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Добірка наукової літератури з теми "Genetic algorithms – Statistical methods"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 6 лютого 2022

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Статті в журналах з теми "Genetic algorithms – Statistical methods"

Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design problem are discussed. A new, hybrid genetic algorithm incorporating local search technique to further the improved genetic algorithm's practicality is proposed. Further comparisons are drawn between these algorithms based on a test case layout. Finally, the developed hybrid algorithm is implemented on a section of an actual ship, and the findings are presented.

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Salimi, Amir Hossein, Jafar Masoompour Samakosh, Ehsan Sharifi, Mohammad Reza Hassanvand, Amir Noori, and Hary von Rautenkranz. "Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data." Water 11, no. 8 (August 10, 2019): 1653. http://dx.doi.org/10.3390/w11081653.

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Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.

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Hatjimihail, A. T. "Genetic algorithms-based design and optimization of statistical quality-control procedures." Clinical Chemistry 39, no. 9 (September 1, 1993): 1972–78. http://dx.doi.org/10.1093/clinchem/39.9.1972.

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Abstract In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process, the program "Optimize" optimizes a user-defined QC procedure, whereas the program "Design" designs a novel optimized QC procedure. The programs search through the parameter space and find the optimal or near-optimal solution. The possible solutions of the optimization problem are evaluated with computer simulation.

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Cotfas, Daniel T., Petru A. Cotfas, Mihai P. Oproiu, and Paul A. Ostafe. "Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters." International Journal of Photoenergy 2021 (September 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/3608138.

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The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms. The comparison between the widely used analytical method and some of the best metaheuristic algorithms from the algorithm families is made for datasets from the specialized literature, using the following statistical tests: absolute error, root mean square error, and the coefficient of determination. The equivalent circuit and mathematical model considered is the single diode model. The result comparison shows that the metaheuristic algorithms have the best performance in almost all cases, and only for the genetic algorithm, there are poorer results for one chosen photovoltaic cell. The parameters of the photovoltaic cells and panels and also the current-voltage characteristic for real outdoor weather conditions are forecasted using the parameters calculated with the best method: one for analytical—the five-parameter analytical method—and one for the metaheuristic algorithms—hybrid successive discretization algorithm. Additionally, the genetic algorithm is used. The forecast current-voltage characteristic is compared with the one measured in real sunlight conditions, and the best results are obtained in the case of a hybrid successive discretization algorithm. The maximum power forecast using the calculated parameters with the five-parameter method is the best, and the error in comparison with the measured ones is 0.48%.

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Tucker, Allan, Jason Crampton, and Stephen Swift. "RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms." Evolutionary Computation 13, no. 4 (December 2005): 477–99. http://dx.doi.org/10.1162/106365605774666903.

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There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.

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Tarnaris, Konstantinos, Ioanna Preka, Dionisis Kandris, and Alex Alexandridis. "Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study." Electronics 9, no. 4 (April 21, 2020): 675. http://dx.doi.org/10.3390/electronics9040675.

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The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, made not only against each other but also against two other well-known algorithms. This appraisal was made using statistical testing. The test results, that proved the efficacy of the algorithms proposed, were analyzed and concluding remarks were drawn.

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Huang, Chien-Feng, Chi-Jen Hsu, Chi-Chung Chen, Bao Rong Chang, and Chen-An Li. "An Intelligent Model for Pairs Trading Using Genetic Algorithms." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/939606.

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Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

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Aizawa, Akiko N., and Benjamin W. Wah. "Scheduling of Genetic Algorithms in a Noisy Environment." Evolutionary Computation 2, no. 2 (June 1994): 97–122. http://dx.doi.org/10.1162/evco.1994.2.2.97.

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In this paper, we develop new methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment. Such methods are related to the scheduling of resources for tests performed in genetic algorithms. Assuming that the population size is given, we address two problems related to the design of efficient scheduling algorithms specifically important in noisy environments. First, we study the durution-scheduling problem that is related to setting dynamically the duration of each generation. Second, we study the sample-allocation problem that entails the adaptive determination of the number of evaluations taken from each candidate in a generation. In our approach, we model the search process as a statistical selection process and derive equations useful for these problems. Our results show that our adaptive procedures improve the performance of genetic algorithms over that of commonly used static ones.

