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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.
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Abraham, 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.
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Rahman, Izaz Ur. "Novel particle swarm optimization algorithms with applications in power systems". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12219.
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Optimization problems are vital in physical sciences, commercial and finance matters. In a nutshell, almost everyone is the stake-holder in certain optimization problems aiming at minimizing the cost of production and losses of system, and also maximizing the profit. In control systems, the optimal configuration problems are essential that have been solved by various newly developed methods. The literature is exhaustively explored for an appropriate optimization method to solve such kind of problems. Particle Swarm Optimization is found to be one of the best among several optimization methods by analysing the experimental results. Two novel PSO variants are introduced in this thesis. The first one is named as N State Markov Jumping Particle Swarm Optimization, which is based on the stochastic technique and Markov chain in updating the particle velocity. We have named the second variant as N State Switching Particle Swarm Optimization, which is based on the evolutionary factor information for updating the velocity. The proposed algorithms are then applied to some widely used mathematical benchmark functions. The statistical results of 30 independent trails illustrate the robustness and accuracy of the proposed algorithms for most of the benchmark functions. The better results in terms of mean minimum evaluation errors and the shortest computation time are illustrated. In order to verify the satisfactory performance and robustness of the proposed algorithms, we have further formulated some basic applications in power system operations. The first application is about the static Economic Load Dispatch and the second application is on the Dynamic Economic Load Dispatch. These are highly complex and non-linear problems of power system operations consisting of various systems and generator constraints. Basically, in the static Economic Load Dispatch, a single load is considered for calculating the cost function. In contrast, the Dynamic Economic Load Dispatch changes the load demand for the cost function dynamically with time. In such a challenging and complex environment the proposed algorithms can be applied. The empirical results obtained by applying both of the proposed methods have substantiated their adaptability and robustness into the real-world environment. It is shown in the numerical results that the proposed algorithms are robust and accurate as compared to the other algorithms. The proposed algorithms have produced consistent best values for their objectives, where satisfying all constraints with zero penalty.
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Kok, 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.
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This thesis is a study of new design methods for allowing evolutionary algorithms to be more effectively utilised in aerospace optimisation applications where computation needs are high and computation platform space may be restrictive. It examines the applicability of special hardware computational platforms known as field programmable gate arrays and shows that with the right implementation methods they can offer significant benefits. This research is a step forward towards the advancement of efficient and highly automated aircraft systems for meeting compact physical constraints in aerospace platforms and providing effective performance speedups over traditional methods.
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Ranjeet, 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.
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Red teaming (RT) is a process that assists an organization in finding vulnerabilities in a system whereby the organization itself takes on the role of an “attacker” to test the system. It is used in various domains including military operations. Traditionally, it is a manual process with some obvious weaknesses: it is expensive, time-consuming, and limited from the perspective of humans “thinking inside the box”. Automated RT is an approach that has the potential to overcome these weaknesses. In this approach both the red team (enemy forces) and blue team (friendly forces) are modelled as intelligent agents in a multi-agent system and the idea is to run many computer simulations, pitting the plan of the red team against the plan of blue team.
This research project investigated techniques that can support automated red teaming by conducting a systematic study involving a genetic algorithm (GA), a basic coevolutionary algorithm and three variants of the coevolutionary algorithm. An initial pilot study involving the GA showed some limitations, as GAs only support the optimization of a single population at a time against a fixed strategy. However, in red teaming it is not sufficient to consider just one, or even a few, opponent‟s strategies as, in reality, each team needs to adjust their strategy to account for different strategies that competing teams may utilize at different points. Coevolutionary algorithms (CEAs) were identified as suitable algorithms which were capable of optimizing two teams simultaneously for red teaming. The subsequent investigation of CEAs examined their performance in addressing the characteristics of red teaming problems, such as intransitivity relationships and multimodality, before employing them to optimize two red teaming scenarios. A number of measures were used to evaluate the performance of CEAs and in terms of multimodality, this study introduced a novel n-peak problem and a new performance measure based on the Circular Earth Movers‟ Distance.
