Tesi sul tema "Evolutionary computation applications"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Vedi i top-33 saggi (tesi di laurea o di dottorato) per l'attività di ricerca sul tema "Evolutionary computation applications".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Vedi le tesi di molte aree scientifiche e compila una bibliografia corretta.
Pridgeon, Carey. "Diverse applications of evolutionary computation in bioinformatics : hypermotifs and gene regulatory network inference". Thesis, University of Exeter, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.479210.
Testo completoAbraham, Ajith 1968. "Hybrid soft computing : architecture optimization and applications". Monash University, Gippsland School of Computing and Information Technology, 2002. http://arrow.monash.edu.au/hdl/1959.1/8676.
Testo completoRahman, Izaz Ur. "Novel particle swarm optimization algorithms with applications in power systems". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.
Testo completoKok, Jonathan. "Design methodologies and architectures of hardware-based evolutionary algorithms for aerospace optimisation applications on FPGAS". Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/72904/5/Jonathan_Kok_Thesis.pdf.
Testo completoRanjeet, Tirtha. "Coevolutionary algorithms for the optimization of strategies for red teaming applications". Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2012. https://ro.ecu.edu.au/theses/558.
Testo completoCreaser, Paul. "Application of evolutionary computation techniques to missile guidance". Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
Testo completoSharifi, Soroosh. "Application of evolutionary computation to open channel flow modelling". Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/478/.
Testo completoHayward, Kevin. "Application of evolutionary algorithms to engineering design". University of Western Australia. School of Mechanical Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0018.
Testo completoWiltshire, Serge William. "On The Application Of Computational Modeling To Complex Food Systems Issues". ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1077.
Testo completoAntão, Tiago Rodrigues. "Evolutionary applications of population genetics with a focus on malaria : a computational approach". Thesis, University of Liverpool, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.569901.
Testo completoCollins, Trevor. "The application of software visualization technology to evolutionary computation : a case study in Genetic Algorithms". Thesis, Open University, 1998. http://oro.open.ac.uk/28579/.
Testo completoFabritius, Björn. "Application of genetic algorithms to problems in computational fluid dynamics". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15236.
Testo completoLuitel, Bipul. "Applications of swarm, evolutionary and quantum algorithms in system identification and digital filter design". Diss., Rolla, Mo. : Missouri University of Science and Technology, 2009. http://scholarsmine.mst.edu/thesis/pdf/Luitel_09007dcc805cd792.pdf.
Testo completoVita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed January 22, 2009) Includes bibliographical references (p. 135-137).
Armstrong, Kathryn Anne. "Computational structure-based modeling and analysis with application to rational and evolutionary molecular engineering". Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39844.
Testo completoThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (leaves 112-127).
The design and development of new proteins and small molecules has considerable practical application in medicine, industry, and basic science. Frequently, progress in this area is made by altering an existing small molecule or protein for new function. This thesis presents methods for the analysis and design of rationally and evolutionarily designed molecules and focuses on applying these methods to make protein and small molecule changes more strategically. First, electrostatic analysis of a series of small molecule neuraminidase inhibitors was used to demonstrate that charge optimization improves the electrostatic component of the binding free energy, despite changes in binding mode and discrete chemical constraints. Additionally, chemical changes suggested by charge optimization frequently corresponded to tighter-binding inhibitors, indicating that this technique would be useful for the design of future inhibitors. Second, computational sequence and structure analysis were used to study the PDZ3-CRIPT binding interaction and a method for sequence analysis was developed to locate residues important for binding specificity. Third, computational analysis of the horseradish peroxidase active site suggested five positions as candidates for mutation, and further studies of new mutant enzymes let to ideas for the improvement of computational enzyme design procedures. Finally, both computational protein design techniques and a model of the evolutionary process were used to study the efficiency of evolution as a tool for creating new proteins in the laboratory. We identified sequences that serve as better evolutionary starting points that others and provide a general framework for considering the impact of protein structure on the allowed sequence space and therefore on the challenges that each protein presents to evolutionary protein engineering procedures.
by Kathryn Anne Armstrong.
