic algorithm as a single reasoning algorithm for cognitive networks. Our approach is to apply the island genetic algorithm to different single and cross layer communication and networking problems and to evaluate its performance through simulation. A proof of concept cognitive network is implemented to understand the implementation challenges and assess the island genetic algorithm performance in a real network environment. We apply the island genetic algorithm to three problems: channel allocation, joint power and channel allocation, and flow routing. The channel allocation problem is a major challenge for dynamic spectrum access which, in turn, has been the focal application for cognitive radios and cognitive networks. The other problems are examples of hard cross layer problems. We first apply the standard island genetic algorithm to a channel allocation problem formulated for the dynamic spectrum cognitive network environment. We also describe the details for implementing a cognitive network prototype using the universal software radio peripheral integrated with our extended implementation of the GNU radio software package and our island genetic algorithm implementation for the dynamic spectrum channel allocation problem. We then develop a localized variation of the island genetic algorithm, denoted LiGA, that allows the standard island genetic algorithm to scale and apply it to the joint power and channel allocation problem. In this context, we also investigate the importance of power control for cognitive networks and study the effect of non-cooperative behavior on the performance of the LiGA. The localized variation of the island genetic algorithm, LiGA, is powerful in solving node-centric problems and problems that requires only limited knowledge about network status. However, not every communication and networking problems can be solved efficiently in localized fashion. Thus, we propose a generalized version of the LiGA, namely the K-hop island genetic algorithm, as our final distributed reasoning algorithm proposal for cognitive networks. The K-hop island genetic algorithm is a promising algorithm to solve a large class of communication and networking problems with controllable cooperation and migration scope that allows for a tradeoff between performance and cost. We apply it to a flow routing problem that includes both power control and channel allocation. For all problems simulation results are provided to quantify the performance of the island genetic algorithm variation. In most cases, simulation and experimental results reveal promising performance for the island genetic algorithm. We conclude our work with a discussion of the shortcomings of island genetic algorithms without guidance from a learning mechanism and propose the incorporation of two learning processes into the cognitive node architecture to solve slow convergence and manual configuration problems. We suggest the cultural algorithm framework and reinforcement learning techniques as candidate leaning techniques for implementing the learning processes. However, further investigation and implementation is left as future work.
Ph. D.
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24

Perumalla, Anvesh Kumar. "A Genetic Algorithm for ASIC Floorplanning." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1484236480221006.

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Norgren, Eric, and Johan Jonasson. "Investigating a Genetic Algorithm-Simulated Annealing Hybrid Applied to University Course Timetabling Problem : A Comparative Study Between Simulated Annealing Initialized with Genetic Algorithm, Genetic Algorithm and Simulated Annealing." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186364.

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Every semester universities around the world have to create new schedules. This task can be very complex considering that a number of constraints has to be taken into account, e.g. there should not exist any timetable clashes for students and a room cannot be double-booked. This can be very hard and time-consuming for a human to do by hand, which is why methods to automate this problem, the University Course Timetabling Problem, has been researched for many years. This report investigates the performance of a hybrid consisting of Genetic Algorithm and Simulated Annealing when solving the University Course Timetabling Problem. An implementation by Yamazaki & Pertoft (2014) was used for the Genetic Algorithm. Simulated Annealing used the Genetic Algorithm as base for its implementation. The hybrid runs the Genetic Algorithm until some breakpoint, takes the best timetable and uses it as an initial solution for the Simulated Annealing. Our results show that our implementation of Simulated Annealing performs better than the hybrid and magnitudes better than the Genetic Algorithm. We believe one reason for this is that the dataset used was too simple, the Genetic Algorithm might scale better as the complexity of the dataset increases.
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Zhou, Yao. "Study on genetic algorithm improvement and application." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-211907/.

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Hincal, Onur. "Optimization Of Multireservoir Systems By Genetic Algorithm." Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609261/index.pdf.

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Application of optimization techniques for determining the optimal operating policy for reservoirs is a major title in water resources planning and management. Genetic algorithms, ruled by evolution techniques, have become popular for solving optimization problems in diversified fields of science. The main aim of this research was to explore the efficiency and effectiveness of the applicability of genetic algorithm in optimization of multi-reservoirs. A computer code has been constructed for this purpose and verified by means of a reference problem with a known global optimum. Three reservoirs in the Colorado River Storage Project were optimized for maximization of energy production. Besides, a real-time approach utilizing a blend of online and a posteriori data was proposed. The results achieved were compared to the real operational data and genetic algorithms were found to be effective, competitive and can be utilized as an alternative technique to other traditional optimization techniques.
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Chen, Weihang. "A Genetic Algorithm For 2d Shape Optimization." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609914/index.pdf.

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In this study, an optimization code has been developed based on genetic algorithms associated with the finite element modeling for the shape optimization of plane stress problems. In genetic algorithms, constraints are mostly handled by using the concept of penalty functions, which penalize infeasible solutions by reducing their fitness values in proportion to the degrees of constraint violation. In this study, An Improved GA Penalty Scheme is used. The proposed method gives information about unfeasible individual fitness as near as possible to the feasible region in the evaluation function. The objective function in this study is the area of the structure. The area is minimized considering the Von-Misses stress criteria. In order to minimize the objective function, one-point crossover with roulette-wheel selection approach is used. Optimum dimensions of four problems available in the literature have been solved by the code developed . The algorithm is tested using several strategies such as
different initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.
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Morelli, Jordan E. "Distribution loss reduction, a genetic algorithm approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0014/MQ52615.pdf.

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Cattral, Robert. "RAGA, Rule Acquisition with a Genetic Algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ57758.pdf.

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31

Kulankara, Krishnakumar. "Machining fixture synthesis using the genetic algorithm." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/16491.

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Yeung, Ka Yiu. "Fixture Layout Optimisation Based on Genetic Algorithm." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523216.

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33

Wu, Xiang. "Application of genetic algorithm to wireless communications." Thesis, University of Newcastle Upon Tyne, 2004. http://hdl.handle.net/10443/668.

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Wireless communication is one of the most active areas of technology development of our time. Like all engineering endeavours, the subject of the wireless communication also brings with it a whole host of complex design issues, concerning network design, signal detection, interference cancellation, and resource allocation, to name a few. Many of these problems have little knowledge of the solution space or have very large search space, which are known as non-deterministic polynomial (NP) -hard or - complete and therefore intractable to solution using analytical approaches. Consequently, varied heuristic methods attempts have been made to solve them ranging from simple deterministic algorithms to complicated random-search methods. Genetic alcyorithm (GA) is an adaptive heuristic search algorithm premised on the evolutionary ideas of evolution and natural selection, which has been successfully applied to a variety of complicated problems arising from physics, engineering, biology, economy or sociology. Due to its outstanding search strength and high designable components, GA has attracted great interests even in the wireless domain. This dissertation is devoted to the application of GA to solve various difficult problems spotlighted from the wireless systems. These problems have been mathematically formulated in the constrained optimisation context, and the main work has been focused on developing the problem-specific GA approaches, which incorporate many modifications to the traditional GA in order to obtain enhanced performance. Comparative results lead to the conclusion that the proposed GA approaches are generally able to obtain the optimal or near-optimal solutions to the considered optimisation problems provided that the appropriate representation, suitable fitness function, and problem-specific operators are utilised. As a whole, the present work is largely original and should be of great interest to the design of practical GA approaches to solve realistic problems in the wireless communications systems.
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34

Abraham, Nathan Luke. "A genetic algorithm for crystal structure prediction." Thesis, University of York, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444727.

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35

Chapman, Colin Donald. "Structural topology optimization via the genetic algorithm." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35410.

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36

Shames, Samuel W. L. (Samuel William Linder). "Modeling trabecular microstructure evolution via genetic algorithm." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/89981.

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Thesis: S.B., Massachusetts Institute of Technology, Department of Materials Science and Engineering, June 2014.
Cataloged from PDF version of thesis. "May 2013."
Includes bibliographical references (pages 86-87).
Connecting structure to properties, and optimizing properties by controlling structure is one of the fundamental goals of materials science and engineering. No where is this connection more apparent than with biomaterials, whose unparalleled properties are the result of the evolution via cumulative selection of highly specialized structures. Beyond biomaterials, cumulative selection offers a generalizable model for materials optimization via accumulative of beneficial mutations in a material's genome that improve the properties for a given function. A genetic algorithm is one method for applying the principals of cumulative selection to material's optimization. One of unique property that cumulative selection generated was the ability of trabecular bone to optimize and adjust its structure in vivo in response to changes in its loading conditions. This work presents a model for trabecular microstructure evolution using a genetic algorithm, the same mechanism through which that ability evolved. The algorithm begins by translating a trabecular genome into a developed structure. It then simulates the structure's response under an applied load and selects for the genome which translates into the best structure. The selected genome is then replicated and mutated. Simulations of microstructure evolution consist of iterating through this process across multiple generations. A series of simulations was conducted demonstrating the ability of the algorithm to improve trabecular architecture. The systems tended to converge to a uniform stress distribution, after which additional generations of evolution had no effect on performance. During the simulations it was found that the length of the computation was most sensitive to the number of offspring per generation. Although focused on trabecular microstructure, this work establishes the use of a genetic algorithm as a general tool for material's optimization.
by Samuel W. L. Shames.
S.B.
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37

Wall, Matthew Bartschi. "A genetic algorithm for resource-constrained scheduling." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10259.

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38

Kang, Hong-ming, and 康宏銘. "Comparisons between Hybrid Taguchi-Genetic Algorithm and Traditional Genetic Algorithm." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/99259342073803393917.

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碩士
國立臺南大學
數位學習科技學系碩士班
99
Hybrid Taguchi genetic algorithm can be used to solve the global continuous optimization problems. Aside from the global search capability of traditional genetic algorithm, it further combines Taguchi experimental method to explore the optimal feasibility of the offspring. Taguchi method is inserted between the crossover and mutation operations of the traditional genetic algorithm. Hybrid Taguchi genetic algorithm also seems to outperform the traditional genetic method in obtaining the optional or near optimal solutions because of its fast convergence ability and robustness. Although the hybrid Taguchi genetic algorithm is more powerful than the traditional genetic one in the optimization of global continuous function, yet it still needs further investigation to conclude if it also offers better solution than the latter to the optimization of global discrete function. Therefore, this study tries to compare the two algorithms in each individual’s performance in the optimization of global discrete function. It aims to figure out whether the hybrid Taguchi genetic algorithm is better than traditional genetic algorithm or not.
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39

Su, Yong-Tian, and 蘇永田. "Fuzzy Genetic Algorithm Controller." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/15555172366609616091.

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碩士
中華科技大學
電子工程研究所碩士班
101
Genetic algorithm is an optimization tool based on natural evolution. According to Darwin's theory of natural selection, species can adapt to the environment, the higher the chances of survival. The basic spirit is to follow the example of natural selection in biological community, survival of the fittest the natural laws of evolution. The genetic algorithm does not like some typical approach, It does not have a specific mathematical formula. This thesis uses general fuzzy controller as a basic framework, it joined the forced evolution method, hoping to enhance the effectiveness of genetic fuzzy controller and learning ability. This thesis uses Matlab software in simulation test, it can really achieve the expected results.
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40

yin, wang jei, and 王瑞吟. "An improved Genetic Algorithm Using the Entropy-based Genetic Algorithm for Soving TSP." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/11118269205378424089.

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碩士
國立高雄師範大學
數學系
88
英文摘要 Abstract The goal of this paper is to propose an improved genetic algorithm for solving TSP . The traveling salesman problem (TSP) is defined as following .There are n cities and a salesman who has to visits starting from a certain city exactly once each city . There are four main points in this paper . Including gather data ; propose an improved genetic algorithm ; try the algorithm by using computer and suggest new direction for further studies . Conclusions as following: First : There are many methods to find the shortest tour of TSP ,for example : Hopfiled-Tank Network , Neural Network….. Second :Using an improved genetic algorithm can get the shortest tour than traditional genetiv algorithm and also has a lot of improved space . Third : Using 20-cities and 50-cities . Last : Suggest new direction for further studies .
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陳政光. "Genetic Algorithm Based Dynamic Scheduling Algorithms in Grid Computing Environment." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/69746793633150342516.

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碩士
中華大學
資訊工程學系(所)
96
Grid computing can integrate computational resources from different networks or regional areas into a high performance computational platform. With the use of this high performance platform, complex computing-intensive problems can be solved efficiently. Scheduling problem is an important issue in a grid computing environment. Because of the differences in computational capabilities and network status of computational resources, an efficient scheduling algorithm is necessary to assign jobs to the appropriate computing nodes. In this thesis, we propose two dynamic scheduling algorithms GDSA and EDSA for scheduling tasks in grid computing environment. The proposed algorithms use the optimal-searching technique of genetic algorithm (GA) to get an efficient scheduling solution in grid computing environment and adapt to different number of computing nodes which have different computational capabilities. And, two types of chromosomes were used to discuss the effect on performance. Furthermore, the hybrid crossover and incremental mutation operations within the EDSA algorithm can move the solution away from the local-optimal solution towards a near-optimal solution. In order to verify the performance of the algorithms, a simulation with randomly generated task sets was performed, and they were then compared with five other scheduling algorithms. The simulation results show that the use of GA can effectively evolve a better schedule than other conventional scheduling algorithms. Especially, the proposed EDSA outperformed among all other scheduling algorithms across a range of scenarios.
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42

Yu, Shang-Hsueh, and 余尚學. "Relative seismic travel time determined by the genetic algorithm and the micro genetic algorithm." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/36868389321095681816.

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碩士
臺灣大學
海洋研究所
95
Abstract Seismic relative arrival times among a local network provide important information for the regional velocity structure. To map the precise velocity structure, we need to find precise relative arrival times. In the past, determining seismic relative arrival times is posed as an overdetermined and linear inverse problem to find an approximate solution. In our method, it is formulated in terms of a non-linear problem and is dealt with using the genetic algorithms. We experiment with data from IndepthIII and an Ocean Bottom Seismometer array. We compare the efficiency between the genetic algorithm and the micro-genetic algorithm. It is shown that after proper normalization、filter and alignment. GA is very efficient in determining the relative arrival times.
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43

Khor, Susan Lay Choo. "A genetic algorithm test generator." Thesis, 2004. http://spectrum.library.concordia.ca/8112/1/MQ94745.pdf.

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Use of a genetic algorithm and formal concept analysis to generate test data for branch coverage is explored in a prototype automatic test generator (ATG) called genet . genet is unique in the sense that it requires minimal source code instrumentation and analysis, and is programming language independent. Besides the novelty of using formal concept analysis within a genetic algorithm, genet extends the opportunism of another evolutionary ATG. Experiments were designed to evaluate the effectiveness of genet and the importance of selection in the evolution of test data. The results of the experiments indicate genet is most effective when selection plays a significant role. This is the case when test solutions for a program are necessarily organized. When it is not necessary for test solutions to resemble each other, adaptation appears to be the more dominant factor and the identification of suitable genetic operators becomes more important. Nevertheless, even in the latter situation, the presence of genet accelerated the evolutionary process for our test programs. Notwithstanding equal adaptation instructions, genetics mattered.
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44

Chou, Hung-Ching, and 周宏磬. "Wireless Broadcast using Genetic Algorithm." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/09824683901610937939.

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碩士
南華大學
資訊管理學系碩士班
90
In mobile distributed systems the data on air can be accessed by a large number of clients. We define and analyze the problem of wireless data scheduling. We use a genetic algorithm (GA) to solve problem that wireless data scheduling. To search the best resolution, and total access time is the shortest. We also evaluate the performance of GA by experiments.
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45

"An adaptive parallel genetic algorithm." 2000. http://library.cuhk.edu.hk/record=b5890403.

