Thematische Bibliographien / Genetic

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Auswahl der wissenschaftlichen Literatur zum Thema „Genetic“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 26. Oktober 2024

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Zeitschriftenartikel zum Thema "Genetic"

Sumida, Brian. „Genetics for genetic algorithms“. ACM SIGBIO Newsletter 12, Nr. 2 (Juni 1992): 44–46. http://dx.doi.org/10.1145/130686.130694.

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Niendorf, Kristin Baker. „Genetic Library: Cancer Genetics“. Journal of Genetic Counseling 11, Nr. 5 (Oktober 2002): 429–34. http://dx.doi.org/10.1023/a:1016854001384.

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Comfort, Nathaniel. „Genetics: The genetic watchmaker“. Nature 502, Nr. 7472 (Oktober 2013): 436–37. http://dx.doi.org/10.1038/502436a.

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Clarke, Angus. „Genetic imprinting in clinical genetics“. Development 108, Supplement (01.04.1990): 131–39. http://dx.doi.org/10.1242/dev.108.supplement.131.

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Genetic, and indeed genomic, imprinting does occur in humans. This is manifest at the level of the genome, the individual chromosome, subchromosomal region or fragile site, or the single locus. The best evidence at the single gene level comes from a consideration of familial tumour syndromes. Chromosomal imprinting effects are revealed when uniparental disomy occurs, as in the Prader-Willi syndrome and doubtless other sporadic, congenital anomaly syndromes. Genomic imprinting is manifest in the developmental defects of hydatidiform mole, teratoma and triploidy. Fragile (X) mental retardation shows an unusual pattern of inheritance, and imprinting can account for these effects. Future work in clinical genetics may identify congenital anomalies and growth disorders caused by imprinting: the identification of imprinting effects for specific chromosomal regions in mice will allow the examination of the homologous chromosomal region in humans.

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Kallab, Chadi, Samir Haddad und Jinane Sayah. „Flexible Traceable Generic Genetic Algorithm“. Open Journal of Applied Sciences 12, Nr. 06 (2022): 877–91. http://dx.doi.org/10.4236/ojapps.2022.126060.

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Shanmugam, Ramalingam. „Biostatistical genetics and genetic epidemiology“. Journal of Statistical Computation and Simulation 73, Nr. 7 (Juli 2003): 543–44. http://dx.doi.org/10.1080/0094965021000044411.

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Siegel, PB, und EA Dunnington. „Genetic selection strategies–population genetics“. Poultry Science 76, Nr. 8 (August 1997): 1062–65. http://dx.doi.org/10.1093/ps/76.8.1062.

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Athanasiou, Y., M. Zavros, M. Arsali, L. Papazachariou, P. Demosthenous, I. Savva, K. Voskarides et al. „GENETIC DISEASES AND MOLECULAR GENETICS“. Nephrology Dialysis Transplantation 29, suppl 3 (01.05.2014): iii339—iii350. http://dx.doi.org/10.1093/ndt/gfu162.

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Ziegel, Eric R. „Biostatistical Genetics and Genetic Epidemiology“. Technometrics 44, Nr. 4 (November 2002): 409. http://dx.doi.org/10.1198/tech.2002.s98.

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Neville, Melvin, und Anaika Sibley. „Developing a generic genetic algorithm“. ACM SIGAda Ada Letters XXIII, Nr. 1 (März 2003): 45–52. http://dx.doi.org/10.1145/1066404.589462.

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Dissertationen zum Thema "Genetic"