Ph.D.
Morrison, Kevin S. "Topological Data Analysis and Applications to Influenza". Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595864809447239.
Testo completoZemzami, Maria. "Variations sur PSO : approches parallèles, jeux de voisinages et applications Application d’un modèle parallèle de la méthode PSO au problème de transport d’électricité A modified Particle Swarm Optimization algorithm linking dynamic neighborhood topology to parallel computation An evolutionary hybrid algorithm for complex optimization problems Interoperability optimization using a modified PSO algorithm A comparative study of three new parallel models based on the PSO algorithm Optimization in collaborative information systems for an enhanced interoperability network". Thesis, Normandie, 2019. http://www.theses.fr/2019NORMIR11.
Testo completoKnown for many years as a stochastic metaheuristic effective in the resolution of difficult optimization problems, the Particle Swarm Optimization (PSO) method, however, shows some drawbacks, the most studied: high running time and premature convergence. In this thesis we consider some variants of the PSO method to escape these two disadvantages. These variants combine two approaches: the parallelization of the calculation and the organization of appropriate neighborhoods for the particles. To prove the performance of the proposed models, we performed an experiment on a series of test functions. By analyzing the obtained experimental results, we observe that the proposed models based on the PSO algorithm performed much better than basic PSO in terms of computing time and solution quality. A model based on the PSO algorithm was selected and developed for an experiment on the problem of electricity transmission. A hybrid variant of this model with Simulated Annealing (SA) algorithm has been considered and tested on the problem of collaborative networks
Wijns, Christopher P. "Exploring conceptual geodynamic models : numerical method and application to tectonics and fluid flow". University of Western Australia. School of Earth and Geographical Sciences, 2005. http://theses.library.uwa.edu.au/adt-WU2005.0068.
Testo completoVera-Licona, Martha Paola. "Algorithms for modeling and simulation of biological systems; applications to gene regulatory networks". Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/28073.
Testo completoPh. D.
Gunes, Baydin Atilim. "Evolut ionary adap tat ion in cas e - bas ed reasoning. An application to cross-domain analogies for mediation". Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/129294.
Testo completoAnalogy plays a fundamental role in problem solving and it lies behind many processes central to human cognitive capacity, to the point that it has been considered "the core of cognition". Analogical reasoning functions through the process of transfer, the use of knowledge learned in one situation in another for which it was not targeted. The case-based reasoning (CBR) paradigm presents a highly related, but slightly different model of reasoning mainly used in artificial intelligence, different in part because analogical reasoning commonly focuses on cross-domain structural similarity whereas CBR is concerned with transfer of solutions between semantically similar cases within one specific domain. In this dissertation, we join these interrelated approaches from cognitive science, psychology, and artificial intelligence, in a CBR system where case retrieval and adaptation are accomplished by the Structure Mapping Engine (SME) and are supported by commonsense reasoning integrating information from several knowledge bases. For enabling this, we use a case representation structure that is based on semantic networks. This gives us a CBR model capable of recalling and adapting solutions from seemingly different, but structurally very similar domains, forming one of our contributions in this study. A traditional weakness of research on CBR systems has always been about adaptation, where most applications settle for a very simple "reuse" of the solution from the retrieved case, mostly through null adaptation or substitutional adaptation. The difficulty of adaptation is even more obvious for our case of cross-domain CBR using semantic networks. Solving this difficulty paves the way to another contribution of this dissertation, where we introduce a novel generative adaptation technique based on evolutionary computation that enables the spontaneous creation or modification of semantic networks according to the needs of CBR adaptation. For the evaluation of this work, we apply our CBR system to the problem of mediation, an important method in conflict resolution. The mediation problem is non-trivial and presents a very good real world example where we can spot structurally similar problems from domains seemingly as far as international relations, family disputes, and intellectual rights.