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Kang, Jae Youn, Byung Ik Choi, Hak Joo Lee, Sang Rok Lee, Joo Sung Kim, and Kee Joo Kim. "Genetic Algorithm Application in Multiaxial Fatigue Criteria Computation." International Journal of Modern Physics B 17, no. 08n09 (April 10, 2003): 1678–83. http://dx.doi.org/10.1142/s0217979203019502.

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Both critical plane and stress invariant approaches are used to evaluate fatigue limit criteria of machine component subjected to non-proportional cyclic loading. Critical plane methods require finding the smallest circle enclosing all the tips of shear stress vectors acting on the critical plane. In stress invariant methods, the maximum amplitude of the second invariant of the stress deviator should be determined. In this paper, the previous algorithms for constructing the minimum circumscribed circle or hyper-sphere are briefly reviewed and the method using genetic algorithm is proposed.

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Marcek, Dusan. "Some statistical and CI models to predict chaotic high-frequency financial data." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6419–30. http://dx.doi.org/10.3233/jifs-189107.

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To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

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Дисертації з теми "Genetic algorithms – Statistical methods"

Czarn, Andrew Simon Timothy. "Statistical exploratory analysis of genetic algorithms." University of Western Australia. School of Computer Science and Software Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0030.

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[Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the investigator along the exploratory pathway. The statistical methodology is demonstrated in this thesis using a number of case studies with a classical genetic algorithm with one-point crossover and bit-replacement mutation. In doing so we answer a number of questions about the relationship between the performance of the GA and the operators and encoding used. The methodology is suitable, however, to be applied to other adaptive optimization algorithms not treated in this thesis. In the first instance, as an initial demonstration of our methodology, we describe case studies using four standard test functions. It is found that the effect upon performance of crossover is predominantly linear while the effect of mutation is predominantly quadratic. Higher order effects are noted but contribute less to overall behaviour. In the case of crossover both positive and negative gradients are found which suggests using rates as high as possible for some problems while possibly excluding it for others. .... This is illustrated by showing how the use of Gray codes impedes the performance on a lower modality test function compared with a higher modality test function. Computer animation is then used to illustrate the actual mechanism by which this occurs. Fourthly, the traditional concept of a GA is that of selection, crossover and mutation. However, a limited amount of data from the literature has suggested that the niche for the beneficial effect of crossover upon GA performance may be smaller than has traditionally been held. Based upon previous results on not-linear-separable problems an exploration is made by comparing two test problem suites, one comprising non-rotated functions and the other comprising the same functions rotated by 45 degrees in the solution space rendering them not-linear-separable. It is shown that for the difficult rotated functions the crossover operator is detrimental to the performance of the GA. It is conjectured that what makes a problem difficult for the GA is complex and involves factors such as the degree of optimization at local minima due to crossover, the bias associated with the mutation operator and the Hamming Distances present in the individual problems due to the encoding. Furthermore, the GA was tested on a real world landscape minimization problem to see if the results obtained would match those from the difficult rotated functions. It is demonstrated that they match and that the features which make certain of the test functions difficult are also present in the real world problem. Overall, the proposed methodology is found to be an effective tool for revealing relationships between a randomized optimization algorithm and its encoding and parameters that are difficult to establish from more ad-hoc experimental studies alone.

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Shen, Rujun, and 沈汝君. "Mining optimal technical trading rules with genetic algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47870011.