KOSHIYAMA, ADRIANO SOARES. „GPFIS: A GENERIC GENETIC-FUZZY SYSTEM BASED ON GENETIC PROGRAMMING“. PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2014. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26560@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Sistemas Fuzzy-Genéticos compreendem uma área que une Sistemas de Inferência Fuzzy e Meta-Heurísticas prevalentes nos conceitos de seleção natural e recombinação genética. Esta é de grande interesse para a comunidade científica, pois propicia a descoberta de conhecimento em áreas onde a compreensão do fenômeno em estudo é exíguo, além de servir de apoio à decisão para gestores público-privados. O objetivo desta dissertação é desenvolver um novo Sistema Fuzzy-Genético Genérico, denominado Genetic Programming Fuzzy Inference System (GPFIS). O principal aspecto do modelo GPFIS são as componentes do seu processo de Inferência Fuzzy. Esta estrutura é composta em sua base pela Programação Genética Multigênica e pretende: (i ) possibilitar o uso de operadores de agregação, negação e modificadores linguísticos de forma simplificada; (ii ) empregar heurísticas de definição do consequente mais apropriado para uma parte antecedente; e (iii ) usar um procedimento de defuzzificação, que induzido pela forma de fuzzificação e sobre determinadas condições, pode proporcionar uma estimativa mais acurada. Todas estas são contribuições que podem ser estendidas a outros Sistemas Fuzzy-Genéticos. Para demonstrar o aspecto genérico, o desempenho e a importância de cada componente para o modelo proposto, são formuladas uma série de investigações empíricas. Cada investigação compreende um tipo de problema: Classificação, Previsão, Regressão e Controle. Para cada problema, a melhor configuração obtida durante as investigações é usada no modelo GPFIS e os resultados são comparados com os de outros Sistemas Fuzzy-Genéticos e modelos presentes na literatura. Por fim, para cada problema é apresentada uma aplicação detalhada do modelo GPFIS em um caso real.
Genetic Fuzzy Systems constitute an area that brings together Fuzzy Inference Systems and Meta-Heuristics that are often related to natural selection and genetic recombination. This area attracts great interest from the scientific community, due to the knowledge discovery capability in situations where the comprehension of the phenomenon under analysis is lacking. It can also provides support to decision makers. This dissertation aims at developing a new Generic Genetic Fuzzy System, called Genetic Programming Fuzzy Inference System (GPFIS). The main aspects of GPFIS model are the components which are part of its Fuzzy Inference procedure. This structure is basically composed of Multi-Gene Genetic Programming and intends to: (i ) apply aggregation operators, negation and linguistic hedges in a simple manner; (ii ) make use of heuristics to define the consequent term most appropriate to the antecedent part; (iii ) employ a defuzzification procedure that, driven by the fuzzification step and under some assumptions, can provide a most accurate estimate. All these features are contributions that can be extended to other Genetic Fuzzy Systems. In order to demonstrate the general aspect of GPFIS, its performance and the relevance of each of its components, several investigations have been performed. They deal with Classification, Forecasting, Regression and Control problems. By using the best configuration obtained for each of the four problems, results are compared to other Genetic Fuzzy Systems and models in the literature. Finally, applications of GPFIS actual cases in each category is reported.

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Rodas, Perez M. C. „Medical genetics in Colombia : genetic consultation and counselling in five genetic clinics“. Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/46980/.

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Today genetic services including genetic counselling are widespread across the world. Although developing countries, like Colombia, have started to apply genetic knowledge to the health area, genetic counselling is usually integrated in the routine clinical genetic consultation, however, before this study the process of communication involved in it had not been explored. In collaboration with the Colombian Association of Medical Genetics, the Bogotá Health Service, and the University of Warwick (UK), I observed 25 genetic consultations in five Colombian genetic clinics. I undertook semi-structured interviews with patients / families before and after the consultation. Thematic analysis of the interview transcripts established mismatches between physician perception and patient comprehension. Efficient communication was affected by patient, relatives, practitioner and external factors. Among these environmental factors were excessive administrative procedures, interruptions during the encounter, patients‟ lack of interest to medical terminology, doctors using scientific language, excessive information given in one session, beliefs and education level of the patient and/or relatives, patient distress caused by bad news, unfulfilled expectations and no availability/accessibility of treatment. I also interviewed 20 medical practitioners working in genetics services. There was general agreement that genetic counselling in Colombia was challenging, and that more training in communication skills was required at Medical schools at undergraduate and postgraduate level. Many physicians did not believe that other health professionals should work as genetic counsellors. There was a general recognition of limited genetic knowledge, awareness and understanding in most medical specialities. These results have made a valuable contribution to describe the current situation with genetics consultation and counselling in Colombian genetic clinics, and have already influenced the future development of an effective and robust genetic counselling service in Colombia. They will also be used in the development of the academic curriculum related to basic and clinical genetics at Colombian Universities.