Results from the investigations involving an intransitive number problem, multimodal problem and two red teaming scenarios showed that in terms of the performance measures used, there is not a single algorithm that consistently outperforms the others across the four test problems. Applications of CEAs on the red teaming scenarios showed that all four variants produced interesting evolved strategies at the end of the optimization process, as well as providing evidence of the potential of CEAs in their future application in red teaming.
The developed techniques can potentially be used for red teaming in military operations or analysis for protection of critical infrastructure. The benefits include the modelling of more realistic interactions between the teams, the ability to anticipate and to counteract potentially new types of attacks as well as providing a cost effective solution.
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Creaser, Paul. "Application of evolutionary computation techniques to missile guidance". Thesis, Cranfield University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367124.
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Sharifi, Soroosh. "Application of evolutionary computation to open channel flow modelling". Thesis, University of Birmingham, 2009. http://etheses.bham.ac.uk//id/eprint/478/.
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This thesis examines the application of two evolutionary computation techniques to two different aspects of open channel flow. The first part of the work is concerned with evaluating the ability of an evolutionary algorithm to provide insight and guidance into the correct magnitude and trend of the three parameters required in order to successfully apply a quasi 2D depth averaged Reynolds Averaged Navier Stokes (RANS) model to the flow in prismatic open channels. The RANS modeled adopted is the Shiono Knight Method (SKM) which requires three input parameters in order to provide closure, i.e. the friction factor (\(f\)), dimensionless eddy viscosity (λ) and a sink term representing the effects of secondary flow (Γ). A non-dominated sorting genetic algorithm II (NSGA-II) is used to construct a multiobjective evolutionary based calibration framework for the SKM from which conclusions relating to the appropriate values of \(f\), λ and Γ are made. The framework is applied to flows in homogenous and heterogeneous trapezoidal channels, homogenous rectangular channels and a number of natural rivers. The variation of \(f\), λ and Γ with the wetted parameter ratio (\(P_b\)/\(P_w\)) and panel structure for a variety of situations is investigated in detail. The situation is complex: \(f\) is relatively independent of the panel structure but is shown to vary with P\(_b\)/P\(_w\), the values of λ and Γ are highly affected by the panel structure but λ is shown to be relatively insensitive to changes in \(P_b\)/\(P_w\). Appropriate guidance in the form of empirical equations are provided. Comparing the results to previous calibration attempts highlights the effectiveness of the proposed semi-automated framework developed in this thesis. The latter part of the thesis examines the possibility of using genetic programming as an effective data mining tool in order to build a model induction methodology. To this end the flow over a free overfall is exampled for a variety of cross section shapes. In total, 18 datasets representing 1373 experiments were interrogated. It was found that an expression of form \(h_c\)=A\(h_e\)\(^{B\sqrt S_o}\), where \(h_c\) is the critical depth, \(h_e\) is the depth at the brink, \(S_o\) is the bed slope and A and B are two cross section dependant constants, was valid regardless of cross sectional shape and Froude number. In all of the cases examined this expression fitted the data to within a coefficient of determination (CoD) larger than 0.975. The discovery of this single expression for all datasets represents a significant step forward and highlights the power and potential of genetic programming.
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Hayward, Kevin. "Application of evolutionary algorithms to engineering design". University of Western Australia. School of Mechanical Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2009.0018.