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In recent years technical trading rules are widely known by more and more people, not only the academics many investors also learn to apply them in financial markets. One approach of constructing technical trading rules is to use technical indicators, such as moving average(MA) and filter rules. These trading rules are widely used possibly because the technical indicators are simple to compute and can be programmed easily. An alternative approach of constructing technical trading rules is to rely on some chart patterns. However, the patterns and signals detected by these rules are often made by the visual inspection through human eyes. As for as I know, there are no universally acceptable methods of constructing the chart patterns. In 2000, Prof. Andrew Lo and his colleagues are the first ones who define five pairs of chart patterns mathematically. They are Head-and-Shoulders(HS) & Inverted Headand- Shoulders(IHS), Broadening tops(BTOP) & bottoms(BBOT), Triangle tops(TTOP) & bottoms(TBOT), Rectangle tops(RTOP) & bottoms( RBOT) and Double tops(DTOP) & bottoms(DBOT). The basic formulation of a chart pattern consists of two steps: detection of (i) extreme points of a price series; and (ii) shape of the pattern. In Lo et al.(2000), the method of kernel smoothing was used to identify the extreme points. It was admitted by Lo et al. (2000) that the optimal bandwidth used in kernel method is not the best choice and the expert judgement is needed in detecting the bandwidth. In addition, their work considered chart pattern detection only but no buy/sell signal detection. It should be noted that it is possible to have a chart pattern formed without a signal detected, but in this case no transaction will be made. In this thesis, I propose a new class of technical trading rules which aims to resolve the above problems. More specifically, each chart pattern is parameterized by a set of parameters which governs the shape of the pattern, the entry and exit signals of trades. Then the optimal set of parameters can be determined by using genetic algorithms (GAs). The advantage of GA is that they can deal with a high-dimensional optimization problems no matter the parameters to be optimized are continuous or discrete. In addition, GA can also be convenient to use in the situation that the fitness function is not differentiable or has a multi-modal surface.
published_or_final_version
Statistics and Actuarial Science
Master
Master of Philosophy

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Barreau, Thibaud. "Strategic optimization of a global bank capital management using statistical methods on open data." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273413.

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This project is about the optimization of the capital management of a French global bank. Capital management corresponds here to allocating the available capital to the different business units. In this project, I focus on the optimization of the allocation of the risk weighted assets (RWA) between some of the business units of the bank, as a representation of the allocated capital. Emphasis is put on the market and retail part of the bank and the first step was to be able to model the evolution of a business unit given an economic environment. The second one was about optimizing the distribution of RWA among the selected parts of the bank.
Projektets ämne handlar om att optimering allokering av kapital inom en fransk global bank. Kapital management syftar här på hur kapital ska fördelas mellan olika avdelningar inom banken. I detta projekt fokuserar jag på optimering av allokeringen av riskvägda resurser (RWA) mellan några av bankens enheter, som en representation av det allokerade kapitalet. Uppsatsen inriktar sig främst emot retail-delen av banken. Första steget var att modellera utvecklingen av en bankavdelning givet en ekonomisk omgivning? Andra steget var att försöka optimera fördelningen av RWA mellan de utvalda bankavdelningarna.

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Larsen, Ross Allen Andrew. "Food Shelf Life: Estimation and Experimental Design." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1315.pdf.

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

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Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research. Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a large proportion of infeasible solutions. The primary goal of this research was to develop a GA that produced low-cost solutions with fewer infeasible solutions created at each generation than previous methods without introducing excessive CPU runtime costs. This research involved developing a GA that produces low-cost solutions with fewer infeasible solutions produced at each generation; and implementing selection and replacement operations that maintained genetic diversity during the evolution process. The GA's performance was tested using tables containing 10,000 and 100,000 cells. The primary criterion for the evaluation of effectiveness of the GA was total cost of the complementary suppressions and the CPU runtime. Experimental results indicate that the GA-based method developed in this dissertation produced better quality solutions than those produced by extant heuristics. Because existing heuristics are very effective, this GA-based method was able to surpass them only modestly. Existing evolutionary methods have also been used to improve upon the quality of solutions produced by heuristics. Experimental results show that the GA-based method developed in this dissertation is computationally more efficient than GA-based methods proposed in the literature. This is attributed to the fact that the specialized genetic operators designed in this study produce fewer infeasible solutions. The results of these experiments suggest the need for continued research into non-probabilistic methods to seed the initial populations, selection and replacement strategies that factor in genetic diversity on the level of the circuits protecting sensitive cells; solution-preserving crossover and mutation operators; and the use of cost benefit ratios to determine program termination.

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ZHANG, GE. "STATISTICAL METHODS IN GENETIC ASSOCIATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744.

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Valenzuela-Del, Rio Jose Eugenio. "Bayesian adaptive sampling for discrete design alternatives in conceptual design." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50263.