Wang, Chien-Tao, e 王建道. "The applications of evolutionary computation to the design of digital filter". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/34405488218469634016.
Testo completo樹德科技大學
資訊工程學系
94
In this thesis, two evolutionary computation algorithm, genetic algorithm (GA) and differential evolution (DE), are applied to the design of IIE filter. First, we use GA to design a Canonical Signed-Digit (CSD) code based IIR filter. It can be seen that, are to the special configuration of CSD code, GA is a better algorithm to evolve the CSD coded parameters. Second, an criterion for checking the stability of an IIR filter will be derived. The stability criterion is a base of designing stable IIR filter in this thesis. Finally, the two algorithms, GA and DE, are used to design the stable IIR filter according to the stability criterion derived in previous step. We will compare the efficiency of the two algorithms in de signing the stable IIR filter.
"Applications of evolutionary algorithms on biomedical systems". 2007. http://library.cuhk.edu.hk/record=b5893179.
Testo completoThesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 95-104).
Abstracts in English and Chinese.
Abstract --- p.i
Acknowledgement --- p.v
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Motivation --- p.1
Chapter 1.1.1 --- Basic Concepts and Definitions --- p.2
Chapter 1.2 --- Evolutionary Algorithms --- p.5
Chapter 1.2.1 --- Chromosome Encoding --- p.6
Chapter 1.2.2 --- Selection --- p.7
Chapter 1.2.3 --- Crossover --- p.9
Chapter 1.2.4 --- Mutation --- p.10
Chapter 1.2.5 --- Elitism --- p.11
Chapter 1.2.6 --- Niching --- p.11
Chapter 1.2.7 --- Population Manipulation --- p.13
Chapter 1.2.8 --- Building Blocks --- p.13
Chapter 1.2.9 --- Termination Conditions --- p.14
Chapter 1.2.10 --- Co-evolution --- p.14
Chapter 1.3 --- Local Search --- p.15
Chapter 1.4 --- Memetic Algorithms --- p.16
Chapter 1.5 --- Objective --- p.17
Chapter 1.6 --- Summary --- p.17
Chapter 2 --- Background --- p.18
Chapter 2.1 --- Multiple Drugs Tumor Chemotherapy --- p.18
Chapter 2.2 --- Bioinformatics --- p.22
Chapter 2.2.1 --- Basics of Bioinformatics --- p.24
Chapter 2.2.2 --- Applications on Biomedical Systems --- p.26
Chapter 3 --- A New Drug Administration Dynamic Model --- p.29
Chapter 3.1 --- Three Drugs Mathematical Model --- p.31
Chapter 3.1.1 --- Rate of Change of Different Subpopulations --- p.32
Chapter 3.1.2 --- Rate of Change of Different Drug Concen- trations --- p.35
Chapter 3.1.3 --- Toxicity Effects --- p.35
Chapter 3.1.4 --- Summary --- p.36
Chapter 4 --- Memetic Algorithm - Iterative Dynamic Program- ming (MA-IDP) --- p.38
Chapter 4.1 --- Problem Formulation: Optimal Control Problem (OCP) for Mutlidrug Optimization --- p.38
Chapter 4.2 --- Proposed Memetic Optimization Algorithm --- p.40
Chapter 4.2.1 --- Iterative Dynamic Programming (IDP) . . --- p.40
Chapter 4.2.2 --- Adaptive Elitist-population-based Genetic Algorithm (AEGA) --- p.44
Chapter 4.2.3 --- Memetic Algorithm 一 Iterative Dynamic Programming (MA-IDP) --- p.50
Chapter 4.3 --- Summary --- p.56
Chapter 5 --- MA-IDP: Experiments and Results --- p.57
Chapter 5.1 --- Experiment Settings --- p.57
Chapter 5.2 --- Optimization Results --- p.61
Chapter 5.3 --- Extension to Other Mutlidrug Scheduling Model . --- p.62
Chapter 5.4 --- Summary --- p.65
Chapter 6 --- DNA Sequencing by Hybridization (SBH) --- p.66
Chapter 6.1 --- Problem Formulation: Reconstructing a DNA Sequence from Hybridization Data --- p.70
Chapter 6.2 --- Proposed Memetic Optimization Algorithm --- p.71
Chapter 6.2.1 --- Chromosome Encoding --- p.71
Chapter 6.2.2 --- Fitness Function --- p.73
Chapter 6.2.3 --- Crossover --- p.74
Chapter 6.2.4 --- Hill Climbing Local Search for Sequencing by Hybridization --- p.76
Chapter 6.2.5 --- Elitism and Diversity --- p.79
Chapter 6.2.6 --- Outline of Algorithm: MA-HC-SBH --- p.81
Chapter 6.3 --- Summary --- p.82
Chapter 7 --- DNA Sequencing by Hybridization (SBH): Experiments and Results --- p.83
Chapter 7.1 --- Experiment Settings --- p.83
Chapter 7.2 --- Experiment Results --- p.85
Chapter 7.3 --- Summary --- p.89
Chapter 8 --- Conclusion --- p.90
Chapter 8.1 --- Multiple Drugs Cancer Chemotherapy Schedule Optimization --- p.90
Chapter 8.2 --- Use of the MA-IDP --- p.91
Chapter 8.3 --- DNA Sequencing by Hybridization (SBH) --- p.92
Chapter 8.4 --- Use of the MA-HC-SBH --- p.92
Chapter 8.5 --- Future Work --- p.93
Chapter 8.6 --- Item Learned --- p.93
Chapter 8.7 --- Papers Published --- p.94
Bibliography --- p.95
Tiwari, A., J. Knowles, E. Avineri, Keshav P. Dahal e R. Roy. "Applications of Soft Computing". 2006. http://hdl.handle.net/10454/2291.
Testo completoBourennani, Farid. "Leadership based multi-objective optimization with applications in energy systems". Diss., 2013. http://hdl.handle.net/10155/396.
Testo completo(11073474), Bin Zhang. "Data-driven Uncertainty Analysis in Neural Networks with Applications to Manufacturing Process Monitoring". Thesis, 2021.
Cerca il testo completoArtificial neural networks, including deep neural networks, play a central role in data-driven science due to their superior learning capacity and adaptability to different tasks and data structures. However, although quantitative uncertainty analysis is essential for training and deploying reliable data-driven models, the uncertainties in neural networks are often overlooked or underestimated in many studies, mainly due to the lack of a high-fidelity and computationally efficient uncertainty quantification approach. In this work, a novel uncertainty analysis scheme is developed. The Gaussian mixture model is used to characterize the probability distributions of uncertainties in arbitrary forms, which yields higher fidelity than the presumed distribution forms, like Gaussian, when the underlying uncertainty is multimodal, and is more compact and efficient than large-scale Monte Carlo sampling. The fidelity of the Gaussian mixture is refined through adaptive scheduling of the width of each Gaussian component based on the active assessment of the factors that could deteriorate the uncertainty representation quality, such as the nonlinearity of activation functions in the neural network.
Following this idea, an adaptive Gaussian mixture scheme of nonlinear uncertainty propagation is proposed to effectively propagate the probability distributions of uncertainties through layers in deep neural networks or through time in recurrent neural networks. An adaptive Gaussian mixture filter (AGMF) is then designed based on this uncertainty propagation scheme. By approximating the dynamics of a highly nonlinear system with a feedforward neural network, the adaptive Gaussian mixture refinement is applied at both the state prediction and Bayesian update steps to closely track the distribution of unmeasurable states. As a result, this new AGMF exhibits state-of-the-art accuracy with a reasonable computational cost on highly nonlinear state estimation problems subject to high magnitudes of uncertainties. Next, a probabilistic neural network with Gaussian-mixture-distributed parameters (GM-PNN) is developed. The adaptive Gaussian mixture scheme is extended to refine intermediate layer states and ensure the fidelity of both linear and nonlinear transformations within the network so that the predictive distribution of output target can be inferred directly without sampling or approximation of integration. The derivatives of the loss function with respect to all the probabilistic parameters in this network are derived explicitly, and therefore, the GM-PNN can be easily trained with any backpropagation method to address practical data-driven problems subject to uncertainties.