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The efficiency of the mechanical design process can be improved by the use of evolutionary algorithms. Evolutionary algorithms provide a convenient and robust method to search for appropriate design solutions. Difficult non-linear problems are often encountered during the mechanical engineering design process. Solutions to these problems often involve computationally-intensive simulations. Evolutionary algorithms tuned to work with a small number of solution iterations can be used to automate the search for optimal solutions to these problems. An evolutionary algorithm was designed to give reliable results after a few thousand iterations; additionally the scalability and the ease of application to varied problems were considered. Convergence velocity of the algorithm was improved considerably by altering the mutation-based parameters in the algorithm. Much of this performance gain can be attributed to making the magnitude of the mutation and the minimum mutation rates self-adaptive. Three motorsport based design problems were simulated and the evolutionary algorithm was applied to search for appropriate solutions. The first two, a racing-line generator and a suspension kinematics simulation, were investigated to highlight properties of the evolutionary algorithm: reliability; solution representation; determining variable/performance relationships; and multiple objectives were discussed. The last of these problems was the lap-time simulation of a Formula SAE vehicle. This problem was solved with 32 variables, including a number of major conceptual differences. The solution to this optimisation was found to be significantly better than the 2004 UWA Motorsport vehicle, which finished 2nd in the 2005 US competition. A simulated comparison showed the optimised vehicle would score 62 more points (out of 675) in the dynamic events of the Formula SAE competition. Notably the optimised vehicle had a different conceptual design to the actual UWA vehicle. These results can be used to improve the design of future Formula SAE vehicles. The evolutionary algorithm developed here can be used as an automated search procedure for problems where performance solutions are computationally intensive.
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Wiltshire, Serge William. "On The Application Of Computational Modeling To Complex Food Systems Issues". ScholarWorks @ UVM, 2019. https://scholarworks.uvm.edu/graddis/1077.
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Transdisciplinary food systems research aims to merge insights from multiple fields, often revealing confounding, complex interactions. Computational modeling offers a means to discover patterns and formulate novel solutions to such systems-level problems. The best models serve as hubs—or boundary objects—which ground and unify a collaborative, iterative, and transdisciplinary process of stakeholder engagement. This dissertation demonstrates the application of agent-based modeling, network analytics, and evolutionary computational optimization to the pressing food systems problem areas of livestock epidemiology and global food security. It is comprised of a methodological introduction, an executive summary, three journal-article formatted chapters, and an overarching discussion section.
Chapter One employs an agent-based computer model (RUSH-PNBM v.1.1) developed to study the potential impact of the trend toward increased producer specialization on resilience to catastrophic epidemics within livestock production chains. In each run, an infection is introduced and may spread according to probabilities associated with the various modes of contact between hog producer, feed mill, and slaughter plant agents. Experimental data reveal that more-specialized systems are vulnerable to outbreaks at lower spatial densities, have more abrupt percolation transitions, and are characterized by less-predictable outcomes; suggesting that reworking network structures may represent a viable means to increase biosecurity.
Chapter Two uses a calibrated, spatially-explicit version of RUSH-PNBM (v.1.2) to model the hog production chains within three U.S. states. Key metrics are calculated after each run, some of which pertain to overall network structures, while others describe each actor’s positionality within the network. A genetic programming algorithm is then employed to search for mathematical relationships between multiple individual indicators that effectively predict each node’s vulnerability. This “meta-metric” approach could be applied to aid livestock epidemiologists in the targeting of biosecurity interventions and may also be useful to study a wide range of complex network phenomena.
Chapter Three focuses on food insecurity resulting from the projected gap between global food supply and demand over the coming decades. While no single solution has been identified, scholars suggest that investments into multiple interventions may stack together to solve the problem. However, formulating an effective plan of action requires knowledge about the level of change resulting from a given investment into each wedge, the time before that effect unfolds, the expected baseline change, and the maximum possible level of change. This chapter details an evolutionary-computational algorithm to optimize investment schedules according to the twin goals of maximizing global food security and minimizing cost. Future work will involve parameterizing the model through an expert informant advisory process to develop the existing framework into a practicable food policy decision-support tool.
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Antão, 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.