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The number of technology alternatives has lately grown to satisfy the increasingly demanding goals in modern engineering. These technology alternatives are handled in the design process as either concepts or categorical design inputs. Additionally, designers desire to bring into early design more and more accurate, but also computationally burdensome, simulation tools to obtain better performing initial designs that are more valuable in subsequent design stages. It constrains the computational budget to optimize the design space. These two factors unveil the need of a conceptual design methodology to use more efficiently sophisticated tools for engineering problems with several concept solutions and categorical design choices. Enhanced initial designs and discrete alternative selection are pursued. Advances in computational speed and the development of Bayesian adaptive sampling techniques have enabled the industry to move from the use of look-up tables and simplified models to complex physics-based tools in conceptual design. These techniques focus computational resources on promising design areas. Nevertheless, the vast majority of the work has been done on problems with continuous spaces, whereas concepts and categories are treated independently. However, observations show that engineering objectives experience similar topographical trends across many engineering alternatives. In order to address these challenges, two meta-models are developed. The first one borrows the Hamming distance and function space norms from machine learning and functional analysis, respectively. These distances allow defining categorical metrics that are used to build an unique probabilistic surrogate whose domain includes, not only continuous and integer variables, but also categorical ones. The second meta-model is based on a multi-fidelity approach that enhances a concept prediction with previous concept observations. These methodologies leverage similar trends seen from observations and make a better use of sample points increasing the quality of the output in the discrete alternative selection and initial designs for a given analysis budget. An extension of stochastic mixed-integer optimization techniques to include the categorical dimension is developed by adding appropriate generation, mutation, and crossover operators. The resulted stochastic algorithm is employed to adaptively sample mixed-integer-categorical design spaces. The proposed surrogates are compared against traditional independent methods for a set of canonical problems and a physics-based rotor-craft model on a screened design space. Next, adaptive sampling algorithms on the developed surrogates are applied to the same problems. These tests provide evidence of the merit of the proposed methodologies. Finally, a multi-objective rotor-craft design application is performed in a large domain space. This thesis provides several novel academic contributions. The first contribution is the development of new efficient surrogates for systems with categorical design choices. Secondly, an adaptive sampling algorithm is proposed for systems with mixed-integer-categorical design spaces. Finally, previously sampled concepts can be brought to construct efficient surrogates of novel concepts. With engineering judgment, design community could apply these contributions to discrete alternative selection and initial design assessment when similar topographical trends are observed across different categories and/or concepts. Also, it could be crucial to overcome the current cost of carrying a set of concepts and wider design spaces in the categorical dimension forward into preliminary design.

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Rogers, Alex. "Modelling genetic algorithms and evolving populations." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/261289/.

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A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics, originally due to Pr¨ugel-Bennett and Shapiro, is extended to ranking selection, a form of selection commonly used in the genetic algorithm community. The extension allows a reduction in the number of macroscopic variables required to model the mean behaviour of the genetic algorithm. This reduction allows a more qualitative understanding of the dynamics to be developed without sacrificing quantitative accuracy. The work is extended beyond modelling the dynamics of the genetic algorithm. A caricature of an optimisation problem with many local minima is considered — the basin with a barrier problem. The first passage time — the time required to escape the local minima to the global minimum — is calculated and insights gained as to how the genetic algorithm is searching the landscape. The interaction of the various genetic algorithm operators and how these interactions give rise to optimal parameters values is studied.

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Shar, Nisar Ahmed. "Statistical methods for predicting genetic regulation." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16729/.

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Transcriptional regulation of gene expression is essential for cellular differentiation and function, and defects in the process are associated with cancer. Transcription is regulated by the cis-acting regulatory regions and trans-acting regulatory elements. Transcription factors bind on enhancers and repressors and form complexes by interacting with each other to control the expression of the genes. Understanding the regulation of genes would help us to understand the biological system and can be helpful in identifying therapeutic targets for diseases such as cancer. The ENCODE project has mapped binding sites of many TFs in some important cell types and this project also has mapped DNase I hypersensitivity sites across the cell types. Predicting transcription factors mutual interactions would help us in finding the potential transcription regulatory networks. Here, we have developed two methods for prediction of transcription factors mutual interactions from ENCODE ChIP-seq data, and both methods generated similar results which tell us about the accuracy of the methods. It is known that functional regions of genome are conserved and here we identified that shared/overlapping transcription factor binding sites in multiple cell types and in transcription factors pairs are more conserved than their respective non-shared/non-overlapping binding sites. It has been also studied that co-binding sites influence the expression level of genes. Most of the genes mapped to the transcription factor co-binding sites have significantly higher level of expression than those genes which were mapped to the single transcription factor bound sites. The ENCODE data suggests a very large number of potential regulatory sites across the complete genome in many cell types and methods are needed to identify those that are most relevant and to connect them to the genes that they control. A penalized regression method, LASSO was used to build correlative models, and choose two regulatory regions that are predictive of gene expression, and link them to their respective gene. Here, we show that our identified regulatory regions accumulate significant number of somatic mutations that occur in cancer cells, suggesting that their effects may drive cancer initiation and development. Harboring of somatic mutations in these identified regulatory regions is an indication of positive selection, which has been also observed in cancer related genes.