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Chi Wai Ho, Raymond.
Thesis submitted in: December 1999.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.
Includes bibliographical references (leaves 93-97).
Abstracts in English and Chinese.
Chapter Chapter 1 --- Introduction --- p.7
Chapter 1.1 --- Thesis Outline --- p.10
Chapter 1.2 --- Contribution at a Glance --- p.11
Chapter Chapter 2 --- Background Concept and Related Work --- p.14
Chapter 2.1 --- Genetic Algorithms (GAs) --- p.14
Chapter 2.2 --- The Nature of GAs --- p.16
Chapter 2.3 --- The Role of Mutation --- p.17
Chapter 2.4 --- The Role of Crossover --- p.18
Chapter 2.5 --- The Roles of the Mutation and Crossover Rates --- p.19
Chapter 2.6 --- Adaptation of the Mutation and Crossover Rates --- p.19
Chapter 2.7 --- Diversity Control --- p.21
Chapter 2.8 --- Coarse-grain Parallel Genetic Algorithms --- p.25
Chapter 2.9 --- Adaptation of Migration Period --- p.26
Chapter 2.10 --- Serial and Parallel GAs --- p.27
Chapter 2.11 --- Distributed Java Machine (DJM) --- p.28
Chapter 2.12 --- Clustering --- p.30
Chapter Chapter 3 --- Adaptation of the Mutation and Crossover Rates --- p.35
Chapter 3.1 --- The Probabilistic Rule-based Adaptive Model (PRAM) --- p.35
Chapter 3.2 --- Time Complexity --- p.37
Chapter 3.3 --- Storage Complexity --- p.38
Chapter Chapter 4 --- Diversity Control --- p.39
Chapter 4.1 --- Repelling --- p.39
Chapter 4.2 --- Implementation --- p.42
Chapter 4.3 --- Lazy Repelling --- p.43
Chapter 4.4 --- Repelling and Lazy Repelling with Deterministic Crowding --- p.43
Chapter 4.5 --- Comparison of Repelling and Lazy Repelling with Recent Diversity Maintenance Models in Time Complexity --- p.44
Chapter Chapter 5 --- An Adaptive Parallel Genetic Algorithm --- p.46
Chapter 5.1 --- A Steady-State Genetic Algorithm --- p.46
Chapter 5.2 --- An Adaptive Parallel Genetic Algorithm (aPGA) --- p.47
Chapter 5.3 --- An Adaptive Parallel Genetic Algorithm for Clustering --- p.48
Chapter 5.4 --- Implementation --- p.48
Chapter 5.5 --- Time Complexity --- p.51
Chapter Chapter 6 --- Performance Evaluation of PRAM --- p.52
Chapter 6.1 --- Solution Quality --- p.58
Chapter 6.2 --- Efficiency --- p.60
Chapter 6.3 --- Discussion --- p.62
Chapter Chapter 7 --- Performance Evaluation of Repelling --- p.66
Chapter 7.1 --- Performance Comparison of Repelling and Lazy Repelling with Deterministic Crowding --- p.70
Chapter 7.2 --- Performance Comparison with Recent Diversity Maintenance Models --- p.73
Chapter 7.3 --- Performance Comparison with Serial and Parallel Gas --- p.75
Chapter Chapter 8 --- Performance Evaluation of aPGA --- p.78
Chapter 8.1 --- Scalability of Different Dimensionalities --- p.78
Chapter 8.2 --- Speedup of Schwefel's function --- p.83
Chapter 8.3 --- Solution Quality of Clustering Problems --- p.87
Chapter 8.4 --- Speedup of The Clustering Problem --- p.89
Chapter Chapter 9 --- Conclusion --- p.91
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46

Haung, Ren-Ze, and 黃仁澤. "Genetic Algorithm on Maze Generator." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/er49up.