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Malaria is a major public health concern for the one-third of the human population esti- mated to be exposed to the threat of the most virulent species, Plasmodium falciparum. Modern molecular and computational tools from population genetics may help to better understand and fight the burden of drug resistant malaria. Malaria biology is substantially different from the underlying paradigm of standard population genetics models, most notably because malaria has both a asexual haploid phase and a sexual diploid phase where selfing (i.e. mating between genetically identical parasites) is possible. It is therefore fundamental to understand if commonly used population genetics methods are robust to the deviations from standard expectations imposed by the malaria life-cycle. We build novel models of malaria population genetics and provide guidelines to interpret empirical studies of the spread of drug resistance. Using realistic models of epistasis between genes involved in drug resistance we suggest that all signals of linkage disequilibrium (LD) are possible and that researchers should be confident in reporting a lack of statistical association between genes involved in resistance to antimalarials. We also suggest that researchers should be cautious in interpreting changes in the prevalence of drug resistance after control interventions as reductions in transmission can cause a change in prevalence without concomitant change in frequency of resistance. We provide guidelines to better design and interpret studies related to estimating effective population size (Ne). We use computational simulations to study scenarios that can approximate control and elimination interventions (i.e. where a significant part of the population is killed). For Ne estimation our results suggest that researchers must increase the number of individuals and loci genotyped in order to have sufficiently precise Ne estimates. LD-based Ne estimators are more appropriate for early detection of control and elimination interventions than temporal-based Ne estimators. Long- term estimators based on heterozygosity should not be used to make inferences about contemporary demographic processes. We also applied our analysis to disease vectors and we concluded that LD-based estimation is able to detect demographic seasonality patterns (i.e. changes in population size due to variations imposed by wet and dry seasons) whereas temporal estimators will provide averages over longer periods of time. We also studied selection detection using FsT-outlier approaches. Our results suggest that temporal FST might not be appropriate for early detection of genes involved in drug resistance (e.g. in the case of artesunate derivatives). We also provide software and guidelines to better design and interpret studies (also across other taxa) of selection based on FsT-outlier approaches. Most notably our results suggest that sampling only one or two SNPs per locus (as it is done in many empirical studies) might not be sufficient to detect areas of the genome under selection, and that at least 4 SNPs per loci should be genotyped.
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Collins, 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/.
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Evolutionary computation is an area within the field of artificial intelligence that is founded upon the principles of biological evolution. Evolution can be defined as the process of gradual development. Evolutionary algorithms are typically applied as a generic problem solving method, searching a problem space in order to locate good solutions. These solutions are found through an iterative evolutionary search that progresses by means of gradual developments. In the majority of cases of evolutionary computation the user is not aware of their algorithm's search behaviour. This causes two problems. First, the user has no way of assuring the quality of any solutions found other than to compare the solutions found by the algorithm with any available benchmark solutions or to re-run the algorithm and check if the results can be repeated or improved upon. Second, because the user is unaware of the algorithm's behaviour they have no way of identifying the contribution of the different components of the algorithm and therefore, no direct way of analyzing the algorithm's design and assigning credit to good algorithm components, or locating and improving ineffective algorithm components. The artificial intelligence and engineering communities have been slow to accept evolutionary computation as a robust problem-solving method because, unlike cased-based systems, rule-based systems or belief networks, they are unable to follow the algorithm's reasoning when locating a set of solutions in the problem space. During an evolutionary algorithm's execution the user may be able to see the results of the search but the search process itself like is a "black box" to the user. It is the search behaviour of evolutionary algorithms that needs to be understood by the user, in order for evolutionary computation to become more accepted within these communities. The aim of software visualization is to help people understand and use computer software. Software visualization technology has been applied successfully to illustrate a variety of heuristic search algorithms, programming languages and data structures. This thesis adopts software visualization as an approach for illustrating the search behaviour of evolutionary algorithms. Genetic Algorithms ("GAs") are used here as a specific case study to illustrate how software visualization may be applied to evolutionary computation. A set of visualization requirements are derived from the findings of a GA user study. A number of search space visualization techniques are examined for illustrating the search behaviour of a GA. "Henson," an extendable framework for developing visualization tools for genetic algorithms is presented. Finally, the application of the Henson framework is illustrated by the development of "Gonzo," a visualization tool designed to enable GA users to explore their algorithm's search behaviour. The contributions made in this thesis extend into the areas of software visualization, evolutionary computation and the psychology of programming. The GA user study presented here is the first and only known study of the working practices of GA users. The search space visualization techniques proposed here have never been applied in this domain before, and the resulting interactive visualizations provide the GA user with a previously unavailable insight into their algorithm's operation.
