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Journal articles on the topic 'Genetic selection methods'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Kang, K. S., B. H. Cheon, S. U. Han, C. S. Kim, and W. Y. Choi. "Genetic Gain and Diversity under Different Selection Methods in a Breeding Seed Orchard of Quercus serrata." Silvae Genetica 56, no. 1-6 (2007): 277–81. http://dx.doi.org/10.1515/sg-2007-0039.

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Abstract Genetic gain and diversity were estimated in a 13- year old Quercus serrata breeding seed orchard under three selection (rouging) methods. The selections were based on individual selection, family selection, and family plus within family selection. Genetic gain was for stem volume and gene diversity was estimated by status number concept. Both estimated genetic gain and gene diversity were compared to those before selection and among selection scenarios. Estimated genetic gain for tree volume ranged from 4.0% to 9.1% for three selection methods under 50% selection intensity. Individua

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Řepková, J., and J. Nedělník. "Modern methods for genetic improvement of Trifolium pratense." Czech Journal of Genetics and Plant Breeding 50, No. 2 (2014): 92–99. http://dx.doi.org/10.17221/139/2013-cjgpb.

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This review focuses on trends in genetic improvement of a significant representative forage crop, Trifolium pratense (red clover) classified taxonomically into the agronomically outstanding family Fabaceae. Red clover breeding is aimed at improving traits like persistency, resistance to biotic and abiotic factors, forage yield and quality characteristics such as protein quality and stability. Isoflavone content in forage is important for cattle reproduction. Interspecific hybridization of red clover with the related wild species T. medium was used for the introgression of useful traits into re

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Gulayeva, Nataliya, and Artem Ustilov. "Analysis of Selection Methods Used in Genetic Algorithms." NaUKMA Research Papers. Computer Science 4 (December 10, 2021): 29–43. http://dx.doi.org/10.18523/2617-3808.2021.4.29-43.

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This paper offers a comprehensive review of selection methods used in the generational genetic algorithms.Firstly, a brief description of the following selection methods is presented: fitness proportionate selection methods including roulette-wheel selection (RWS) and its modifications, stochastic remainder selection with replacement (SRSWR), remainder stochastic independent selection (RSIS), and stochastic universal selection (SUS); ranking selection methods including linear and nonlinear rankings; tournament selection methods including deterministic and stochastic tournaments as well as tour

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Huspi, Sharin Hazlin, and Chong Ke Ting. "Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification." International Journal of Innovative Computing 11, no. 2 (2021): 73–80. http://dx.doi.org/10.11113/ijic.v11n2.345.

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Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that s

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Pavlidis, Pavlos, and Nikolaos Alachiotis. "A survey of methods and tools to detect recent and strong positive selection." Journal of Biological Research-Thessaloniki 24, no. 1 (2017): 7. https://doi.org/10.1186/s40709-017-0064-0.

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Positive selection occurs when an allele is favored by natural selection. The frequency of the favored allele increases in the population and due to genetic hitchhiking the neighboring linked variation diminishes, creating so-called selective sweeps. Detecting traces of positive selection in genomes is achieved by searching for signatures introduced by selective sweeps, such as regions of reduced variation, a specific shift of the site frequency spectrum, and particular LD patterns in the region. A variety of methods and tools can be used for detecting sweeps, ranging from simple implementatio

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Jameel, Noor, and Hasanen S. Abdullah. "Intelligent Feature Selection Methods: A Survey." Engineering and Technology Journal 39, no. 1B (2021): 175–83. http://dx.doi.org/10.30684/etj.v39i1b.1623.

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Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish al

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Wei, R. P., C. R. Hansen, N. K. Dhir, and F. C. Yeh. "Genetic gain with desired status number in breeding programs: a study on selection effects." Canadian Journal of Forest Research 28, no. 12 (1998): 1861–69. http://dx.doi.org/10.1139/x98-168.