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Asher, Allison Marie. „CONSERVATION GENETICS OF PADDLEFISH: GENETIC EFFECTIVE POPULATION SIZE AND RANGEWIDE GENETIC STRUCTURE“. OpenSIUC, 2019. https://opensiuc.lib.siu.edu/dissertations/1693.

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Paddlefish (Polyodon spathula) is a commercially and recreationally important species, with a native range that extends over 22 US states. This is a large, long-lived, highly mobile riverine species that has been negatively impacted by habitat fragmentation, historic overharvest, and hatchery supplementation. Dams are the primary cause of habitat fragmentation, blocking migration routes, flooding spawning grounds, and isolating populations. A common management action to mitigate the impacts of habitat fragmentation and maintain harvestable populations is hatchery propagation and stocking. Reduction in stock size, isolation of populations, and stocking can all negatively impact the genetic integrity of Paddlefish. I evaluated the impacts of isolation and hatchery supplementation on the effective population size (Ne) of Paddlefish as well as the range-wide genetic structure of Paddlefish.

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Bland, Ian Michael. „Generic systolic arrays for genetic algorithms“. Thesis, University of Reading, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312529.

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Campino, Susana. „Genetic analysis of murine malaria“. Doctoral thesis, Umeå universitet, Medicinsk biovetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-124.

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Malaria, an infectious disease caused by Plasmodium parasites, is one of the major world-scale health problems. Despite the efforts aimed at finding an effective way to control the disease, the success has been thwarted by the emergence of parasite drug resistance and mosquito resistance to insecticides. This thesis focuses on the genetic analysis of resistance to murine malaria induced by the lethal Plasmodium berghei ANKA using a wild-derived-inbred strain (WDIS). The aim of this thesis was to exploit the genetic diversity represented among WDIS for identifying loci contributing to resistance/susceptibility to murine malaria. The work included a genome-wide polymorphism survey using microsatellite markers performed on 10 WDIS. Comparisons of these strains to laboratory inbred strains confirmed a higher rate of polymorphism among the WDIS. We conclude that these WDIS represent repositories of unique naturally occurring genetic variability that may prove to be invaluable for the study of complex phenotypes. Next, we used the WDIS to search for novel phenotypes related to malaria pathogenesis. Whereas most laboratory strains were susceptible to experimental cerebral malaria (ECM) after infection with P. berghei ANKA, several WDIS were found to be resistant. To study the genetic inheritance of resistant/susceptibility to P. berghei ANKA infection we analysed backcross and F2 cohorts derived from crossing the WLA wild-derived strain with a laboratory mouse strain (C57BL/6). A novel phenotype represented by the cure of infection, clearance of parasitaemia and establishment of immunological memory was observed in the F2 progeny. The backcross progeny was used to genetically map one locus on chromosome 1 (Berr1) and one locus on chromosome 11 (Berr2) that mediate control of resistance to ECM induced by P. berghei ANKA. Genetic mapping using the F2 progeny showed that a locus on chromosome 1 (Berr1) and a locus on chromosome 9 (Berr3) were contributing to control survival time after infection with lethal Plasmodium. Finally, we identified, a locus on chromosome 4 (Berr4) that appears to control time of death due to hyperparasitaemia. This thesis underlines the value of using WDIS to reveal genetic factors involved in the aetiology of disease phenotypes. The characterisation of the genetic factors represented by the malaria resistance loci identified here are expected to provide a better understanding of the malaria pathology.

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De, Bustos Cecilia. „Genetic and Epigenetic Variation in the Human Genome : Analysis of Phenotypically Normal Individuals and Patients Affected with Brain Tumors“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6629.

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Hedmark, Eva. „Conservation Genetics of Scandinavian Wolverines“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Universitetsbiblioteket [distributör], 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6636.

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Nordquist, Niklas. „Genetic Studies of Rheumatoid Arthritis using Animal Models“. Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2001. http://publications.uu.se/theses/91-554-5117-9/.

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Hayes, Christina Savannah Maria. „Generic properties of the infinite population genetic algorithm“. Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/hayes/HayesC0806.pdf.

