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Artículos de revistas sobre el tema "Genetic algorithms – Statistical methods"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 6 de febrero de 2022

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1

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

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2

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 varia

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3

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,

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4

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. T

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5

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 perfor

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6

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, mad

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7

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 sho

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8

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 dete

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9

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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10

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 com

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Silva Arantes, Jesimar da, Márcio da Silva Arantes, Claudio Fabiano Motta Toledo, Onofre Trindade Júnior, and Brian Charles Williams. "Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760008. http://dx.doi.org/10.1142/s0218213017600089.

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The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the

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BILLHARDT, HOLGER, DANIEL BORRAJO, and VICTOR MAOJO. "LEARNING RETRIEVAL EXPERT COMBINATIONS WITH GENETIC ALGORITHMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 01 (February 2003): 87–113. http://dx.doi.org/10.1142/s0218488503001965.

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The goal of information retrieval (IR) is to provide models and systems that help users to identify the relevant documents to their information needs. Extensive research has been carried out to develop retrieval methods that solve this goal. These IR techniques range from purely syntax-based, considering only frequencies of words, to more semantics-aware approaches. However, it seems clear that there is no single method that works equally well on all collections and for all queries. Prior work suggests that combining the evidence from multiple retrieval experts can achieve significant improvem

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Mramba, Lazarus, and Salvador Gezan. "Evaluating Algorithm Efficiency for Optimizing Experimental Designs with Correlated Data." Algorithms 11, no. 12 (December 18, 2018): 212. http://dx.doi.org/10.3390/a11120212.

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The search for efficient methods and procedures to optimize experimental designs is a vital process in field trials that is often challenged by computational bottlenecks. Most existing methods ignore the presence of some form of correlations in the data to simplify the optimization process at the design stage. This study explores several algorithms for improving field experimental designs using a linear mixed models statistical framework adjusting for both spatial and genetic correlations based on A- and D-optimality criteria. Relative design efficiencies are estimated for an array of algorith

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14

Jiang, Zhenni, and Xiyu Liu. "A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data." Processes 8, no. 10 (October 21, 2020): 1326. http://dx.doi.org/10.3390/pr8101326.

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In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k

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15

Marcek, Dusan. "Time Series Analysis and Data Prediction of Large Databases: An Application to Electricity Demand Prediction." Advanced Materials Research 811 (September 2013): 401–6. http://dx.doi.org/10.4028/www.scientific.net/amr.811.401.

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We evaluate statistical and machine learning methods for half-hourly 1-step-ahead electricity demand prediction using Australian electricity data. We show that the machine learning methods that use autocorrelation feature selection and BackPropagation Neural Networks, Linear Regression as prediction algorithms outperform the statistical methods Exponential Smoothing and also a number of baselines. We analyze the effect of day time on the prediction error and show that there are time-intervals associated with higher and lower errors and that the prediction methods also differ in their accuracy

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LIN, C. Y., and A. J. LEE. "ESTIMATION OF ADDITIVE AND NONADDITIVE GENETIC VARIANCES IN NONINBRED POPULATIONS UNDER SIRE OR FULLSIB MODEL." Canadian Journal of Animal Science 69, no. 1 (March 1, 1989): 61–68. http://dx.doi.org/10.4141/cjas89-009.

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The separation of additive and nonadditive genetic variances has been a problem for animal breeding researchers because conventional methods of statistical analyses (least squares or ANOVA type) were not able to accomplish this task. Henderson presented computing algorithms for restricted maximum likelihood (REML) estimation of additive and nonadditive genetic variances from an animal model for noninbred populations. Unfortunately, application of this algorithm in practice requires extensive computing. This study extends Henderson's methodology to estimate additive genetic variance independent

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17

GONZALEZ-MONROY, LUIS I., and A. CORDOBA. "OPTIMIZATION OF ENERGY SUPPLY SYSTEMS: SIMULATED ANNEALING VERSUS GENETIC ALGORITHM." International Journal of Modern Physics C 11, no. 04 (June 2000): 675–90. http://dx.doi.org/10.1142/s0129183100000638.