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Genetic gain and average coancestry or status number was investigated for five selection methods: penalty index selection (PIS), family index selection (FIS), combined between-family and within-family selection (CBW), restricted individual selection (RIS), and combined index selection (RCS). PIS was a function of an individual's breeding value and family contributions, modelled as a stepwise procedure to select superior individuals one by one. A penalty would indicate the need to have low average coancestry or large status number. Breeding populations of unrelated families were investigated by

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Moeinizade, Saba, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable. "Multi-trait Genomic Selection Methods for Crop Improvement." Genetics 215, no. 4 (2020): 931–45. http://dx.doi.org/10.1534/genetics.120.303305.

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Plant breeders make selection decisions based on multiple traits, such as yield, plant height, flowering time, and disease resistance. A commonly used approach in multi-trait genomic selection is index selection, which assigns weights to different traits relative to their economic importance. However, classical index selection only optimizes genetic gain in the next generation, requires some experimentation to find weights that lead to desired outcomes, and has difficulty optimizing nonlinear breeding objectives. Multi-objective optimization has also been used to identify the Pareto frontier o

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Ma, Ling Yong, Bing Xin Gu, and Gong Liang Liu. "A Study on High-Rise Building Structure Selections Using Artificial Intelligence Methods." Applied Mechanics and Materials 351-352 (August 2013): 1198–201. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.1198.

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With the increasing height of building, high-rise building structure selection becomes more and more important. This paper presents the application of genetic neural network method to study high-rise building structure selection and uses the MATLAB neural network toolbox with a combination of genetic algorithm toolbox to develop a genetic neural network expert system for high-rise building structure selection to make the selection process simple.

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Jia, Yi, and Jean-Luc Jannink. "Multiple-Trait Genomic Selection Methods Increase Genetic Value Prediction Accuracy." Genetics 192, no. 4 (2012): 1513–22. http://dx.doi.org/10.1534/genetics.112.144246.

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Kaskinova, M. D., A. M. Salikhova, L. R. Gaifullina, and E. S. Saltykova. "Genetic methods in honey bee breeding." Vavilov Journal of Genetics and Breeding 27, no. 4 (2023): 366–72. http://dx.doi.org/10.18699/vjgb-23-44.

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The honey bee Apis mellifera is a rather difficult object for selection due to the peculiarities of its biology. Breeding activities in beekeeping are aimed at obtaining bee colonies with high rates of economically useful traits, such as productivity, resistance to low temperatures and diseases, hygienic behavior, oviposition of the queen, etc. With two apiaries specializing in the breeding of A. m. mellifera and A. m. carnica as examples, the application of genetic methods in the selection of honey bees is considered. The first stage of the work was subspecies identification based on the anal

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Latifi, Meysam, Amir Rashidi, Rostam Abdollahi-Arpanahi, and Mohammad Razmkabir. "Comparison of different selection methods for improving litter size in sheep using computer simulation." Spanish Journal of Agricultural Research 18, no. 1 (2020): e0403. http://dx.doi.org/10.5424/sjar/2020181-15459.

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Aim of study: To assess selection methods via introgression to improve litter size in native and synthetic sheep breeds.Area of study: Sanandaj, Kurdistan, Iran.Material and methods: Selection approaches were performed using classical, genomic, gene-assisted classical (GasClassical) and gene-assisted genomic (GasGenomic) selection. Litter size trait with heritability of 0.1 including two chromosomes was simulated. On chromosome 1, a single QTL as the major gene was created to explain 40% of the total additive genetic variance. After simulation of a historical population, the animals from the l

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Kandel, Rupak, Ishwari Prasad Kadariya, Kailash Bohara, and Sonu Adhikari. "A review on molecular breeding techniques: Crucial approach in livestock improvement." Archives of Agriculture and Environmental Science 8, no. 4 (2023): 639–51. http://dx.doi.org/10.26832/24566632.2023.0804027.

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For underdeveloped countries, molecular breeding (MB) has a lot of promise. However, the implementation in developing countries is far from uniform. Livestock improvement programs aim to improve the genetics of domesticated animal populations by selecting males and females who, when mated, will produce progeny that perform better than the current generation's average. The amount of genetic progress made through conventional selection and breeding methods for quantitative traits in livestock is successful, but limitations such as routinely recording phenotypes, animal sacrifice for meat quality

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Vanavermaete, David, Jan Fostier, Steven Maenhout, and Bernard De Baets. "Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain." G3: Genes|Genomes|Genetics 10, no. 8 (2020): 2753–62. http://dx.doi.org/10.1534/g3.120.401354.

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Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and deliver

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Et.al, Chetan J. Shingadiya. "Genetic Algorithm for Test Suite Optimization: An Experimental Investigation of Different Selection Methods." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 3778–87. http://dx.doi.org/10.17762/turcomat.v12i3.1661.

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Software Testing is an important aspect of the real time software development process. Software testing always assures the quality of software product. As associated with software testing, there are few very important issues where there is a need to pay attention on it in the process of software development test. These issues are generation of effective test case and test suite as well as optimization of test case and suite while doing testing of software product. The important issue is that testing time of the test case and test suite. It is very much important that after development of softw

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Sun, Jingyi. "Application and Challenges of Statistical Methods in Biological Genetics." Highlights in Science, Engineering and Technology 40 (March 29, 2023): 43–49. http://dx.doi.org/10.54097/hset.v40i.6519.

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Humans are curious about genes, from plants to animals, from breeding to diseases. For centuries, it has been considered a genetic disease. With the development of medicine, people have also realized that many diseases are heritable. With the birth of modern statistics, humans have created many models. This article focuses on the application of statistical methods in biological genetics. This paper introduces the principles and their applications of Least Absolute Shrinkage and Selection Operator Regression, the Chen-Stein Method, and Logical Regression model in different branches, such as gen

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Kowalczyk, Marek, Agnieszka Kaliniak-Dziura, Michał Prasow, Piotr Domaradzki, and Anna Litwińczuk. "Meat quality – Genetic background and methods of its analysis." Czech Journal of Food Sciences 40, No. 1 (2022): 15–25. http://dx.doi.org/10.17221/255/2020-cjfs.

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Growing consumer awareness is forcing food producers to supply raw material and products of increasingly high quality and health-promoting properties. Knowledge of the genetic background of quality characteristics is taking on great importance, enabling selection based on molecular markers. The increasing throughput of molecular techniques, in combination with an expanding bioinformatics infrastructure, is leading to continual improvement in understanding of the molecular mechanisms influencing meat quality. This has resulted in the identification of polymorphic nucleotides [single nucleotide

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Naroui Rad, Mohammad Reza. "Melon Selection for Breeding Based on Traits and Diversity." Current Agriculture Research Journal 10, no. 2 (2022): 39–45. http://dx.doi.org/10.12944/carj.10.2.01.

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Genetic improvement of vegetables like melons needs information on its phenotypic diversity and so on. To choose the appropriate breeding strategies to fulfill the goal of breeding, information on genetics and genetic resources is essential. Information on genetic diversity, genetic resource and types of breeding of vegetable crops is helpful for breeders. Based on the targets of breeding, improving of melon by scientific methods will result in useful varieties or hybrids. Increasing genetic diversity is vital for the production of hybrid seeds. Hence, determining the distribution of genetic r

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Falkenhagen, Emile R., and Jean D. Gibbons. "Selecting Populations in Tree Breeding: An Alternative to Multiple Comparisons." Forest Science 35, no. 2 (1989): 425–36. http://dx.doi.org/10.1093/forestscience/35.2.425.

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Abstract This paper explains the indifference zone and subset selection methods of selecting populations. We recommend them as preferable to pairwise multiple comparisons for selecting the best genetic material in tree breeding, especially when a large number of groups with small differences must be tested. Subset selection permits the data to determine the number of groups designated as including the best genetic material with preassigned probability. Numerical examples of selection procedures are given. For. Sci. 35(2):425-436.

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Fridley, Brooke L. "Bayesian variable and model selection methods for genetic association studies." Genetic Epidemiology 33, no. 1 (2009): 27–37. http://dx.doi.org/10.1002/gepi.20353.

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Lācis, Gunārs. "Characterisation of Latvia Fruit Crop Genetic Resources by Application of Molecular Genetics Methods." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences 67, no. 2 (2013): 84–93. http://dx.doi.org/10.2478/prolas-2013-0014.

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A large diversity of fruit crop accessions is maintained at the Latvia State Institute of Fruit- Growing, which consists of modern cultivars, landraces and selections from local breeding programmes, as well as germplasm that has resulted from scientific exchange and co-operation with other institutes. Presently, the germplasm collection comprises 2509 accessions of 17 fruit crops; 676 accessions are designated as national genetic resources. Conservation of germplasm itself has little value without characterisation and further utilisation of the stored plant material. To intensify these activit

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Bakker, Theo C. M. "THE STUDY OF INTERSEXUAL SELECTION USING QUANTITATIVE GENETICS." Behaviour 136, no. 9 (1999): 1237–66. http://dx.doi.org/10.1163/156853999501748.

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AbstractIn this review, I stress the importance of incorporating Quantitative Genetics (QG) in the study of sexual selection through female mate choice. A short overview of QG principles and methods of estimating genetic variance and covariance is given. The state of knowledge is summarized as to two QG assumptions (genetic variance in female mating preferences and male sexual traits) and one QG prediction (genetic covariance between preferences and preferred traits) of models of sexual selection. A review is given of studies of repeatability of mating preferences because of recent accumulatio

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Pemberton, Josephine M. "Evolution of quantitative traits in the wild: mind the ecology." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1552 (2010): 2431–38. http://dx.doi.org/10.1098/rstb.2010.0108.

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Recent advances in the quantitative genetics of traits in wild animal populations have created new interest in whether natural selection, and genetic response to it, can be detected within long-term ecological studies. However, such studies have re-emphasized the fact that ecological heterogeneity can confound our ability to infer selection on genetic variation and detect a population's response to selection by conventional quantitative genetics approaches. Here, I highlight three manifestations of this issue: counter gradient variation, environmentally induced covariance between traits and th

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Austen, Emily J., and Arthur E. Weis. "Estimating selection through male fitness: three complementary methods illuminate the nature and causes of selection on flowering time." Proceedings of the Royal Society B: Biological Sciences 283, no. 1825 (2016): 20152635. http://dx.doi.org/10.1098/rspb.2015.2635.

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Our understanding of selection through male fitness is limited by the resource demands and indirect nature of the best available genetic techniques. Applying complementary, independent approaches to this problem can help clarify evolution through male function. We applied three methods to estimate selection on flowering time through male fitness in experimental populations of the annual plant Brassica rapa : (i) an analysis of mating opportunity based on flower production schedules, (ii) genetic paternity analysis, and (iii) a novel approach based on principles of experimental evolution. Selec

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Yaekoblorato L and MerhunLamaro L. "Review on trends of Selection superior Dairy cattle through marker assisted selection methods." International Journal of Scholarly Research in Biology and Pharmacy 1, no. 1 (2022): 033–40. http://dx.doi.org/10.56781/ijsrbp.2022.1.1.0022.

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Marker-Assisted Selection is selection that used for indirect selection of superior breeding animals that depend on identifying association between genetic marker and linked quantitative traits loci. Since the association between marker and quantitative traits loci depends on distance between marker and target traits. As soon as markers linked to quantitative traits loci have been identified, they can be used in selection programme of dairy cattle that is beneficial when the traits are difficult and expensive to measure and low heritability and recessive traits in dairy cattle. Therefore, Mark

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Hamad, Zana O. "REVIEW OF FEATURE SELECTION METHODS USING OPTIMIZATION ALGORITHM." Polytechnic Journal 12, no. 2 (2023): 203–14. http://dx.doi.org/10.25156/ptj.v12n2y2022.pp203-214.

