Sample records for breeder genetic algorithm
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Weigel, K A; VanRaden, P M; Norman, H D; Grosu, H
2017-12-01
In the early 1900s, breed society herdbooks had been established and milk-recording programs were in their infancy. Farmers wanted to improve the productivity of their cattle, but the foundations of population genetics, quantitative genetics, and animal breeding had not been laid. Early animal breeders struggled to identify genetically superior families using performance records that were influenced by local environmental conditions and herd-specific management practices. Daughter-dam comparisons were used for more than 30 yr and, although genetic progress was minimal, the attention given to performance recording, genetic theory, and statistical methods paid off in future years. Contemporary (herdmate) comparison methods allowed more accurate accounting for environmental factors and genetic progress began to accelerate when these methods were coupled with artificial insemination and progeny testing. Advances in computing facilitated the implementation of mixed linear models that used pedigree and performance data optimally and enabled accurate selection decisions. Sequencing of the bovine genome led to a revolution in dairy cattle breeding, and the pace of scientific discovery and genetic progress accelerated rapidly. Pedigree-based models have given way to whole-genome prediction, and Bayesian regression models and machine learning algorithms have joined mixed linear models in the toolbox of modern animal breeders. Future developments will likely include elucidation of the mechanisms of genetic inheritance and epigenetic modification in key biological pathways, and genomic data will be used with data from on-farm sensors to facilitate precision management on modern dairy farms. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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USDA-ARS?s Scientific Manuscript database
Recessive genetic defects are currently on the minds of many cattle breeders. The relatively rapid development of diagnostic DNA tests for recessive defects appears to be a major recent technological advancement. However, the attitude of breeders and breed associations toward recessive defects seems...
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Kim, H-R; Kwon, Y-K; Bae, Y-C; Oem, J-K; Lee, O-S
2010-11-01
In South Korea, 32 sequences of chicken infectious anemia virus (CIAV) from various flocks of breeder and commercial chickens were genetically characterized for the first time. Phylogenetic analysis of the viral protein 1 gene, including a hypervariable region of the CIAV genome, indicated that Korean CIAV strains were separated into groups II, IIIa, and IIIb. Strains were commonly identified in great-grandparent and grandparent breeder farms as well as commercial chicken farms. In the field, CIAV strains from breeder farms had no clinical effects, but commercial farm strains were associated with depression, growth retardation, and anemia regardless of the group from which the strain originated. In addition, we identified 7 CIAV genomes that were similar to vaccine strains from vaccinated and unvaccinated breeder flocks. These data suggest that further studies on pathogenicity and vaccine efficacy against the different CIAV group are needed, along with continuous CIAV surveillance and genetic analysis at breeder farms.
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Czerwinski, Veronika; McArthur, Michelle; Smith, Bradley; Hynd, Philip; Hazel, Susan
2016-01-01
Simple Summary One of the most important factors influencing the health and welfare of puppies is the decision made by the breeder on which dam and sire they will breed from. Unfortunately, our understanding of what dog breeders consider important when selecting their dogs, particularly the dam, is limited. In order to bridge this gap, we conducted an online survey of Australian purebred dog breeders. We identified four major factors that the breeder considered important in relation to the dam: Maternal Care; Offspring Potential; Dam Temperament; and Dam Genetics and Health. Overall, the priorities and practices of dog breeders surveyed were variable across breeds. Importantly, it seemed that not all breeders understood the importance of maternal care behaviour, despite the significant role it may play on future puppy behaviour. Abstract Every year, thousands of purebred domestic dogs are bred by registered dog breeders. Yet, little is known about the rearing environment of these dogs, or the attitudes and priorities surrounding breeding practices of these dog breeders. The objective of this study was to explore some of the factors that dog breeders consider important for stock selection, with a particular emphasis on issues relating to the dam. Two-hundred and seventy-four Australian purebred dog breeders, covering 91 breeds across all Australian National Kennel Club breed groups, completed an online survey relating to breeding practices. Most breeders surveyed (76%) reported specialising in one breed of dog, the median number of dogs and bitches per breeder was two and three respectively, and most breeders bred two litters or less a year. We identified four components, relating to the dam, that were considered important to breeders. These were defined as Maternal Care, Offspring Potential, Dam Temperament, and Dam Genetics and Health. Overall, differences were observed in attitudes and beliefs across these components, showing that there is variation according to breed/breed groups. In particular, the importance of Maternal Care varied according to dog breed group. Breeders of brachycephalic breeds tended to differ the most in relation to Offspring Potential and Dam Genetics and Health. The number of breeding dogs/bitches influenced breeding priority, especially in relation to Dam Temperament, however no effect was found relating to the number of puppies bred each year. Only 24% of breeders used their own sire for breeding. The finding that some breeders did not test for diseases relevant to their breed, such as hip dysplasia in Labrador Retrievers and German Shepherds, provides important information on the need to educate some breeders, and also buyers of purebred puppies, that screening for significant diseases should occur. Further research into the selection of breeding dams and sires will inform future strategies to improve the health and behaviour of our best friend. PMID:27854338
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World directory of forest geneticists and tree breeders
F. Thomas Ledig; David B. Neale
1998-01-01
A formal task of the Forest Genetic Resources Study Group/North American Forestry Commission/Food and Agriculture Organization of the United Nations and Working Party 2.04.09 / Division 2- Physiology and Genetics / International Union of Forest ResearchOrganizations, this international directory lists more than 1,800 forest geneticists and tree breeders from 86...