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Fabritius, Björn. "Application of genetic algorithms to problems in computational fluid dynamics". Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15236.
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In this thesis a methodology is presented to optimise non–linear mathematical models in numerical engineering applications. The method is based on biological evolution and uses known concepts of genetic algorithms and evolutionary compu- tation. The working principle is explained in detail, the implementation is outlined and alternative approaches are mentioned. The optimisation is then tested on a series of benchmark cases to prove its validity. It is then applied to two different types of problems in computational engineering. The first application is the mathematical modeling of turbulence. An overview of existing turbulence models is followed by a series of tests of different models applied to various types of flows. In this thesis the optimisation method is used to find improved coefficient values for the k–ε, the k–ω-SST and the Spalart–Allmaras models. In a second application optimisation is used to improve the quality of a computational mesh automatically generated by a third party software tool. This generation can be controlled by a set of parameters, which are subject to the optimisation. The results obtained in this work show an improvement when compared to non–optimised results. While computationally expensive, the genetic optimisation method can still be used in engineering applications to tune predefined settings with the aim to produce results of higher quality. The implementation is modular and allows for further extensions and modifications for future applications.
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Luitel, 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.
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Thesis (M.S.)--Missouri University of Science and Technology, 2009.Vita. 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).
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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.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Biological Engineering Division, 2007.This 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.
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Morrison, Kevin S. "Topological Data Analysis and Applications to Influenza". Miami University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=miami1595864809447239.
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Zemzami, 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.
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Reconnue depuis de nombreuses années comme une méthode efficace pour la résolution de problèmes difficiles, la méta-heuristique d’optimisation par essaim de particules PSO (Particle Swarm Optimization) présente toutefois des inconvénients dont les plus étudiés sont le temps de calcul élevé et la convergence prématurée. Cette thèse met en exergue quelques variantes de la méthode PSO visant à échapper à ces deux inconvénients de la méthode. Ces variantes combinent deux approches : la parallélisation de la méthode de calcul et l’organisation de voisinages appropriés pour les particules. L’évaluation de la performance des modèles proposés a été effectuée sur la base d'une expérimentation sur une série de fonctions tests. A la lumière de l’analyse des résultats expérimentaux obtenus, nous observons que les différents modèles proposés donnent des résultats meilleurs que ceux du PSO classique en termes de qualité de la solution et du temps de calcul. Un modèle basé PSO a été retenu et développé en vue d'une expérimentation sur le problème du transport d’électricité. Une variante hybride de ce modèle avec la méthode du recuit simulé SA (Simulated Annealing) a été considérée et expérimentée sur la problématique des réseaux de collaborationKnown 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
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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.
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Geodynamic modelling, via computer simulations, offers an easily controllable method for investigating the behaviour of an Earth system and providing feedback to conceptual models of geological evolution. However, most available computer codes have been developed for engineering or hydrological applications, where strains are small and post-failure deformation is not studied. Such codes cannot simultaneously model large deformation and porous fluid flow. To remedy this situation in the face of tectonic modelling, a numerical approach was developed to incorporate porous fluid flow into an existing high-deformation code called Ellipsis. The resulting software, with these twin capabilities, simulates the evolution of highly deformed tectonic regimes where fluid flow is important, such as in mineral provinces. A realistic description of deformation depends on the accurate characterisation of material properties and the laws governing material behaviour. Aside from the development of appropriate physics, it can be a difficult task to find a set of model parameters, including material properties and initial geometries, that can reproduce some conceptual target. In this context, an interactive system for the rapid exploration of model parameter space, and for the evaluation of all model results, replaces the traditional but time-consuming approach of finding a result via trial and error. The visualisation of all solutions in such a search of parameter space, through simple graphical tools, adds a new degree of understanding to the effects of variations in the parameters, the importance of each parameter in controlling a solution, and the degree of coverage of the parameter space. Two final applications of the software code and interactive parameter search illustrate the power of numerical modelling within the feedback loop to field observations. In the first example, vertical rheological contrasts between the upper and lower crust, most easily related to thermal profiles and mineralogy, exert a greater control over the mode of crustal extension than any other parameters. A weak lower crust promotes large fault spacing with high displacements, often overriding initial close fault spacing, to lead eventually to metamorphic core complex formation. In the second case, specifically tied to the history of compressional orogenies in northern Nevada, exploration of model parameters shows that the natural reactivation of early normal faults in the Proterozoic basement, regardless of basement topography or rheological contrasts, would explain the subsequent elevation and gravitationally-induced thrusting of sedimentary layers over the Carlin gold trend, providing pathways and ponding sites for mineral-bearing fluids.