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Many works have been done to reduce complexity in terms of time and memory space. The feature selection process is one of the strategies to reduce system complexity and can be defined as a process of selecting the most important feature among feature space. Therefore, the most useful features will be kept, and the less useful features will be eliminated. In the fault classification and diagnosis field, feature selection plays an important role in reducing dimensionality and sometimes might lead to having a high classification rate. In this paper, a comprehensive review is presented about featu

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Luque-Rodriguez, Maria, Jose Molina-Baena, Alfonso Jimenez-Vilchez, and Antonio Arauzo-Azofra. "Initialization of Feature Selection Search for Classification." Journal of Artificial Intelligence Research 75 (November 27, 2022): 953–83. http://dx.doi.org/10.1613/jair.1.14015.

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Selecting the best features in a dataset improves accuracy and efficiency of classifiers in a learning process. Datasets generally have more features than necessary, some of them being irrelevant or redundant to others. For this reason, numerous feature selection methods have been developed, in which different evaluation functions and measures are applied. This paper proposes the systematic application of individual feature evaluation methods to initialize search-based feature subset selection methods. An exhaustive review of the starting methods used by genetic algorithms from 2014 to 2020 ha

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SIEDLECKI, WOJCIECH, and JACK SKLANSKY. "ON AUTOMATIC FEATURE SELECTION." International Journal of Pattern Recognition and Artificial Intelligence 02, no. 02 (1988): 197–220. http://dx.doi.org/10.1142/s0218001488000145.

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We review recent research on methods for selecting features for multidimensional pattern classification. These methods include nonmonotonicity-tolerant branch-and-bound search and beam search. We describe the potential benefits of Monte Carlo approaches such as simulated annealing and genetic algorithms. We compare these methods to facilitate the planning of future research on feature selection.

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Harahap, Antoni, Teuku Fadlon Haser, Suri Purnama Febri, and Darsiani Darsiani. "Fish Selection Based on DNA Markers: Literature Review." Jurnal Ilmiah Samudra Akuatika 6, no. 1 (2022): 59–66. http://dx.doi.org/10.33059/jisa.v6i1.8321.

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Selection of fish based on DNA markers is a method or technique that has started to develop rapidly in the field of genetics and fish breeding. Selection based on DNA markers utilizes the genetic information contained in fish DNA to obtain individuals with characteristics appropriate to the stages and production of aquaculture in a timely, efficient, and measurable manner. This literature review presents several discussions and literature sources that are quite relevant regarding various aspects of DNA marker-based fish selection, including the basic principles, analytical methods, benefits, a

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Engel, Mara Luana, Antônio Rioyei Higa, Gisela Pedrassani Andrejow, Paulo César Flôres Junior, and Izabele Domingues Soares. "GENETIC GAIN FROM DIFFERENT SELECTION METHODS IN Eucalyptus macarthurii PROGENIES IN DIFFERENT ENVIRONMENTS." CERNE 22, no. 3 (2016): 299–308. http://dx.doi.org/10.1590/01047760201622032197.

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ABSTRACT The aims of this research was to estimate genetic parameters of Eucalyptus macarthurii progenies and to predict genetic gain for different selection methods. In order to evaluate the gains, eleven progenies tests were studied. These tests were installed in 1997, in randomized blocks design, in two sites, with 5 replicates of five plants per plot. The progenies were evaluated, from the first to the eighth year, regarding the variable diameter at breast height (DBH). The genetic parameters were estimated using the mixed model procedure (REML/BLUP). The selection of the progenies for est

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Zas, Rafael. "The impact of spatial heterogeneity on selection: a case study on Pinus pinaster breeding seedling orchards." Canadian Journal of Forest Research 38, no. 1 (2008): 114–24. http://dx.doi.org/10.1139/x07-099.