The GM-PNN is applied to two data-driven condition monitoring schemes of manufacturing processes. For tool wear monitoring in the turning process, a systematic feature normalization and selection scheme is proposed for the engineering of optimal feature sets extracted from sensor signals. The predictive tool wear models are established using two methods, one is a type-2 fuzzy network for interval-type uncertainty quantification and the other is the GM-PNN for probabilistic uncertainty quantification. For porosity monitoring in laser additive manufacturing processes, convolutional neural network (CNN) is used to directly learn patterns from melt-pool patterns to predict porosity. The classical CNN models without consideration of uncertainty are compared with the CNN models in which GM-PNN is embedded as an uncertainty quantification module. For both monitoring schemes, experimental results show that the GM-PNN not only achieves higher prediction accuracies of process conditions than the classical models but also provides more effective uncertainty quantification to facilitate the process-level decision-making in the manufacturing environment.
Based on the developed uncertainty analysis methods and their proven successes in practical applications, some directions for future studies are suggested. Closed-loop control systems may be synthesized by combining the AGMF with data-driven controller design. The AGMF can also be extended from a state estimator to the parameter estimation problems in data-driven models. In addition, the GM-PNN scheme may be expanded to directly build more complicated models like convolutional or recurrent neural networks.
"Learning Bayesian networks using evolutionary computation and its application in classification". 2001. http://library.cuhk.edu.hk/record=b5890754.
Testo completoThesis (M.Phil.)--Chinese University of Hong Kong, 2001.
Includes bibliographical references (leaves 126-133).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Problem Statement --- p.4
Chapter 1.2 --- Contributions --- p.4
Chapter 1.3 --- Thesis Organization --- p.5
Chapter 2 --- Background --- p.7
Chapter 2.1 --- Bayesian Networks --- p.7
Chapter 2.1.1 --- A Simple Example [42] --- p.8
Chapter 2.1.2 --- Formal Description and Notations --- p.9
Chapter 2.1.3 --- Learning Bayesian Network from Data --- p.14
Chapter 2.1.4 --- Inference on Bayesian Networks --- p.18
Chapter 2.1.5 --- Applications of Bayesian Networks --- p.19
Chapter 2.2 --- Bayesian Network Classifiers --- p.20
Chapter 2.2.1 --- The Classification Problem in General --- p.20
Chapter 2.2.2 --- Bayesian Classifiers --- p.21
Chapter 2.2.3 --- Bayesian Network Classifiers --- p.22
Chapter 2.3 --- Evolutionary Computation --- p.28
Chapter 2.3.1 --- Four Kinds of Evolutionary Computation --- p.29
Chapter 2.3.2 --- Cooperative Coevolution --- p.31
Chapter 3 --- Bayesian Network Learning Algorithms --- p.33
Chapter 3.1 --- Related Work --- p.34
Chapter 3.1.1 --- Using GA --- p.34
Chapter 3.1.2 --- Using EP --- p.36
Chapter 3.1.3 --- Criticism of the Previous Approaches --- p.37
Chapter 3.2 --- Two New Strategies --- p.38
Chapter 3.2.1 --- A Hybrid Framework --- p.38
Chapter 3.2.2 --- A New Operator --- p.39
Chapter 3.3 --- CCGA --- p.44
Chapter 3.3.1 --- The Algorithm --- p.45