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Vera-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.
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Systems biology is an emergent field focused on developing a system-level understanding of biological systems. In the last decade advances in genomics, transcriptomics and proteomics have gathered a remarkable amount data enabling the possibility of a system-level analysis to be grounded at a molecular level. The reverse-engineering of biochemical networks from experimental data has become a central focus in systems biology. A variety of methods have been proposed for the study and identification of the systemâ s structure and/or dynamics.
The objective of this dissertation is to introduce and propose solutions to some of the challenges inherent in reverse-engineering of biological systems.
First, previously developed reverse engineering algorithms are studied and compared using data from a simulated network. This study draws attention to the necessity for a uniform benchmark that enables an ob jective comparison and performance evaluation of reverse engineering methods.
Since several reverse-engineering algorithms require discrete data as input (e.g. dynamic Bayesian network methods, Boolean networks), discretization methods are being used for this purpose. Through a comparison of the performance of two network inference algorithms that use discrete data (from several different discretization methods) in this work, it has been shown that data discretization is an important step in applying network inference methods to experimental data.
Next, a reverse-engineering algorithm is proposed within the framework of polynomial dynamical systems over finite fields. This algorithm is built for the identification of the underlying network structure and dynamics; it uses as input gene expression data and, when available, a priori knowledge of the system. An evolutionary algorithm is used as the heuristic search method for an exploration of the solution space. Computational algebra tools delimit the search space, enabling also a description of model complexity. The performance and robustness of the algorithm are explored via an artificial network of the segment polarity genes in the D. melanogaster.
Once a mathematical model has been built, it can be used to run simulations of the biological system under study. Comparison of simulated dynamics with experimental measurements can help refine the model or provide insight into qualitative properties of the systems dynamical behavior. Within this work, we propose an efficient algorithm to describe the phase space, in particular to compute the number and length of all limit cycles of linear systems over a general finite field.
This research has been partially supported by NIH Grant Nr. RO1GM068947-01.Ph. D.
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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.
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L'analogia juga un papel fonamental en la resolucio de problemes i es troba darrere de molts dels processos centrals de la capacitat cognitiva humana, fins al punt que s'ha considerat "el nucli del coneixement". El raonament analogic funciona a traves del proces de la transferencia, l'us del coneixement apres en una situacio en l'altra per a la qual no va ser destinat. El paradigma de raonament basat en casos (case-based reasoning, CBR) presenta un model molt relacionat, pero lleugerament diferent de raonament utilitzat principalment en la intel.ligencia artificial; diferent en part perque el raonament analogic se centra habitualment en la similitud estructural entre-dominis mentre que CBR te a veure amb la transferencia de solucions entre els casos semanticament similars dins d'un domini especific.
En aquesta tesi, ens unim a aquests enfocaments interrelacionats de la ciència cognitiva, la psicologia i la intel.ligencia artificial, en un sistema CBR, on la recuperacio i l'adaptacio es duen a terme per l'Motor d'Associacio Estructural (SME) i son recolzats per el raonament de sentit comu integrant la informacio des de diverses bases de coneixement. Per permetre aixo, utilitzem una estructura de representacio de casos que es basa en les xarxes semantiques. Aixo ens dona un model CBR capac de recuperar i adaptar solucions de dominis que son aparentment diferents pero estructuralment molt similars, formant una de les nostres contribucions en aquest estudi.
Una de les principals limitacions de la investigacio sobre els sistemes CBR sempre ha estat l'adaptacio, on la majoria de les aplicacions es van conformar amb una simple "reutilitzacio" de la solucio del cas recuperat, principalment mitjancant una adaptacio null} o adaptacio sustitucional. La dificultat de l'adaptacio es encara mes evident per al nostre cas d'inter-domini CBR utilitzant xarxes semantiques. Resoldre aquesta dificultat aplana el cami per a una contribucio igualment important d'aquesta tesi, on s'introdueix una tecnica nova d'adaptacio generativa basada en la computacio evolutiva que permet la creacio o modificacio espontania de les xarxes semantiques d'acord a les necessitats d'adaptacio CBR.
Per a l'avaluacio d'aquest treball, apliquem el nostre sistema CBR al problema de la mediacio, un metode important en la resolucio de conflictes. El problema de la mediacio no es trivial i representa un molt bon exemple del mon real, en el qual podem detectar problemes estructuralment similars de dominis aparentment tan lluny com les relacions internacionals, conflictes familiars i els drets intel.lectuals.Analogy 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.
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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.
Texto completo da fonteResumo:
碩士樹德科技大學
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.
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"Applications of evolutionary algorithms on biomedical systems". 2007. http://library.cuhk.edu.hk/record=b5893179.
Texto completo da fonteResumo:
Tse, Sui Man.Thesis (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
22
Tiwari, A., J. Knowles, E. Avineri, Keshav P. Dahal e R. Roy. "Applications of Soft Computing". 2006. http://hdl.handle.net/10454/2291.
Texto completo da fonte
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Bourennani, Farid. "Leadership based multi-objective optimization with applications in energy systems". Diss., 2013. http://hdl.handle.net/10155/396.
Texto completo da fonteResumo:
Multi-objective optimization metaheuristics (MOMs) are powerful methods for solving complex optimization problems but can require a large number of function evaluations to find optimal solutions. Thus, an efficient multi-objective optimization method should generate accurate and diverse solutions in a timely manner. Improving MOMs convergence speed is an important and challenging research problem which is the scope of this thesis. This thesis conducted the most comprehensive comparative study ever in MOMs. Based on the results, multi-objective (MO) versions of particle swarm optimization (PSO) and differential evolution (DE) algorithms achieved the highest performances; therefore, these two MOMs have been selected as bases for further acceleration in this thesis. To accelerate the selected MOMs, this work focuses on the incorporation of leadership concept to MO variants of DE and PSO algorithms. Two complex case studies of MO design of renewable energy systems are proposed to demonstrate the efficiency of the proposed MOMs. This thesis proposes three new MOMs, namely, leader and speed constraint multi-objective PSO (LSMPSO), opposition-based third evolution step of generalized DE (OGDE3), and multi-objective DE with leadership enhancement (MODEL) which are compared with seven state-of-the-art MOMS using various benchmark problems. LSMPSO was found to be the fastest MOM for the problem undertaken. Further, LSMPSO achieved the highest solutions accuracy for optimal design of a photovoltaic farm in Toronto area.
OGDE3 is the first successful application of OBL to a MOM with single population (no-coevolution) using leadership and self-adaptive concepts; the convergence speed of OGDE3 outperformed the other MOMs for the problems solved. MODEL embodies leadership concept into mutation operator of GDE3 algorithm. MODEL achieved the highest accuracy for the 30 studied benchmark problems. Furthermore, MODEL achieved the highest solution accuracy for a MO optimization problem of hydrogen infrastructures design across the province Ontario between 2008 and 2025 considering electricity infrastructure constraints.
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(11073474), Bin Zhang. "Data-driven Uncertainty Analysis in Neural Networks with Applications to Manufacturing Process Monitoring". Thesis, 2021.
Encontre o texto completo da fonteResumo:
Artificial 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.
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"Learning Bayesian networks using evolutionary computation and its application in classification". 2001. http://library.cuhk.edu.hk/record=b5890754.
Texto completo da fonteResumo:
by Lee Shing-yan.Thesis (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
26
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.
Texto completo da fonte
27
(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.