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Although failure to account for spatial autocorrelation has been dramatic in some forest progeny trials, little attention has been paid to how this issue may affect selections within the trials. The effects of spatial autocorrelation of height growth on the estimation of genetic gain and on the spatial distribution of the selected trees were studied in four Pinus pinaster Ait. progeny trials that were rogued using different selection methods and intensities. When selections are based on unadjusted original values, selected trees tend to be located in the best microsites and are unlikely to be

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Martí, Luis, Eduardo Segredo, Nayat Sánchez-Pi, and Emma Hart. "Selection methods and diversity preservation in many-objective evolutionary algorithms." Data Technologies and Applications 52, no. 4 (2018): 502–19. http://dx.doi.org/10.1108/dta-01-2018-0009.

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Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, whic

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Visscher, P. M., and C. S. Haley. "On the efficiency of marker-assisted introgression." Animal Science 68, no. 1 (1999): 59–68. http://dx.doi.org/10.1017/s1357729800050086.

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AbstractThe efficiency of marker-assisted introgression programmes, expressed as genetic lag relative to a commercial population under continuous selection, was investigated using analytical methods. A genetic model was assumed for which the genetic variance in the introgression population was a function of the within-breed genetic variance and the initial breed difference. It was found that most of the genetic lag occurs in the latter stages of an introgression programme, when males and females which are heterozygous for the alíele to be introgressed are mated to produce homozygous individual

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Hill, William G. "Understanding and using quantitative genetic variation." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1537 (2010): 73–85. http://dx.doi.org/10.1098/rstb.2009.0203.

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Quantitative genetics, or the genetics of complex traits, is the study of those characters which are not affected by the action of just a few major genes. Its basis is in statistical models and methodology, albeit based on many strong assumptions. While these are formally unrealistic, methods work. Analyses using dense molecular markers are greatly increasing information about the architecture of these traits, but while some genes of large effect are found, even many dozens of genes do not explain all the variation. Hence, new methods of prediction of merit in breeding programmes are again bas

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Sartori, Maria Márcia Pereira, Jackson da Silva, and Mauricio Dutra Zanotto. "Comparison of methods for selection of castor beans lineages." Comunicata Scientiae 9, no. 4 (2019): 687–94. http://dx.doi.org/10.14295/cs.v9i4.2970.

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The choice of the most appropriate method is determined by the precision desired by the researcher, by the ease of the analysis, as well as by the way of obtaining the data. In order to select lineages of low size and high productivity this study aimed to evaluate different methods of cluster analysis in the representation of genetic divergence, compared to univariate methods. The analyzed variables were grain yield, plant size and oil yield of 24 lineages of castor beans cultivated in the years 2014 and 2015. The Single and Average methods presented similar results in the formation of groups

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Widyatmoko, A. Y.P.B.C., S. Shiraishi, A. Nirsatmanto, and H. Kawazaki. "THE EFFECT OF INDIVIDUAL SELECTION FOR GENETIC DIVERSITY OF Acacia mangium SEEDLING SEED ORCHARD USING AFLP MARKERS." JOURNAL OF FORESTRY RESEARCH 3, no. 2 (2006): 75–81. https://doi.org/10.20886/ijfr.2006.3.2.75-81.

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Establishment of seed orchard is aimed at producing good quality seeds which is an important activity for breeding program. Seed orchard is also a base population, thus its genetic diversity is depending on its design and composition (provenance, family and individual tree). Selection of an individual tree in seed orchard is needed for the enhancement of  retaining good-character trees. However, selection of individual tree can change the genetic diversity of seed orchard, and the degrees to which the genetic diversity will change depend on the used selection methods. In order to investig

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Babenko, O. I., V. P. Оleshko, and V. Y. Afanasenko. "THE PREDICTED GENETIC PROGRESS IN DAIRY CATTLE POPULATIONS USING A VARIETY OF METHODS FOR EVALUATION AND SELECTION OF ANIMALS." Animal Breeding and Genetics 51 (March 28, 2018): 27–34. http://dx.doi.org/10.31073/abg.51.04.