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Zenger, Kyall Richard. „Genetic linkage maps and population genetics of macropods“. Phd thesis, Australia : Macquarie University, 2002. http://hdl.handle.net/1959.14/47604.

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"November 2001".
Thesis (PhD)--Macquarie University, Division of Environmental and Life Sciences, Department of Biological Sciences, 2002.
Bibliography: leaves 136-157.
General introduction -- Molecular markers for comparative and quantitative studies in macropods -- Genetic linkage map construction in the tammar wallaby (M. eugenii) -- Intraspecific variation, sex-biased dispersal and phylogeography of the eastern grey kangaroo (M. giganteus) -- General discussion.
The analysis of DNA using molecular techniques is an important tool for studies of evolutionary relationships, population genetics and genome organisation. The use of molecular markers within marsupials is primarily limited by their availability and success of amplification. Within this study, 77 macropodid type II microsatellite loci and two type I genetic markers were characterised within M. eugenii to evaluate polymorphic levels and cross-species amplification artifacts. Results indicated that 65 microsatellite loci amplified a single locus in M. eugenii with 44 exhibiting high levels of variability. The success of crossspecies amplification of microsatellite loci was inversely proportional to the evolutionary distance between the macropod species. It is revealed that the majority of species within the Macropodidae are capable of using many of the available heterologous microsatellites. When comparing the degree of variability between source-species and M. eugenii, most were significantly higher within source species (P < 0.05). These differences were most likely caused by ascertainment bias in microsatellite selection for both length and purity. -- The production of a marsupial genetic linkage map is perhaps one of the most important objectives in marsupial research. This study used a total of 353 informative meioses and 64 genetic markers to construct a framework genetic linkage map for M. eugenii. Nearly all markers (93.7%) formed a significant linkage (LOD > 3.0) with at least one other marker. More than 70% (828 cM) of the genome had been mapped when compared with chiasmata data. Nine linkage groups were identified, with all but one (LG7; X-linked) allocated to the autosomes. Theses groups ranged in size from 15.7 cM to 176.5 cM, and have an average distance of 16.2 cM between adjacent markers. Of the autosomal linkage groups, LG2 and LG3 were assigned to chromosome 1 and LG4 localised to chromosome 3 based on physical localisation of genes. Significant sex-specific distortions towards reduced female recombination rates were revealed in 22% of comparisons. Positive interference was observed within all the linkage groups analysed. When comparing the X-chromosome data to closely related species it is apparent that it is conserved both in synteny and gene order. -- The investigation of population dynamics of eastern grey kangaroos has been limited to a few ecological studies. The present investigation provides analysis of mtDNA and microsatellite data to infer both historical and contemporary patterns of population structuring and dispersal. The average level of genetic variation across sample locations was exceedingly high (h = 0.95, HE = 0.82), and is one of the highest observed for marsupials. Contrary to ecological studies, both genic and genotypic analyses reveal weak genetic structure of populations where high levels of dispersal may be inferred up to 230 km. The movement of individuals was predominantly male-biased (average N,m = 22.61, average N p = 2.73). However, neither sex showed significant isolation by distance. On a continental scale, there was strong genetic differentiation and phylogeographic distinction between southern (TAS, VIC and NSW) and northern (QLD) Australian populations, indicating a current and / or historical restriction of geneflow. In addition, it is evident that northern populations are historically more recent, and were derived from a small number of southern eastern grey kangaroo founders. Phylogenetic comparisons between M. g. giganteus and M. g. tasmaniensis, indicated that the current taxonomic status of these subspecies should be revised as there was a lack of genetic differentiation between the populations sampled.
Mode of access: World Wide Web.
xv, 182 leaves ill

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Bücher zum Thema "Genetic"

Wexler, Barbara. Genetics and genetic engineering. 2. Aufl. Detroit, MI: Thomson/Gale Group, 2006.

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Yount, Lisa. Genetics and genetic engineering. New York: Facts on File, 1997.

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1932-, Elston Robert C., Olson Jane M und Palmer Lyle, Hrsg. Biostatistical genetics and genetic epidemiology. Chichester, England: New York, NY, USA, 2002.

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Snustad, D. Peter. Principles of genetics. 2. Aufl. New York: John Wiley, 2002.

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F, Dominiczak A., und Connell J. M. C, Hrsg. Genetics of hypertension. Edinburgh: Elsevier, 2007.

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J, Simmons Michael, Hrsg. Principles of genetics. 2. Aufl. New York: John Wiley, 2000.

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J, Simmons Michael, Hrsg. Principles of genetics. 5. Aufl. Hoboken: Wiley, 2009.

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J, Simmons Michael, Hrsg. Principles of genetics. 3. Aufl. New York, NY: John Wiley & Sons, 2003.

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J, Simmons Michael, Hrsg. Principles of genetics. 4. Aufl. Hoboken, NJ: John Wiley & Sons, 2006.

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J, Simmons Michael, und Jenkins John B, Hrsg. Principles of genetics. New York: John Wiley, 1997.

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Buchteile zum Thema "Genetic"

Foroud, Tatiana, und Daniel L. Koller. „Genetic Inheritance and Population Genetics“. In Molecular Genetic Pathology, 393–403. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-405-6_14.

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Foroud, Tatiana, und Daniel L. Koller. „Genetic Inheritance and Population Genetics“. In Molecular Genetic Pathology, 111–27. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4800-6_5.

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Twfieg, Mohammed-Elfatih, und M. Dawn Teare. „Molecular Genetics and Genetic Variation“. In Methods in Molecular Biology, 3–12. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60327-416-6_1.

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Hagemann, Rudolf, Monika M. Hagemann und Ralph Block. „Genetic Extranuclear Inheritance: Plastid Genetics“. In Progress in Botany, 108–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80446-5_4.

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Vogel, Friedrich, und Arno G. Motulsky. „Population Genetics: Consanguinity, Genetic Drift“. In Human Genetics, 549–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03356-2_14.

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Lee, Keekok. „Genetic Resources, Genetic Democracy and Genetic Equity“. In Genetic Democracy, 121–32. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-6212-4_10.

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Clarke, Angus. „Genetics and genetic counselling: An introduction“. In Harper's Practical Genetic Counselling, 3–22. Eighth edition | Boca Raton : CRC Press, [2020] | Preceded by Practical genetic counselling / Peter S. Harper. 7th ed. 2010.: CRC Press, 2019. http://dx.doi.org/10.1201/9780367371944-1.

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Gaspin, Christine, und Thomas Schiex. „Genetic algorithms for genetic mapping“. In Lecture Notes in Computer Science, 145–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0026597.

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Merks, Johannes H. M., und Ines B. Brecht. „Genetic Predisposition and Genetic Susceptibility“. In Rare Tumors In Children and Adolescents, 69–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04197-6_6.

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Stanier, Roger Y., John L. Ingraham, Mark L. Wheelis und Page R. Painter. „Microbial Genetics: Genetic Exchange and Recombination“. In General Microbiology, 257–85. London: Macmillan Education UK, 1986. http://dx.doi.org/10.1007/978-1-349-08754-9_11.

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Konferenzberichte zum Thema "Genetic"

Neville, Melvin, und Anaika Sibley. „Developing a generic genetic algorithm“. In the 2002 annual ACM SIGAda international conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/589451.589462.

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Chapman, Colin D., Kazuhiro Saitou und Mark J. Jakiela. „Genetic Algorithms As an Approach to Configuration and Topology Design“. In ASME 1993 Design Technical Conferences. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/detc1993-0338.

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Abstract The Genetic Algorithm, a search and optimization technique based on the theory of natural selection, is applied to problems of structural topology optimization. Given a structure’s boundary conditions and maximum allowable design domain, a discretized design representation is created. Populations of genetic algorithm “chromosomes” are then mapped into the design representation, creating potentially optimal structure topologies. Utilizing genetics-based operators such as crossover and mutation, generations of increasingly-desirable structure topologies are created. In this paper, the use of the genetic algorithm (GA) in structural topology optimization is presented. An overview of the genetic algorithm will describe the genetics-based representations and operators used in a typical genetic algorithm search. After defining topology optimization and its relation to the broader area of structural optimization, a review of previous research in GA-based and non-GA-based structural optimization is provided. The design representations, and methods for mapping genetic algorithm “chromosomes” into structure topology representations, are then detailed. Several examples of genetic algorithm-based structural topology optimization are provided: we address the optimization of beam cross-section topologies and cantilevered plate topologies, and we also investigate efficient techniques for using finite element analysis in a genetic algorithm-based search. Finally, a description of potential future work in genetic algorithm-based structural topology optimization is offered.

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Morgan, Jeffrey R. „Genetic Strategies for Tissue Engineering“. In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-1165.

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Abstract Recent advances in molecular genetics have resulted in the development of new technologies for the introduction and expression of genes in human somatic cells. These gene transfer technologies have given rise to a potentially new field of medical treatment known as gene therapy. Gene therapy is broadly defined as the transfer of genetic material to cells or tissues in order to achieve a therapeutic effect for inherited as well as acquired diseases. We are exploring the potential application of gene transfer technologies to the field of tissue engineering and are interested in determining if genetic modification can be used to enhance the function and/or performance of cells used as or part of biological substitutes for the restoration, maintenance or improvement of tissue function. We believe that gene transfer technologies will be an important addition to the field of tissue engineering.

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Langdon, William B. „Genetic Improvement of Genetic Programming“. In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185771.

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Chan, Tak-Ming, Kwong-Sak Leung, Kin-Hong Lee und Pietro Lio'. „Generic spaced DNA motif discovery using Genetic Algorithm“. In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5585924.

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Zimmerman, David C. „Navigating Expensive and Complex Design Spaces Using Genetic Algorithms“. In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8168.

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Abstract The overall objective of this study is to formulate and study a generic procedure for navigating expensive and complex design spaces. The term generic is meant to imply that the procedure would be equally valid in exploring design problems in a multitude of fields. The term expensive design space implies that the computational cost, or burden, associated with a single function is considered “large”. What is desired is a methodology which can identify “promising regions” of the design space using as few function evaluations as possible. To approach this problem, a neural network approach is developed to serve as an inexpensive and generic function approximation procedure. The genetic algorithm was selected as the optimization technique based on its ability to search multi-modal, discontinuous, mixed parameter, and noisy design spaces.

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Arakawa, Masao. „PSO Driven Genetic Range Genetic Algorithms“. In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84942.

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This paper deals with development of Genetic Range Genetic Algorithms (GRGAs). In GRGAs, one of the key is to set a new searching range, it needs to be followed after current searching situations, to be focused on local minute search and to be scattered as widely as possible for global search. However, first two strategies have a possibility of early stage convergence, and random scattering cause vain function calls to produce the range which seems no chance to prosper for a number of generations. In this paper, we propose a new method of setting it by using Particle Swarm Optimization (PSO) to overcome dilemma of the conventional method.

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Lopes, Rui L., und Ernesto Costa. „Genetic programming with genetic regulatory networks“. In Proceeding of the fifteenth annual conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2463372.2463488.

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„Genetic linkage to explain genetic variation“. In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.a4.mijangos.

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Hofestaedt, Ralf, und Hermann Mueller. „Genetic algorithms based on genetic grammar“. In Aerospace Sensing, herausgegeben von Firooz A. Sadjadi. SPIE, 1992. http://dx.doi.org/10.1117/12.139959.

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Berichte der Organisationen zum Thema "Genetic"

Sharp, David H., John Reinitz und Eric Mjolsness. Genetic Algorithms for Genetic Neural Nets. Fort Belvoir, VA: Defense Technical Information Center, Januar 1991. http://dx.doi.org/10.21236/ada256223.

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Arthur, Jennifer Ann. Genetic Algorithms. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1375151.

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Eric J. Hall. Individual Genetic Susceptibility. Office of Scientific and Technical Information (OSTI), Dezember 2008. http://dx.doi.org/10.2172/943485.

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Licht, Mark A. Corn Genetic Isolines. Ames: Iowa State University, Digital Repository, 2008. http://dx.doi.org/10.31274/farmprogressreports-180814-113.

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Kammenga, J. E. Hidden genetic variation : From recognition to acknowledgement of genetic individuality. Wageningen: Wageningen University & Research, 2016. http://dx.doi.org/10.18174/409705.

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Rothstein, M. A. Genetic secrets: Protecting privacy and confidentiality in the genetic era. Office of Scientific and Technical Information (OSTI), Juli 1998. http://dx.doi.org/10.2172/656488.

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Cahaner, Avigdor, Susan J. Lamont, E. Dan Heller und Jossi Hillel. Molecular Genetic Dissection of Complex Immunocompetence Traits in Broilers. United States Department of Agriculture, August 2003. http://dx.doi.org/10.32747/2003.7586461.bard.

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Objectives: (1) Evaluate Immunocompetence-OTL-containing Chromosomal Regions (ICRs), marked by microsatellites or candidate genes, for magnitude of direct effect and for contribution to relationships among multiple immunocompetence, disease-resistance, and growth traits, in order to estimate epistatic and pleiotropic effects and to predict the potential breeding applications of such markers. (2) Evaluate the interaction of the ICRs with genetic backgrounds from multiple sources and of multiple levels of genetic variation, in order to predict the general applicability of molecular genetic markers across widely varied populations. Background: Diseases cause substantial economic losses to animal producers. Emerging pathogens, vaccine failures and intense management systems increase the impact of diseases on animal production. Moreover, zoonotic pathogens are a threat to human food safety when microbiological contamination of animal products occurs. Consumers are increasingly concerned about drug residues and antibiotic- resistant pathogens derived from animal products. The project used contemporary scientific technologies to investigate the genetics of chicken resistance to infectious disease. Genetic enhancement of the innate resistance of chicken populations provides a sustainable and ecologically sound approach to reduce microbial loads in agricultural populations. In turn, animals will be produced more efficiently with less need for drug treatment and will pose less of a potential food-safety hazard. Major achievements, conclusions and implications:. The PI and co-PIs had developed a refined research plan, aiming at the original but more focused objectives, that could be well-accomplished with the reduced awarded support. The successful conduct of that research over the past four years has yielded substantial new information about the genes and genetic markers that are associated with response to two important poultry pathogens, Salmonella enteritidis (SE) and Escherichia coli (EC), about variation of immunocompetence genes in poultry, about relationships of traits of immune response and production, and about interaction of genes with environment and with other genes and genetic background. The current BARD work has generated a base of knowledge and expertise regarding the genetic variation underlying the traits of immunocompetence and disease resistance. In addition, unique genetic resource populations of chickens have been established in the course of the current project, and they are essential for continued projects. The US laboratory has made considerable progress in studies of the genetics of resistance to SE. Microsatellite-marked chromosomal regions and several specific genes were linked to SE vaccine response or bacterial burden and the important phenomenon of gene interaction was identified in this system. In total, these studies demonstrate the role of genetics in SE response, the utility of the existing resource population, and the expertise of the research group in conducting such experiments. The Israeli laboratories had showed that the lines developed by selection for high or low level of antibody (Ab) response to EC differ similarly in Ab response to several other viral and bacterial pathogens, indicating the existence of a genetic control of general capacity of Ab response in young broilers. It was also found that the 10w-Ab line has developed, possibly via compensatory "natural" selection, higher cellular immune response. At the DNA levels, markers supposedly linked to immune response were identified, as well as SNP in the MHC, a candidate gene responsible for genetic differences in immunocompetence of chickens.

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Girisankar Prema, Abinaya, Iyshwarya Bhaskar Kalarani und Ramakrishnan Veerabathiran. Genetic aspects of epilepsy. Peeref, November 2022. http://dx.doi.org/10.54985/peeref.2211p2734634.

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Rothstein, M. A. Genetic secrets: Protecting privacy and confidentiality in the genetic era. Final report. Office of Scientific and Technical Information (OSTI), September 1998. http://dx.doi.org/10.2172/656499.

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Gootwine, Elisha, Warren C. Foote, Moshe Shani und H. Goot. Genetic Improvement of Sheep by Introduction of Foreign Genetic Information into Prolific Breeds. United States Department of Agriculture, August 1985. http://dx.doi.org/10.32747/1985.7566578.bard.

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