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Pittman, Jennifer L. "Adaptive splines and genetic algorithms for optimal statistical modeling." Adobe Acrobat reader required to view the full dissertation, 2000. http://www.etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-23/index.html.

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Книги з теми "Genetic algorithms – Statistical methods"

An introduction to genetic algorithms. Cambridge, Mass: MIT Press, 1996.

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Bayesian phylogenetics: Methods, algorithms, and applications. Boca Raton: CRC Press/Taylor & Francis, 2014.

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Mathematical and statistical methods for genetic analysis. 2nd ed. New York: Springer, 2002.

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Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5.

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Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5.

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Mathematical and statistical methods for genetic analysis. New York: Springer, 1997.

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Rattray, Lars Magnus. Modelling the dynamics of genetic algorithms using statistical mechanics. Manchester: University of Manchester, 1997.

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Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

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Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

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Barone, Piero, Arnoldo Frigessi, and Mauro Piccioni, eds. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9.

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Частини книг з теми "Genetic algorithms – Statistical methods"

Lange, Kenneth. "Counting Methods and the EM Algorithm." In Mathematical and Statistical Methods for Genetic Analysis, 19–34. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_2.

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Lange, Kenneth. "Counting Methods and the EM Algorithm." In Mathematical and Statistical Methods for Genetic Analysis, 21–38. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5_2.

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Chen, Yingrui, Mark Elliot, and Duncan Smith. "The Application of Genetic Algorithms to Data Synthesis: A Comparison of Three Crossover Methods." In Privacy in Statistical Databases, 160–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99771-1_11.

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Battaglia, Francesco, Domenico Cucina, and Manuel Rizzo. "Periodic Autoregressive Models with Multiple Structural Changes by Genetic Algorithms." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 107–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_19.

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Kuri-Morales, Angel Fernando, and Jesús Gutiérrez-García. "Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis." In MICAI 2002: Advances in Artificial Intelligence, 108–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46016-0_12.

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Corazza, Marco, Giovanni Fasano, and Riccardo Gusso. "Portfolio selection with an alternative measure of risk: Computational performances of particle swarm optimization and genetic algorithms." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 123–30. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_15.

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Ossowski, Andrzej, and Anna Święcicka. "Statistical Genetic Algorithms." In Advances in Intelligent and Soft Computing, 143–54. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1813-0_13.

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Elston, Robert C., Jaya Satagopan, and Shuying Sun. "Statistical Genetic Terminology." In Methods in Molecular Biology, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7274-6_1.

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Jeffers, John N. R. "Genetic Algorithms I." In Machine Learning Methods for Ecological Applications, 107–21. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5_4.

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Stockwell, David R. B. "Genetic Algorithms II." In Machine Learning Methods for Ecological Applications, 123–44. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5_5.

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Тези доповідей конференцій з теми "Genetic algorithms – Statistical methods"

Reeves, C. R. "Genetic algorithms and statistical methods: a comparison." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951038.

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Carata, Serban-Vasile, Veta Ghenescu, Marian Ghenescu, Mihai Chindea, and Roxana Mihaescu. "Salt and Pepper Noise Removal by Combining Genetic Algorithms - Neural Networks and Statistical Methods." In 2018 12th International Conference on Communications (COMM). IEEE, 2018. http://dx.doi.org/10.1109/iccomm.2018.8430175.

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Zeliff, Kayla, Walter Bennette, and Scott Ferguson. "Multi-Objective Composite Panel Optimization Using Machine Learning Classifiers and Genetic Algorithms." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60125.