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We have applied two methods (simulated annealing and genetic algorithms) to search the solution of a problem of optimization with constraints in order to determine the best way to fulfill different energy demands using a set of facilities of energy transformation and storage. We have introduced a computational efficiency factor that measures the efficiency of the optimization algorithm and, as a result, we can conclude that for short computation times, genetic algorithms are more efficient than simulated annealing when demand profiles are not very long, whereas the latter is more efficient tha

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18

Popelka, Ondřej, and Jiří Šťastný. "WWW portal usage analysis using genetic algorithms." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 57, no. 6 (2009): 201–8. http://dx.doi.org/10.11118/actaun200957060201.

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The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining). Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By leveraging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free g

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19

Horton, Pascal, Michel Jaboyedoff, and Charles Obled. "Global Optimization of an Analog Method by Means of Genetic Algorithms." Monthly Weather Review 145, no. 4 (March 13, 2017): 1275–94. http://dx.doi.org/10.1175/mwr-d-16-0093.1.

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Abstract Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at whic

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DE LOS CAMPOS, GUSTAVO, DANIEL GIANOLA, GUILHERME J. M. ROSA, KENT A. WEIGEL, and JOSÉ CROSSA. "Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods." Genetics Research 92, no. 4 (August 2010): 295–308. http://dx.doi.org/10.1017/s0016672310000285.

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SummaryPrediction of genetic values is a central problem in quantitative genetics. Over many decades, such predictions have been successfully accomplished using information on phenotypic records and family structure usually represented with a pedigree. Dense molecular markers are now available in the genome of humans, plants and animals, and this information can be used to enhance the prediction of genetic values. However, the incorporation of dense molecular marker data into models poses many statistical and computational challenges, such as how models can cope with the genetic complexity of

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21

D'Angelo, Donna J., Judy L. Meyer, Leslie M. Howard, Stanley V. Gregory, and Linda R. Ashkenas. "Ecological uses for genetic algorithms: predicting fish distributions in complex physical habitats." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 9 (September 1, 1995): 1893–908. http://dx.doi.org/10.1139/f95-782.

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Genetic algorithms (GA) are artificial intelligence techniques based on the theory of evolution that through the process of natural selection evolve formulae to solve problems or develop control strategies. We designed a GA to examine relationships between stream physical characteristics and trout distribution data for 3rd-, 5th-, and 7th-order stream sites in the Cascade Mountains, Oregon. Although traditional multivariate statistical techniques can perform this particular task, GAs are not constrained by assumptions of independence and linearity and therefore provide a useful alternative. To

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Sun, Hong Tao, Yong Shou Dai, Fang Wang, and Xing Peng. "Seismic Wavelet Estimation Using High-Order Statistics and Chaos-Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 4241–47. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4241.

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Accurate and effective seismic wavelet estimation has an extreme significance in the seismic data processing of high resolution, high signal-to-noise ratio and high fidelity. The emerging non-liner optimization methods enhance the applied potential for the statistical method of seismic wavelet extraction. Because non-liner optimization algorithms in the seismic wavelet estimation have the defects of low computational efficiency and low precision, Chaos-Genetic Algorithm (CGA) based on the cat mapping is proposed which is applied in the multi-dimensional and multi-modal non-linear optimization.

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Sen, G., and E. Akyol. "A genetic-algorithm approach for assessing the liquefaction potential of sandy soils." Natural Hazards and Earth System Sciences 10, no. 4 (April 9, 2010): 685–98. http://dx.doi.org/10.5194/nhess-10-685-2010.

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Abstract. The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been develo

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Mahdavi, Ali, Mohsen Najarchi, Emadoddin Hazaveie, Seyed Mohammad Mirhosayni Hazave, and Seyed Mohammad Mahdai Najafizadeh. "Comparison of neural networks and genetic algorithms to determine missing precipitation data (Case study: the city of Sari)." Revista de la Universidad del Zulia 11, no. 29 (February 8, 2020): 114–28. http://dx.doi.org/10.46925//rdluz.29.08.

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Neural networks and genetic programming in the investigation of new methods for predicting rainfall in the catchment area of the city of Sari. Various methods are used for prediction, such as the time series model, artificial neural networks, fuzzy logic, fuzzy Nero, and genetic programming. Results based on statistical indicators of root mean square error and correlation coefficient were studied. The results of the optimal model of genetic programming were compared, the correlation coefficients and the root mean square error 0.973 and 0.034 respectively for training, and 0.964 and 0.057 respe

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Drachal, Krzysztof, and Michał Pawłowski. "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities." Economies 9, no. 1 (January 19, 2021): 6. http://dx.doi.org/10.3390/economies9010006.

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic alg

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Juhola, M., S. Lammi, K. Viikki, and J. Laurikkala. "Comparison of Genetic Algorithms and Other Classification Methods in the Diagnosis of Female Urinary Incontinence." Methods of Information in Medicine 38, no. 02 (1999): 125–31. http://dx.doi.org/10.1055/s-0038-1634175.

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AbstractGalactica, a newly developed machine-learning system that utilizes a genetic algorithm for learning, was compared with discriminant analysis, logistic regression, k-means cluster analysis, a C4.5 decision-tree generator and a random bit climber hill-climbing algorithm. The methods were evaluated in the diagnosis of female urinary incontinence in terms of prediction accuracy of classifiers, on the basis of patient data. The best methods were discriminant analysis, logistic regression, C4.5 and Galactica. Practically no statistically significant differences existed between the prediction

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Vayenas, Nick, and Sihong Peng. "Reliability analysis of underground mining equipment using genetic algorithms." Journal of Quality in Maintenance Engineering 20, no. 1 (March 4, 2014): 32–50. http://dx.doi.org/10.1108/jqme-02-2013-0006.

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Purpose – While increased mechanization and automation make considerable contributions to mine productivity, unexpected equipment failures and imperfect planned or routine maintenance prohibit the maximum possible utilization of sophisticated mining equipment and require significant amount of extra capital investment. Traditional preventive/planned maintenance is usually scheduled at a fixed interval based on maintenance personnel's experience and it can result in decreasing reliability. This paper deals with reliability analysis and prediction for mining machinery. A software tool called GenR

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XIAO, HANGUANG, CONGZHONG CAI, and YUZONG CHEN. "MILITARY VEHICLE CLASSIFICATION VIA ACOUSTIC AND SEISMIC SIGNALS USING STATISTICAL LEARNING METHODS." International Journal of Modern Physics C 17, no. 02 (February 2006): 197–212. http://dx.doi.org/10.1142/s0129183106008789.

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It is a difficult and important task to classify the types of military vehicles using the acoustic and seismic signals generated by military vehicles. For improving the classification accuracy and reducing the computing time and memory size, we investigated different pre-processing technology, feature extraction and selection methods. Short Time Fourier Transform (STFT) was employed for feature extraction. Genetic Algorithms (GA) and Principal Component Analysis (PCA) were used for feature selection and extraction further. A new feature vector construction method was proposed by uniting PCA an

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Lee, Michael, and Ting Hu. "Computational Methods for the Discovery of Metabolic Markers of Complex Traits." Metabolites 9, no. 4 (April 4, 2019): 66. http://dx.doi.org/10.3390/metabo9040066.

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Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of

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LI, JING, and TAO JIANG. "A SURVEY ON HAPLOTYPING ALGORITHMS FOR TIGHTLY LINKED MARKERS." Journal of Bioinformatics and Computational Biology 06, no. 01 (February 2008): 241–59. http://dx.doi.org/10.1142/s0219720008003369.

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Two grand challenges in the postgenomic era are to develop a detailed understanding of heritable variation in the human genome, and to develop robust strategies for identifying the genetic contribution to diseases and drug responses. Haplotypes of single nucleotide polymorphisms (SNPs) have been suggested as an effective representation of human variation, and various haplotype-based association mapping methods for complex traits have been proposed in the literature. However, humans are diploid and, in practice, genotype data instead of haplotype data are collected directly. Therefore, efficien

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Nezhadhosein, Saeed, Aghileh Heydari, and Reza Ghanbari. "A Modified Hybrid Genetic Algorithm for Solving Nonlinear Optimal Control Problems." Mathematical Problems in Engineering 2015 (2015): 1–21. http://dx.doi.org/10.1155/2015/139036.

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Here, a two-phase algorithm is proposed for solving bounded continuous-time nonlinear optimal control problems (NOCP). In each phase of the algorithm, a modified hybrid genetic algorithm (MHGA) is applied, which performs a local search on offsprings. In first phase, a random initial population of control input values in time nodes is constructed. Next, MHGA starts with this population. After phase 1, to achieve more accurate solutions, the number of time nodes is increased. The values of the associated new control inputs are estimated by Linear interpolation (LI) or Spline interpolation (SI),

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Kingsley, Mark T., Timothy M. Straub, Douglas R. Call, Don S. Daly, Sharon C. Wunschel, and Darrell P. Chandler. "Fingerprinting Closely Related Xanthomonas Pathovars with Random Nonamer Oligonucleotide Microarrays." Applied and Environmental Microbiology 68, no. 12 (December 2002): 6361–70. http://dx.doi.org/10.1128/aem.68.12.6361-6370.2002.

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ABSTRACT Current bacterial DNA-typing methods are typically based on gel-based fingerprinting methods. As such, they access a limited complement of genetic information and many independent restriction enzymes or probes are required to achieve statistical rigor and confidence in the resulting pattern of DNA fragments. Furthermore, statistical comparison of gel-based fingerprints is complex and nonstandardized. To overcome these limitations of gel-based microbial DNA fingerprinting, we developed a prototype, 47-probe microarray consisting of randomly selected nonamer oligonucleotides. Custom ima

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NOROUZZADEH, P., B. RAHMANI, and M. S. NOROUZZADEH. "FORECASTING SMOOTHED NON-STATIONARY TIME SERIES USING GENETIC ALGORITHMS." International Journal of Modern Physics C 18, no. 06 (June 2007): 1071–86. http://dx.doi.org/10.1142/s0129183107011133.

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We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time s

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Yang, Yao Wen, and Ai Wei Miao. "Structural Parameters Identification Using PZT Sensors and Genetic Algorithms." Advanced Materials Research 79-82 (August 2009): 63–66. http://dx.doi.org/10.4028/www.scientific.net/amr.79-82.63.

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Piezoelectric ceramic lead zirconate titanate (PZT) based electro-mechanical impedance (EMI) technique for structural health monitoring (SHM) has been successfully applied to various engineering systems [1-5]. In the traditional EMI method, statistical analysis methods such as root mean square deviation indices of the PZT electromechanical (EM) admittance are used as damage indicator, which is difficult to specify the effect of damage on structural properties. This paper proposes to use the genetic algorithms (GAs) to identify the structural parameters according to the changes in the PZT admit

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Alam, Tanweer, Shamimul Qamar, Amit Dixit, and Mohamed Benaida. "Genetic Algorithm: Reviews, Implementations, and Applications." International Journal of Engineering Pedagogy (iJEP) 10, no. 6 (December 8, 2020): 57. http://dx.doi.org/10.3991/ijep.v10i6.14567.

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Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely u

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Abdellahoum, Hamza, and Abdelmajid Boukra. "A Fuzzy Cooperative Approach to Resolve the Image Segmentation Problem." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 188–214. http://dx.doi.org/10.4018/ijsir.2021070109.

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The image segmentation problem is one of the most studied problems because it helps in several areas. In this paper, the authors propose new algorithms to resolve two problems, namely cluster detection and centers initialization. The authors opt to use statistical methods to automatically determine the number of clusters and the fuzzy sets theory to start the algorithm with a near optimal configuration. They use the image histogram information to determine the number of clusters and a cooperative approach involving three metaheuristics, genetic algorithm (GA), firefly algorithm (FA). and bioge

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37

Mühlenbein, Heinz, and Robin Höns. "The Estimation of Distributions and the Minimum Relative Entropy Principle." Evolutionary Computation 13, no. 1 (March 2005): 1–27. http://dx.doi.org/10.1162/1063656053583469.

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Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. In this paper we explain the relationship of EDA to algorithms developed in statistics, artificial intelligence, and statistical physics. The major design issues are discussed within a general interdisciplinary framework. It is shown that maximum entropy approximations play a crucial role. All proposed algorithms try to minimize the Kullback-Leibler divergence KLD between the unknown distribution p(x) and a class q(x) of approximations. However, the Kullback-Leibler divergence is not symmetric

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38

Ay, Ahmet, Dihong Gong, and Tamer Kahveci. "Network-based Prediction of Cancer under Genetic Storm." Cancer Informatics 13s3 (January 2014): CIN.S14025. http://dx.doi.org/10.4137/cin.s14025.

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Classification of cancer patients using traditional methods is a challenging task in the medical practice. Owing to rapid advances in microarray technologies, currently expression levels of thousands of genes from individual cancer patients can be measured. The classification of cancer patients by supervised statistical learning algorithms using the gene expression datasets provides an alternative to the traditional methods. Here we present a new network-based supervised classification technique, namely the NBC method. We compare NBC to five traditional classification techniques (support vecto

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39

Huang, Chien-Feng, Tsung-Nan Hsieh, Bao Rong Chang, and Chih-Hsiang Chang. "A study of risk-adjusted stock selection models using genetic algorithms." Engineering Computations 31, no. 8 (October 28, 2014): 1720–31. http://dx.doi.org/10.1108/ec-11-2012-0293.

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Purpose – Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively. Design/methodology/approach – The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and s

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40

Camacho, Francy Liliana, Rodrigo Torres-Sáez, and Raúl Ramos-Pollán. "Assessing the behavior of machine learning methods to predict the activity of antimicrobial peptides." Revista Facultad de Ingeniería 26, no. 44 (December 31, 2016): 167. http://dx.doi.org/10.19053/01211129.v26.n44.2017.5834.

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This study demonstrates the importance of obtaining statistically stable results when using machine learning methods to predict the activity of antimicrobial peptides, due to the cost and complexity of the chemical processes involved in cases where datasets are particularly small (less than a few hundred instances). Like in other fields with similar problems, this results in large variability in the performance of predictive models, hindering any attempt to transfer them to lab practice. Rather than targeting good peak performance obtained from very particular experimental setups, as reported

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41

Leal, José, and Teresa Costa. "Tuning a semantic relatedness algorithm using a multiscale approach." Computer Science and Information Systems 12, no. 2 (2015): 635–54. http://dx.doi.org/10.2298/csis140905020l.

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The research presented in this paper builds on previous work that lead to the definition of a family of semantic relatedness algorithms. These algorithms depend on a semantic graph and on a set of weights assigned to each type of arcs in the graph. The current objective of this research is to automatically tune the weights for a given graph in order to increase the proximity quality. The quality of a semantic relatedness method is usually measured against a benchmark data set. The results produced by a method are compared with those on the benchmark using a nonparametric measure of statistical

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42

Zhang, Zuoquan, Fan Lang, and Qin Zhao. "Research of Financial Early-Warning Model on Evolutionary Support Vector Machines Based on Genetic Algorithms." Discrete Dynamics in Nature and Society 2009 (2009): 1–8. http://dx.doi.org/10.1155/2009/830572.

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A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines (SVMs) have been applied to the problem of financial early-warning prediction (Rose, 1999). The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the kernel function which influence the result and performance of support vector machines have not been decided. Based on genetic algorithms, this paper proposes a new scientific method to automatically s

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43

Kumar, Adarsh, Saurabh Jain, and Divakar Yadav. "A novel simulation-annealing enabled ranking and scaling statistical simulation constrained optimization algorithm for Internet-of-things (IoTs)." Smart and Sustainable Built Environment 9, no. 4 (March 6, 2020): 675–93. http://dx.doi.org/10.1108/sasbe-06-2019-0073.

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PurposeSimulation-based optimization is a decision-making tool for identifying an optimal design of a system. Here, optimal design means a smart system with sensing, computing and control capabilities with improved efficiency. As compared to testing the physical prototype, computer-based simulation provides much cheaper, faster and lesser time-and resource-consuming solutions. In this work, a comparative analysis of heuristic simulation optimization methods (genetic algorithms, evolutionary strategies, simulated annealing, tabu search and simplex search) is performed.Design/methodology/approac

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Kolesnikov, A. V., and O. P. Fedorov. "SYSTEM OF THE COMPLICATED PRACTICAL PROBLEMS ANALYSIS." Mathematical Modelling and Analysis 7, no. 1 (June 30, 2002): 83–92. http://dx.doi.org/10.3846/13926292.2002.9637181.