Chapter 3.3.2 --- CI Test Phase --- p.46
Chapter 3.3.3 --- Cooperative Coevolution Search Phase --- p.47
Chapter 3.4 --- HEP --- p.52
Chapter 3.4.1 --- A Novel Realization of the Hybrid Framework --- p.54
Chapter 3.4.2 --- Merging in HEP --- p.55
Chapter 3.4.3 --- Prevention of Cycle Formation --- p.55
Chapter 3.5 --- Summary --- p.56
Chapter 4 --- Evaluation of Proposed Learning Algorithms --- p.57
Chapter 4.1 --- Experimental Methodology --- p.57
Chapter 4.2 --- Comparing the Learning Algorithms --- p.61
Chapter 4.2.1 --- Comparing CCGA with MDLEP --- p.63
Chapter 4.2.2 --- Comparing HEP with MDLEP --- p.65
Chapter 4.2.3 --- Comparing CCGA with HEP --- p.68
Chapter 4.3 --- Performance Analysis of CCGA --- p.70
Chapter 4.3.1 --- Effect of Different α --- p.70
Chapter 4.3.2 --- Effect of Different Population Sizes --- p.72
Chapter 4.3.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.73
Chapter 4.3.4 --- Effect of Varying Belief Factor --- p.76
Chapter 4.4 --- Performance Analysis of HEP --- p.77
Chapter 4.4.1 --- The Hybrid Framework and the Merge Operator --- p.77
Chapter 4.4.2 --- Effect of Different Population Sizes --- p.80
Chapter 4.4.3 --- Effect of Different --- p.81
Chapter 4.4.4 --- Efficiency of the Merge Operator --- p.84
Chapter 4.5 --- Summary --- p.85
Chapter 5 --- Learning Bayesian Network Classifiers --- p.87
Chapter 5.1 --- Issues in Learning Bayesian Network Classifiers --- p.88
Chapter 5.2 --- The Multinet Classifier --- p.89
Chapter 5.3 --- The Augmented Bayesian Network Classifier --- p.91
Chapter 5.4 --- Experimental Methodology --- p.94
Chapter 5.5 --- Experimental Results --- p.97
Chapter 5.6 --- Discussion --- p.103
Chapter 5.7 --- Application in Direct Marketing --- p.106
Chapter 5.7.1 --- The Direct Marketing Problem --- p.106
Chapter 5.7.2 --- Response Models --- p.108
Chapter 5.7.3 --- Experiment --- p.109
Chapter 5.8 --- Summary --- p.115
Chapter 6 --- Conclusion --- p.116
Chapter 6.1 --- Summary --- p.116
Chapter 6.2 --- Future Work --- p.118
Chapter A --- A Supplementary Parameter Study --- p.120
Chapter A.1 --- Study on CCGA --- p.120
Chapter A.1.1 --- Effect of Different α --- p.120
Chapter A.1.2 --- Effect of Different Population Sizes --- p.121
Chapter A.1.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.121
Chapter A.1.4 --- Effect of Varying Belief Factor --- p.122
Chapter A.2 --- Study on HEP --- p.123
Chapter A.2.1 --- The Hybrid Framework and the Merge Operator --- p.123
Chapter A.2.2 --- Effect of Different Population Sizes --- p.124
Chapter A.2.3 --- Effect of Different Δα --- p.124
Chapter A.2.4 --- Efficiency of the Merge Operator --- p.125
Yi-HungTang e 湯伊鴻. "Application of Evolutionary Computation to the Design of Linear Antenna Arrays". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/28318885913704215209.
Testo completo(9813641), Dujuan Li. "Constrained multi-objective particle swarm optimization with application in power generation". Thesis, 2009. https://figshare.com/articles/thesis/Constrained_multi-objective_particle_swarm_optimization_with_application_in_power_generation/16910656.
Testo completoChen, Zhen-Yao, e 陳振耀. "Application of Evolutionary Computation-Based Radial Basis Function Neural Network to IPC Sales Forecasting". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/d83vya.