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USDA-ARS?s Scientific Manuscript database
Wheat kernel texture dictates U.S. wheat market class and culinary end-uses. Of interest to wheat breeders is to identify quantitative trait loci (QTL) for wheat kernel texture, milling performance, or end-use quality because it is imperative for wheat breeders to ascertain the genetic architecture ...
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Effects of breeder turnover and harvest on group composition and recruitment in a social carnivore
Ausband, David E.; Mitchell, Michael S.; Waits, Lisette P.
2017-01-01
Breeder turnover can influence population growth in social carnivores through changes to group size, composition and recruitment.Studies that possess detailed group composition data that can provide insights about the effects of breeder turnover on groups have generally been conducted on species that are not subject to recurrent annual human harvest. We wanted to know how breeder turnover affects group composition and how harvest, in turn, affects breeder turnover in cooperatively breeding grey wolves (Canis lupus Linnaeus 1758).We used noninvasive genetic sampling at wolf rendezvous sites to construct pedigrees and estimate recruitment in groups of wolves before and after harvest in Idaho, USA.Turnover of breeding females increased polygamy and potential recruits per group by providing breeding opportunities for subordinates although resultant group size was unaffected 1 year after the turnover. Breeder turnover had no effect on the number of nonbreeding helpers per group. After breeding male turnover, fewer female pups were recruited in the new males’ litters. Harvest had no effect on the frequency of breeder turnover.We found that breeder turnover led to shifts in the reproductive hierarchies within groups and the resulting changes to group composition were quite variable and depended on the sex of the breeder lost. We hypothesize that nonbreeding females direct help away from non-kin female pups to preserve future breeding opportunities for themselves. Breeder turnover had marked effects on the breeding opportunities of subordinates and the number and sex ratios of subsequent litters of pups. Seemingly subtle changes to groups, such as the loss of one individual, can greatly affect group composition, genetic content, and short-term population growth when the individual lost is a breeder.
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Effects of breeder turnover and harvest on group composition and recruitment in a social carnivore.
Ausband, David E; Mitchell, Michael S; Waits, Lisette P
2017-09-01
Breeder turnover can influence population growth in social carnivores through changes to group size, composition and recruitment. Studies that possess detailed group composition data that can provide insights about the effects of breeder turnover on groups have generally been conducted on species that are not subject to recurrent annual human harvest. We wanted to know how breeder turnover affects group composition and how harvest, in turn, affects breeder turnover in cooperatively breeding grey wolves (Canis lupus Linnaeus 1758). We used noninvasive genetic sampling at wolf rendezvous sites to construct pedigrees and estimate recruitment in groups of wolves before and after harvest in Idaho, USA. Turnover of breeding females increased polygamy and potential recruits per group by providing breeding opportunities for subordinates although resultant group size was unaffected 1 year after the turnover. Breeder turnover had no effect on the number of nonbreeding helpers per group. After breeding male turnover, fewer female pups were recruited in the new males' litters. Harvest had no effect on the frequency of breeder turnover. We found that breeder turnover led to shifts in the reproductive hierarchies within groups and the resulting changes to group composition were quite variable and depended on the sex of the breeder lost. We hypothesize that nonbreeding females direct help away from non-kin female pups to preserve future breeding opportunities for themselves. Breeder turnover had marked effects on the breeding opportunities of subordinates and the number and sex ratios of subsequent litters of pups. Seemingly subtle changes to groups, such as the loss of one individual, can greatly affect group composition, genetic content, and short-term population growth when the individual lost is a breeder. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.
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Czerwinski, Veronika; McArthur, Michelle; Smith, Bradley; Hynd, Philip; Hazel, Susan
2016-11-16
Every year, thousands of purebred domestic dogs are bred by registered dog breeders. