Tesis sobre el tema "Genetic algorithm"
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Harris, Steven C. "A genetic algorithm for robust simulation optimization". Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178645751.
Texto completoHayes, 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.
Texto completoLiakhovitch, Evgueni. "Genetic algorithm using restricted sequence alignments". Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172598174.
Texto completoBentley, 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.
Texto completoChohan, Ossam. "University Scheduling using Genetic Algorithm". Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3791.
Texto completoMurugan, Anandaraj Soundarya Raja. "University Timetabling using Genetic Algorithm". Thesis, Högskolan Dalarna, Datateknik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3792.
Texto completoWen, Mengtao. "Optical design by genetic algorithm". Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/27080.
Texto completoCai, 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.
Texto completoHaroun, 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.
Texto completoEdelstein, 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.
Texto completoLaw, Nga Lam. "Parameter-free adaptive genetic algorithm /". View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202007%20LAW.
Texto completoVin, 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.
Texto completomanufacturing 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
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.
Texto completoThe 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.
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.
Texto completoNeste 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.
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.
Texto completo) 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.
Sen, Caner. "Tsunami Source Inversion Using Genetic Algorithm". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612939/index.pdf.
Texto completos 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.
Ma, Jiya. "A Genetic Algorithm for Solar Boat". Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3488.
Texto completoWilliams, 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.
Texto completoWest, 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.
Texto completoWANG, 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.
Texto completoYan, Kai. "Genetic algorithm assisted CDMA multiuser detection". Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343015.
Texto completoJohnson, Maury E. "Planning Genetic Algorithm: Pursuing Meta-knowledge". NSUWorks, 1999. http://nsuworks.nova.edu/gscis_etd/611.
Texto completoEl-Nainay, Mustafa Y. "Island Genetic Algorithm-based Cognitive Networks". Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28297.
Texto completoPh. D.
Perumalla, Anvesh Kumar. "A Genetic Algorithm for ASIC Floorplanning". Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1484236480221006.
Texto completoNorgren, Eric y 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.
Texto completoZhou, Yao. "Study on genetic algorithm improvement and application". Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-211907/.
Texto completoHincal, Onur. "Optimization Of Multireservoir Systems By Genetic Algorithm". Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609261/index.pdf.
Texto completoChen, Weihang. "A Genetic Algorithm For 2d Shape Optimization". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609914/index.pdf.
Texto completodifferent initial population number, different probability of mutation and crossover. The results are compared with the ones in literature and conclusions are driven accordingly.
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.
Texto completoCattral, 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.
Texto completoKulankara, Krishnakumar. "Machining fixture synthesis using the genetic algorithm". Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/16491.
Texto completoYeung, 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.
Texto completoWu, Xiang. "Application of genetic algorithm to wireless communications". Thesis, University of Newcastle Upon Tyne, 2004. http://hdl.handle.net/10443/668.
Texto completoAbraham, 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.
Texto completoChapman, Colin Donald. "Structural topology optimization via the genetic algorithm". Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/35410.
Texto completoShames, 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.
Texto completoCataloged 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.
Wall, Matthew Bartschi. "A genetic algorithm for resource-constrained scheduling". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10259.
Texto completoKang, Hong-ming y 康宏銘. "Comparisons between Hybrid Taguchi-Genetic Algorithm and Traditional Genetic Algorithm". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/99259342073803393917.
Texto completo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.
Su, Yong-Tian y 蘇永田. "Fuzzy Genetic Algorithm Controller". Thesis, 2013. http://ndltd.ncl.edu.tw/handle/15555172366609616091.
Texto completo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.
yin, wang jei y 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.
Texto completo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 .
陳政光. "Genetic Algorithm Based Dynamic Scheduling Algorithms in Grid Computing Environment". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/69746793633150342516.
Texto completo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.
Yu, Shang-Hsueh y 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.
Texto completo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.
Khor, Susan Lay Choo. "A genetic algorithm test generator". Thesis, 2004. http://spectrum.library.concordia.ca/8112/1/MQ94745.pdf.
Texto completoChou, Hung-Ching y 周宏磬. "Wireless Broadcast using Genetic Algorithm". Thesis, 2002. http://ndltd.ncl.edu.tw/handle/09824683901610937939.
Texto completo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.
"An adaptive parallel genetic algorithm". 2000. http://library.cuhk.edu.hk/record=b5890403.
Texto completoThesis 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
Haung, Ren-Ze y 黃仁澤. "Genetic Algorithm on Maze Generator". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/er49up.
Texto completo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.
李俊瑩. "Electoral Redistricting In Genetic Algorithm". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/14675172142372323133.
Texto completo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.
Chen, Shih-Hsin y 陳世興. "The Self-Guided Genetic Algorithm". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/04297165631705723544.
Texto completo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.
Li-Wei, Chen. "A Two-staged Fuzzy Clustering Algorithm with Genetic Algorithm". 2001. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611304722.
Texto completoChen, Li-Wei y 陳立偉. "A Two-staged Fuzzy Clustering Algorithm with Genetic Algorithm". Thesis, 2001. http://ndltd.ncl.edu.tw/handle/60765776016126554417.
Texto completo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.