Testo completo國立臺北科技大學
工商管理研究所
98
Forecasting is one of the crucial factors in practical application since it ensures the effective allocation of capacity and proper amount of inventory. Since auto-regressive integrated moving average (ARIMA) models which are more suitable for linear data have their constraints in predicting complex data for the real-world problems, some approaches have been developed to conquer the challenge of nonlinear forecasting. Therefore, for the purpose of forecasting nonlinear data, this study intends to develop three integrated evolutionary computation (EC)-based algorithms for training radial basis function neural network (RBFnn). The EC-based algorithms include genetic algorithm (GA), particle swarm optimization (PSO), and artificial immune system (AIS). In order to verify these three developed integrated EC-based algorithms, three benchmark continuous test functions were employed. The experimental results of three integrated EC-based algorithms are really very promising. In addition, industrial personal computer (IPC) sales data provided by an international well-known IPC manufacturer in Taiwan is also applied to further assess these developed algorithms. The model evaluation results indicated that the developed algorithms really can forecast more accurately. Furthermore, if foreign exchange (FX) factor is considered, the forecasting results can be improved.
Xie, Yue. "Bio-Inspired Computing for Chance-Constrained Combinatorial Optimisation Problems". Thesis, 2021. https://hdl.handle.net/2440/134213.
Testo completoThesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2021
Chang, Che-Ming, e 張哲銘. "Application of real-coding on evolutionary computation for distribution system feeder reconfiguration problems under load variations". Thesis, 2012. http://ndltd.ncl.edu.tw/handle/77c8x9.
Testo completo國立臺北科技大學
自動化科技研究所
100
As the concepts of environmental protection and energy saving bring more attention, how to reduce the energy loss during the normal operations of distribution system becomes an important issue. Feeder reconfiguration is a very important technique that can be used to deal with different types of distribution system problems. By changing the distribution system structure the distribution system can be operated in a more efficient way during normal and contingency operations. Feeder reconfiguration is a typical combinatorial optimization problem. Due to the large amount of switches on a distribution system, the possible solutions of the switching operation plans increase dramatically. Therefore, searching for the best switching operation plan to accomplish the feeder reconfiguration becomes an important issue. This paper applies real-coding of Genetic Algorithm for single- and multi-objectives feeder reconfiguration under fixed load and various load conditions. The searching efficiency and stability with other coding methods are compared. In the multi-objectives feeder reconfiguration problems, the improved TOPSIS is applied to calculate the fitness value of the Genetic Algorithm in order to effectively solve the feeder reconfiguration problems.
Sun, Yuan Ming, e 孫院明. "The Evaluation and Application of Constructing Investment Decision Model of Multi-Stages Based on Evolutionary Computation". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/98674956673234952877.
Testo completo輔仁大學
資訊管理學系
95
The real stock price is difficult to predict, so if can find out the relation of the financial value of every company and stock price, it will contributes to promoting accuracy predicted that the stock price fluctuates. The research designs the first stage model with ' Genetic Algorithms ' (GA), and designs the fitness function with ' Regression Analysis '. It succeeds in filtering 86 key indicators base on 605 items of stock financial indicator that Taiwan Economic Journal (TEJ) offer. There are obvious relations between it and stock price change. And then accord with the spirit of ' Value Investing ', scoring and ranking with 86 key indicators and industry's weighting, as the design of the second stage model, can assess the relatively strong weak tendency that the stock displays on the basic side effectively, and then promote the accuracy of choosing stocks and investment performance. Finally, the research designs the third stage model with ' Genetic Programming ' (GP), combine the rate of returns of Portfolio, Risk, Value at Risk (VaR), and Sharpe Ratio, etc., to design different fitness function, can suit different investment purposes and risk attribute effectively. In addition, perform the revision of the algorithm of GAOT toolbox, and improves GPLAB toolbox with joins the mechanism of pruning, contribute to promoting the efficiency of evolution. Proved by the experiment, it is a experimental model for 3 months of the length of the slide window, its performance exceed other experiment models with one month and 6 months of the length by a wide margin, meanwhile, the investment risk only increases slightly, show