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碩士
國立東華大學
資訊工程學系
102
Maze is one of the popular puzzle games. However, maze generator is rare in academic-field. This thesis makes the maze generator based on Genetic Algorithm (one of the evolution algorithm). When the chromosome in the population is evolved for many generations, its fitness value will increase. While the fitness value is related to the difficulty of the maze, we will get more difficult mazes. Most maze generators are made on the properties of the size of the maze or the number of the route in the mazes. And its algorithm is usually Depth First Search (DFS). We use Genetic Algorithm to develop the maze generator. It can increase the fitness value through evolution with random feature in its operations. Thus, the maze generator is different from the traditional generators.
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47

李俊瑩. "Electoral Redistricting In Genetic Algorithm." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/14675172142372323133.

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碩士
國立政治大學
資訊科學學系
96
Electoral redistricting is normally required when election regulations changed. Traditionally, electoral redistricting is done manually. Though manual redistricting could consider humane or cultural factor, which may be very difficult to be included in the computation model, the cost of manual redistricting normally is high. In addition, manual redistricting may induce controversial issues. In this thesis, we propose a systematic way that could do the electoral redistricting automatically. Our major considerations are: (1) the population must be evenly partitioned, within an acceptable error; (2) the shape of the redistricted region is reasonably good; (3) the integrity of the second level district must be kept reasonably well. Our method consists of three major parts: initial district production, district’s integrity fixing, and district reshaping. The concept of potential is used in producing the initial districts. A heuristic is used in fixing the district’s integrity. And, finally, Genetic Algorithm is used in district reshaping. We use Taipei City as an example to illustrate our idea. Experimental results show that our method can do electoral redistricting effectively.
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48

Chen, Shih-Hsin, and 陳世興. "The Self-Guided Genetic Algorithm." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/04297165631705723544.

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博士
元智大學
工業工程與管理學系
96
This thesis proposed a Self-Guided genetic algorithm which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models (EAPM). Previous EAPM research explicitly used the probabilistic model from the parental distribution, and then generated solutions by sampling from the probabilistic model without using genetic operators whereas GA employs crossover and mutation operators. Although EAPM is promising in solving different kinds of problems, Self-Guided GA doesn''t intend to generate solution by the probabilistic model directly because the time-complexity is high when we solve difficult combinatorial problems, particularly the sequencing ones. In this research, the probabilistic model serves as a fitness surrogate which estimates the fitness of the new solution beforehand. So the probabilistic model is used to guide the evolutionary process of crossover and mutation. When it comes to local search, the fitness surrogate also can be applied in local search and it prunes some bad moves in advance. The hybridization of local search and probabilistic model is named Self-Guided Genetic Local Search. This research studied the single-objective scheduling problems, including the single machine and flowshop scheduling problems. From the experiment results, it shows that the Self-Guided GA outperform other algorithms significantly in terms of solution quality and computational time. Self-Guided Genetic Local Search evens works more efficiently than Genetic Algorithms. As a result, it could be a significant contribution in the branch of EAPM and is of interest for researchers in the field of evolutionary computation.
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Li-Wei, Chen. "A Two-staged Fuzzy Clustering Algorithm with Genetic Algorithm." 2001. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611304722.

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Chen, Li-Wei, and 陳立偉. "A Two-staged Fuzzy Clustering Algorithm with Genetic Algorithm." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/60765776016126554417.

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碩士
元智大學
資訊管理研究所
89
Cluster analysis is a kind of data mining techniques, and its goal is to find the hiding patterns from the unknown data. In related studies, most of them are focused on the improvement of the clustering results, and less on the efficiency of clustering. However, the efficiency of data analysis technique is getting increasingly important in practice, while the analysis of huge amount of data is needed (due to the influence of globalization and internet). In this paper, we propose a two-staged fuzzy clustering algorithm to meet the practical need. We test our algorithm based on the four parts (the maximum of Nc, the effectiveness of the initialization method, the quality of the clustering results, and the converging time of clustering) with four standard test data sets: Iris Plants Data, Balance Scale Weight & Distance Data, Wisconsin Breast Cancer Data, and Contraceptive Method Choice Data. According to the results of experiments in this study, our algorithm has improved not only the problem of local optimization of FCM algorithm, but also the efficiency of GA. Therefore, our proposed algorithm can provide faster clustering results with an acceptable quality of clustering for practical needs.
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