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Design spaces that consist of millions or billions of design combinations pose a challenge to current methods for identifying optimal solutions. Complex analyses can also lead to lengthy computation times that further challenge the effectiveness of an algorithm in terms of solution quality and run-time. This work explores combining the design space exploration approach of a Multi-Objective Genetic Algorithm with different instance-based, statistical, rule-based and ensemble classifiers to reduce the number of unnecessary function evaluations associated with poorly performing designs. Results indicate that introducing a classifier to identify child designs that are likely to push the Pareto frontier toward an optima reduce the number of function calculations by 75–85%, depending on the classifier implemented.

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Naranjo-Pérez, Javier, Andrés Sáez, Javier F. Jiménez-Alonso, Pablo Pachón, and Víctor Compán. "A Hybrid UKF-MAG Algorithm for Finite Element Model Updating of Historical Constructions." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.0029.

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<p>The finite element model (FE) updating is a calibration method that allows minimizing the discrepancies between the numerical and experimental modal parameters. As result, a more accurate FE model is obtained and the structural analysis can represent the real behaviour of the structure. However, it is a high computational cost process. To overcome this issue, alternative techniques have been developed. This study focuses on the use of the unscented Kalman filter (UKF), which is a local optimization algorithm based on statistical estimation of parameters taken into account the measurements. The dome of a real chapel is considered as benchmark structure. A FE model is updated applying two different algorithms: (i) the multi-objective genetic algorithm and (ii) a hybrid unscented Kalman filter-multi-objective genetic algorithm (UKF-MGA). Finally, a discussion of the results will be presented to compare the performance of both algorithms.</p>

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Serghiuta, Dumitru, John Tholammakkil, Naj Hammouda, and Anthony O’Hagan. "Testing of Statistical Procedures for Use in Optimization of Reactor Performance Under Aged Conditions." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-31054.

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This paper discusses a framework for designing artificial test problems, evaluation criteria, and two of the benchmark tests developed under a research project initiated by the Canadian Nuclear Safety Commission to investigate the approaches for qualification of tolerance limit methods and algorithms proposed for application in optimization of CANDU reactor protection trip setpoints for aged conditions. A significant component of this investigation has been the development of a series of benchmark problems of gradually increased complexity, from simple “theoretical” problems up to complex problems closer to the real application. The first benchmark problem discussed in this paper is a simplified scalar problem which does not involve extremal, maximum or minimum, operations, typically encountered in the real applications. The second benchmark is a high dimensional, but still simple, problem for statistical inference of maximum channel power during normal operation. Bayesian algorithms have been developed for each benchmark problem to provide an independent way of constructing tolerance limits from the same data and allow assessing how well different methods make use of those data and, depending on the type of application, evaluating what the level of “conservatism” is. The Bayesian method is not, however, used as a reference method, or “gold” standard, but simply as an independent review method. The approach and the tests developed can be used as a starting point for developing a generic suite (generic in the sense of potentially applying whatever the proposed statistical method) of empirical studies, with clear criteria for passing those tests. Some lessons learned, in particular concerning the need to assure the completeness of the description of the application and the role of completeness of input information, are also discussed. It is concluded that a formal process, which should include extended and detailed benchmark tests, but targeted to the context of the particular application and aimed at identifying the domain of validity of the proposed tolerance limit method and algorithm, is needed and might provide the necessary confidence in the proposed statistical procedure.

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Bordoloi, D. J., and Rajiv Tiwari. "Health Monitoring of Gear Elements Based on Time-Frequency Vibration by Support Vector Machine Algorithms." In ASME 2013 Gas Turbine India Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gtindia2013-3772.

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Health monitoring of a gear box has been attempted by the support vector machine (SVM) learning technique with the help of time-frequency (wavelet) vibration data. Multi-fault classification capability of the SVM is suitably demonstrated that is based on the selection of SVM parameters. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing the SVM parameters. Four fault conditions have been considered including the no defect case. Time domain vibration signals were obtained from the gearbox casing operated in a suitable speed range. The continuous wavelet transform (CWT) and wavelet packet transform (WPT) are extracted from time domain signals. A set of statistical features are extracted from the wavelet transform. The classification ability is noted and compared against predictions when purely time domain data is used, and it shows an excellent prediction performance.

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Marri, Kiran, and Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.

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The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.

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Ali, Muhammad Ansab, Tariq S. Khan, Saqib Salam, and Ebrahim Al Hajri. "Shape Optimization of Microchannels Using Surrogate Modelling." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87780.