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Genetic progress in a herd of animals is due to the selection of four categories of pedigree animals: fathers of sires, mothers of sires, fathers of cows and mothers of cows. Extremely important role in genetic improvement of a herd plays selection of the sires for insemination of breeding stock which become the potential parents of cows. The selection of sires’ mothers, sires’ and cows’ parents provides 90-95% of the effect of selection in animal population, and massive selection of cows’ mothers only 5-10%. The main selection trait in а herd is milk production, therefore with the purpose of

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Goodarzi, Mohammad, Bieke Dejaegher, and Yvan Vander Heyden. "Feature Selection Methods in QSAR Studies." Journal of AOAC INTERNATIONAL 95, no. 3 (2012): 636–51. http://dx.doi.org/10.5740/jaoacint.sge_goodarzi.

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Abstract A quantitative structure-activity relationship (QSAR) relates quantitative chemical structure attributes (molecular descriptors) to a biological activity. QSAR studies have now become attractive in drug discovery and development because their application can save substantial time and human resources. Several parameters are important in the prediction ability of a QSAR model. On the one hand, different statistical methods may be applied to check the linear or nonlinear behavior of a data set. On the other hand, feature selection techniques are applied to decrease the model complexity,

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Wijsman, Ellen M. "Monte Carlo Markov chain methods and model selection in Genetic analysis." Animal Biotechnology 8, no. 1 (1997): 129–44. http://dx.doi.org/10.1080/10495399709525875.

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Wijsman, Ellen M. "Monte Carlo Markov chain methods and model selection in genetic epidemiology." Computational Statistics & Data Analysis 32, no. 3-4 (2000): 349–60. http://dx.doi.org/10.1016/s0167-9473(99)00088-2.

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Uphoff, Michael D., Walter R. Fehr, and Silvia R. Cianzio. "Genetic Gain for Soybean Seed Yield by Three Recurrent Selection Methods." Crop Science 37, no. 4 (1997): 1155–58. http://dx.doi.org/10.2135/cropsci1997.0011183x003700040021x.

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Brizgalov, G. Ya. "MOLECULAR-GENETIC METHODS IN SELECTION OF CHUKCHI REINDEER BREED (RANGIFER TARANDUS)." Theoretical & applied problems of agro-industry 34, no. 1 (2018): 26–31. http://dx.doi.org/10.32935/2221-7312-2018-34-1-26-31.

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Bank, Claudia, Gregory B. Ewing, Anna Ferrer-Admettla, Matthieu Foll, and Jeffrey D. Jensen. "Thinking too positive? Revisiting current methods of population genetic selection inference." Trends in Genetics 30, no. 12 (2014): 540–46. http://dx.doi.org/10.1016/j.tig.2014.09.010.

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EFUNBOADE, Ayodeji Oyeyinka, Olubunmi Rotimi OYENIRAN, and Olufemi Ayodeji ODENIYI. "Performance Evaluation of Genetic Algorithm Selection Methods in Outlier Detection: Further Analysis." International Journal of Mathematics And Computer Research 10, no. 06 (2022): 2701–4. https://doi.org/10.5281/zenodo.6606704.

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Feature selection is very crucial in the activities of soft computing algorithms for quality, precision  and  accuracy.  This  paper  evaluates  the  performance  of  some  feature  selection  methods  of  Genetic  Algorithm in outlier detection on fingerprint images. Roulette wheel, Rank and Tournament methods  were   considered   for   feature   selection   and   selected   features   were   enhanced   using &

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Sullivan, P. G. "Alternatives for genetic evaluation with uncertain parentage." Canadian Journal of Animal Science 75, no. 1 (1995): 31–36. http://dx.doi.org/10.4141/cjas95-004.

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Alternative methods of genetic evaluation which consider uncertain paternity were compared theoretically and through Monte Carlo simulation. Records were simulated for 300 base generation animals and 10 subsequent generations of 100 animals each. Probabilities of paternal uncertainty were either 20 or 50%, heritability was 0.05, 0.25 or 0.50, mating was random with a female-to-male ratio of 5, and selection of breeding animals was either random or by truncation on phenotype. Simulations were replicated 10 times. Differences in expected selection response, for the genetic evaluation methods stu

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He, Xiao Dong, Hai Ying Li, Jian Wu Wu, and Xiao Jian Liu. "Genetic Algorithm Based Product Innovation Design Methods and its Application in Bag Design." Applied Mechanics and Materials 34-35 (October 2010): 691–95. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.691.

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Based on an interactive genetic algorithm implemented the bags innovation design and optimization, a structure that contains 10 operators system integrity algorithm is built, including individual programs, constraints structure, species maintenance, individual evaluation, selection, restructuring, variation, optimization control, decoding and interactive selection. The bags visualization species have been constructed, and the optimization methods can be realized through crossover and mutation genetic manipulations. The experimental results confirmed the effectiveness.

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Lee, Jaehyeong, Hyuk Jang, Sungmin Ha, and Yourim Yoon. "Android Malware Detection Using Machine Learning with Feature Selection Based on the Genetic Algorithm." Mathematics 9, no. 21 (2021): 2813. http://dx.doi.org/10.3390/math9212813.

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Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We a

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Yu, Yuanyuan, Lei Hou, Xu Shi, et al. "Impact of nonrandom selection mechanisms on the causal effect estimation for two-sample Mendelian randomization methods." PLOS Genetics 18, no. 3 (2022): e1010107. http://dx.doi.org/10.1371/journal.pgen.1010107.

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Nonrandom selection in one-sample Mendelian Randomization (MR) results in biased estimates and inflated type I error rates only when the selection effects are sufficiently large. In two-sample MR, the different selection mechanisms in two samples may more seriously affect the causal effect estimation. Firstly, we propose sufficient conditions for causal effect invariance under different selection mechanisms using two-sample MR methods. In the simulation study, we consider 49 possible selection mechanisms in two-sample MR, which depend on genetic variants (G), exposures (X), outcomes (Y) and th

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Silva, Fabiana Mota da, Elise De Matos Pereira, Bruno Henrique Pedroso Val, Dilermando Perecin, Antonio Orlando Di Mauro, and Sandra Helena Unêda-Trevisoli. "Strategies to select soybean segregating populations with the goal of improving agronomic traits." Acta Scientiarum. Agronomy 40, no. 1 (2018): 39324. http://dx.doi.org/10.4025/actasciagron.v40i1.39324.

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The success of breeding programs depends on selection procedures and on the breeding methods adopted for selecting segregating populations. The objective of this study was to evaluate the efficiency of the Bulk method with selection in the F3 generation (BulkF3) compared to that of Bulk method as well as determine the most effective selection strategy in terms of genetic gain. Twenty segregating populations were selected by two methods. The 60 best families of each method were selected according to their average agronomic performance. An augmented block design was used. The following agronomic

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Bain, Catherine, Dingjing Shi, Yaser Banad, Lauren Ethridge, Jordan Norris, and Jordan Loeffelman. "A Tutorial on Supervised Machine Learning Variable Selection Methods in Classification for the Social and Health Sciences in R." Journal of Behavioral Data Science 5, no. 1 (2025): 1–45. https://doi.org/10.35566/jbds/bain.

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With the increasing availability of large datasets in the behavioral and health sciences, the need for efficient and effective variable selection techniques has grown. While traditional methods like stepwise regression remain prevalent, numerous advanced techniques are available but underutilized in these fields. This tutorial aims to increase awareness and understanding of five variable selection methods available in the popular statistical software R: LASSO, Elastic Net, a penalized SVM classifier, random forest, and the genetic algorithm. Using a recent survey-based assessment dataset on mi

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