Texto completo da fonteResumo:
This thesis is devoted to the study of metaheuristic optimization algorithms and their application in power generation. The study focuses on constrained multi-objective optimization using Particle Swarm Optimization algorithm.
A multi-objective constraint-handling method incorporating a dynamic neighbourhood PSO algorithm is proposed for tackling single objective constrained optimization problems. The benchmark simulation results demonstrate the proposed approach is effective and efficient in finding the consistent quality solutions. Compared with the recent research results, the proposed approach is able to provide better or similar good results in most benchmark functions. The proposed performance-based dynamic neighbourhood topology has proved to be able to help make convergence faster than the static neighbourhood topology.
The thesis also presents a modified PSO algorithm for solving multi-objective constrained optimization problems. Based on the constraint dominance concept, the proposed approach defines two sets of rules for determining the cognitive and social components of the PSO algorithm. Simulation results for the four numerical optimization problems demonstrate the proposed approach is effective. The proposed approach has a number of advantages such as being applicable to any number of objective functions and computationally inexpensive.
As applications, three engineering design optimization problems and the power generation loading optimization problem are investigated. The simulation results for the engineering design optimization problems and the power generation loading optimization problem reveal the capability, effectiveness and efficiency of the proposed approaches. The methodology can be readily applicable to a broad range of applications.
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Chen, 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.
Texto completo da fonteResumo:
博士國立臺北科技大學
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.
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Xie, Yue. "Bio-Inspired Computing for Chance-Constrained Combinatorial Optimisation Problems". Thesis, 2021. https://hdl.handle.net/2440/134213.
Texto completo da fonteResumo:
Bio-inspired methods have been widely used to solve stochastic optimisation problems, and they can find high-quality solutions. Motivated by real-world applications such as mining engineering problems where constraint violations have distributive effects, we discuss using chance-constrained optimisation to solve optimisation problems under various uncertainties. This thesis contributes to the theoretical analysis and application of evolutionary algorithms on chance-constrained combinatorial optimisation problems. We address complex problems under stochastic settings and are subject to chance constraints. We start our investigations in two significant areas: chance-constrained knapsack problem (CCKP) and a real-world application problem. The CCKP is a stochastic version of the classical knapsack problem, which aims to maximise the profit of selected items under a constraint that the knapsack capacity bound is violated with a small probability. We first show how to use well-known deviation inequalities as surrogate functions when tacking the chance constraint. Then, we investigate the performance of some classical approaches for solving knapsack problems and the simplest single- and multi-objective evolutionary algorithms on solving the CCKP instances. Our experimental results show that evolutionary algorithms perform better than those classical approaches in computation time and quality of solutions. Afterwards, to improve the performance of the evolutionary algorithms on solving the chance-constrained knapsack problems, we examine the use of two problem-specific operators and present a new multi-objective model of the problem. Our experimental results show that this leads to significant performance improvements when using the proposed operators in multi-objective evolutionary algorithms. Then, we perform a runtime analysis of a randomised search algorithm and a basic evolutionary algorithm for the chance-constrained knapsack problem with correlated uniform weights. Furthermore, we investigate a real-world problem in this thesis, the stockpile blending problem, which aims to blend material from stockpiles to construct concentrate parcels containing optimal metal grades. For this problem, we first present the model of the problem without chance constraints and name it the "deterministic model" and then show that the results obtained by a differential evolution approach are better than the actual results for all instances. We then consider the problem with uncertain source supply, named the stockpile blending problem with chance constraints. We introduce chance-constrained optimisation to guarantee the constraints are violated with a small probability to tackle the stochastic material grades and evaluate the performance of the differential evolution algorithm.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2021
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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.
Texto completo da fonteResumo:
碩士國立臺北科技大學
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.
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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.
Texto completo da fonteResumo:
碩士輔仁大學
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.
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McNeany, Scott Edward. "Characterizing software components using evolutionary testing and path-guided analysis". 2013. http://hdl.handle.net/1805/3775.
Texto completo da fonteResumo:
Indiana University-Purdue University Indianapolis (IUPUI)Evolutionary 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.
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(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.
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In 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.