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The original methodology of the system analysis of the inhomogeneous problem is offered, including stages of its reducing to homogeneous parts and selecting for them appropriate toolkits: methods and models. This system applies the accumulated knowledge and the experts skills to refer of each homogeneous problem to one or several alternative classes of modelling methods: analytical methods, statistical methods, artificial neuronets, knowledge based systems, fuzzy systems, genetic algorithms. The knowledge base testing has shown sufficiency and consistency of knowledge for realization of the in

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45

Korkmaz, Nimet, İsmail Öztürk, Adem Kalinli, and Recai Kiliç. "A Comparative Study on Determining Nonlinear Function Parameters of the Izhikevich Neuron Model." Journal of Circuits, Systems and Computers 27, no. 10 (May 24, 2018): 1850164. http://dx.doi.org/10.1142/s0218126618501645.

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In the literature, the parabolic function of the Izhikevich Neuron Model (IzNM) is transformed to the Piecewise Linear (PWL) functions in order to make digital hardware implementations easier. The coefficients in this PWL functions are identified by utilizing the error-prone classical step size method. In this paper, it is aimed to determine the coefficients of the PWL functions in the modified IzNM by using the stochastic optimization methods. In order to obtain more accurate results, Genetic Algorithm and Artificial Bee Colony Algorithm (GA and ABC) are used as alternative estimation methods

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46

Kumari, Madhulata, Neeraj Tiwari, and Naidu Subbarao. "A genetic programming-based approach to identify potential inhibitors of serine protease of Mycobacterium tuberculosis." Future Medicinal Chemistry 12, no. 2 (January 2020): 147–59. http://dx.doi.org/10.4155/fmc-2018-0560.

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Aim: We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of Mycobacterium tuberculosis ( Mtb) and the discovery of new inhibitors as drug candidates. Materials & methods: The experimental dataset of serine protease inhibitors of Mtb descriptors was optimized by genetic algorithm (GA) along with the correlation-based feature selection (CFS) in order to develop predictive models using machine-learning algorithms. The best model was deployed on a library of 918 phytochemical compounds to screen potential serine

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47

Wang, Chun, Zhicheng Ji, and Yan Wang. "Many-objective flexible job shop scheduling using NSGA-III combined with multi-attribute decision making." Modern Physics Letters B 32, no. 34n36 (December 30, 2018): 1840110. http://dx.doi.org/10.1142/s0217984918401103.

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This paper considers many-objective flexible job shop scheduling problem (MaOFJSP) in which the number of optimization problems is larger than three. An integrated multi-objective optimization method is proposed which contains both optimization and decision making. The non-dominated sorting genetic algorithm III (NSGA-III) is utilized to find a trade-off solution set by simultaneously optimizing six objectives including makespan, workload balance, mean of earliness and tardiness, cost, quality, and energy consumption. Then, an integrated multi-attribute decision-making method is introduced to

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48

Elston, Robert C. "An Accidental Genetic Epidemiologist." Annual Review of Genomics and Human Genetics 21, no. 1 (August 31, 2020): 15–36. http://dx.doi.org/10.1146/annurev-genom-103119-125052.

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I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston–Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction—statistical and biological—are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is

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49

Schaffer, Jesse D., Paul J. Roebber, and Clark Evans. "Development and Evaluation of an Evolutionary Programming-Based Tropical Cyclone Intensity Model." Monthly Weather Review 148, no. 5 (April 15, 2020): 1951–70. http://dx.doi.org/10.1175/mwr-d-19-0346.1.

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Abstract A statistical–dynamical tropical cyclone (TC) intensity model is developed from a large ensemble of algorithms through evolutionary programming (EP). EP mimics the evolutionary principles of genetic information, reproduction, and mutation to develop a population of algorithms with skillful predictor combinations. From this evolutionary process the 100 most skillful algorithms as determined by root-mean square error on validation data are kept and bias corrected. Bayesian model combination is used to assign weights to a subset of 10 skillful yet diverse algorithms from this list. The r

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50

Frost, Volker J., and Karl Molt. "Use of a Genetic Algorithm for Factor Selection in Principal Component Regression." Journal of Near Infrared Spectroscopy 6, A (January 1998): A185—A190. http://dx.doi.org/10.1255/jnirs.192.

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The critical point in the development of principal component regression (PCR) calibration programs is the automatic factor selection step. In classical methods this is based on a differentiation between primary and secondary factors and other statistical assumptions and criteria. In contrast to this the Genetic Algorithm (GA) used for factor selection in this paper finds the optimal combination of factors without statistical constraints beyond an appropriately chosen fitness function.

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