with change rate of three months of the stock key indicators, have best choosing stocks consulting and accuracy. In addition, the experiment also proves, the stock market of Taiwan has characteristic of ' Weak Form Efficient Market ' during the period from 1990 to 1994. So it is very suitable for using ' Value Investing ', ' Relative Strength Strategy ' and ' Momentum Strategy ' to make the investment, and then have obvious ' Price Momentum Effect ' and ' Book-to-Market Ratio Effect ', and is also suitable for ' Three-factor Model ', but ' P/E Ratio Effect ' and ' Size Effect ' is not apparent. By pretest, can effective optimize to take experiment control parameters, and can obtain better experimental results. According to the portfolio theory, filtering key indicators, industry’s weighting, scoring and ranking, and the evolution of choosing stocks tactics with combination of the mechanism of multi-stages, the performance of investment created by it, exceed the portfolio combination which a researcher produced only with single indicator or a few indicators in the past by a wide margin. The items of investment used in this research contain all stock of TES and OTC, and it has better extensive and practicability. Finally, all experimental models are by the inspection of Benchmark experiment, and the analysis of ANOVA, shows the investment decision model of multi-stages of this research has apparent reliability and validity.
McNeany, Scott Edward. "Characterizing software components using evolutionary testing and path-guided analysis". 2013. http://hdl.handle.net/1805/3775.
Testo completoEvolutionary testing (ET) techniques (e.g., mutation, crossover, and natural selection) have been applied successfully to many areas of software engineering, such as error/fault identification, data mining, and software cost estimation. Previous research has also applied ET techniques to performance testing. Its application to performance testing, however, only goes as far as finding the best and worst case, execution times. Although such performance testing is beneficial, it provides little insight into performance characteristics of complex functions with multiple branches. This thesis therefore provides two contributions towards performance testing of software systems. First, this thesis demonstrates how ET and genetic algorithms (GAs), which are search heuristic mechanisms for solving optimization problems using mutation, crossover, and natural selection, can be combined with a constraint solver to target specific paths in the software. Secondly, this thesis demonstrates how such an approach can identify local minima and maxima execution times, which can provide a more detailed characterization of software performance. The results from applying our approach to example software applications show that it is able to characterize different execution paths in relatively short amounts of time. This thesis also examines a modified exhaustive approach which can be plugged in when the constraint solver cannot properly provide the information needed to target specific paths.
(9811760), Scott Ladley. "An investigation into the application of evolutionary algorithms on highly constrained optimal control problems and the development of a graphical user interface for comprehensive algorithm control and monitoring". Thesis, 2003. https://figshare.com/articles/thesis/An_investigation_into_the_application_of_evolutionary_algorithms_on_highly_constrained_optimal_control_problems_and_the_development_of_a_graphical_user_interface_for_comprehensive_algorithm_control_and_monitoring/19930160.
Testo completoIn this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be applied to finding solutions to discrete optimal control problems. Also, a detailed investigation is carried out into the design and development of a superior execution environment for Evolutionary Algorithms.
An overview of the basic processes of an Evolutionary Algorithm is given, as well as detailed descriptions for several genetic operators. Several additional operators that may be applied in conjunction with an Evolutionary Algorithm are also studied. These operators include several versions of the simplex method, as well as 3 distinct hill -climbers, each designed for a specific purpose. The hill -climbing routines have been designed for purposes that include local search, escaping local minima, and a hill -climbing routine designed for self -adaptation to a broad range of problems.
The mathematical programming formulation of discrete optimal control problems is used to generate a class of highly constrained problems. Techniques are developed to accurately and rapidly solve these problems, whilst satisfying the equality constraints to machine accuracy.
The improved execution environment for Evolutionary Algorithms proposes the use of a Graphical User Interface for data visualisation, algorithm control and monitoring, as well as a Client/Server network interface for connecting the GUI to remotely run algorithms.