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To minimize the computational and optimization time, a numerical simulation of 3D microchannel heat sink was performed using surrogate model to achieve the optimum shape. Latin hypercube sampling method was used to explore the design space and to construct the model. The accuracy of the model was evaluated using statistical methods like coefficient of multiple determinations and root mean square error. Thermal resistance and pressure drop being conflicting objective functions were selected to optimize the geometric parameters of the microchannel. Multi objective shape optimization of design was conducted using genetic algorithm and the optimum design solutions are presented in the Pareto front. The application of the surrogate methods has predicted the performance of the heat sink with the sufficient accuracy employing significantly lower computational resources.

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Ismail, Zuhaimy, Mohd Zulariffin Md Maarof, and Mohammad Fadzli. "Alteration of Box-Jenkins methodology by implementing genetic algorithm method." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907522.

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Звіти організацій з теми "Genetic algorithms – Statistical methods"

Gurdal, Zafer, Raphael T. Haftka, and Layne T. Watson. Wing Structural Design by Genetic Algorithms and Homotopy Methods. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada387245.

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Ott, Jurg. Statistical Genetic Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301699.

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Bazarov, Ivan, Matthew Andorf, William Bergan, Cameron Duncan, Vardan Khachatryan, Danilo Liarte, David Rubin, and James Sethna. Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1530158.

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Hutchinson, M. L., J. E. L. Corry, and R. H. Madden. A review of the impact of food processing on antimicrobial-resistant bacteria in secondary processed meats and meat products. Food Standards Agency, October 2020. http://dx.doi.org/10.46756/sci.fsa.bxn990.

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For meat and meat products, secondary processes are those that relate to the downstream of the primary chilling of carcasses. Secondary processes include maturation chilling, deboning, portioning, mincing and other operations such as thermal processing (cooking) that create fresh meat, meat preparations and ready-to-eat meat products. This review systematically identified and summarised information relating to antimicrobial resistance (AMR) during the manufacture of secondary processed meatand meat products (SPMMP). Systematic searching of eight literature databases was undertaken and the resultantpapers were appraised for relevance to AMR and SPMMP. Consideration was made that the appraisal scores, undertaken by different reviewers, were consistent. Appraisal reduced the 11,000 initially identified documents to 74, which indicated that literature relating to AMR and SPMMP was not plentiful. A wide range of laboratory methods and breakpoint values (i.e. the concentration of antimicrobial used to assess sensitivity, tolerance or resistance) were used for the isolation of AMR bacteria.The identified papers provided evidence that AMR bacteria could be routinely isolated from SPMMP. There was no evidence that either confirmed or refuted that genetic materials capable of increasing AMR in non-AMR bacteria were present unprotected (i.e. outside of a cell or a capsid) in SPMMP. Statistical analyses were not straightforward because different authors used different laboratory methodologies.However, analyses using antibiotic organised into broadly-related groups indicated that Enterobacteriaceaeresistant to third generation cephalosporins might be an area of upcoming concern in SPMMP. The effective treatment of patients infected with Enterobacteriaceaeresistant to cephalosporins are a known clinical issue. No AMR associations with geography were observed and most of the publications identified tended to be from Europe and the far east.AMR Listeria monocytogenes and lactic acid bacteria could be tolerant to cleaning and disinfection in secondary processing environments. The basis of the tolerance could be genetic (e.g. efflux pumps) or environmental (e.g. biofilm growth). Persistent, plant resident, AMR L. monocytogenes were shown by one study to be the source of final product contamination. 4 AMR genes can be present in bacterial cultures used for the manufacture of fermented SPMMP. Furthermore, there was broad evidence that AMR loci could be transferred during meat fermentation, with refrigeration temperatures curtailing transfer rates. Given the potential for AMR transfer, it may be prudent to advise food business operators (FBOs) to use fermentation starter cultures that are AMR-free or not contained within easily mobilisable genetic elements. Thermal processing was seen to be the only secondary processing stage that served as a critical control point for numbers of AMR bacteria. There were significant linkages between some AMR genes in Salmonella. Quaternary ammonium compound (QAC) resistance genes were associated with copper, tetracycline and sulphonamide resistance by virtue of co-location on the same plasmid. No evidence was found that either supported or refuted that there was any association between AMR genes and genes that encoded an altered stress response or enhanced the survival of AMR bacteria exposed to harmful environmental conditions.

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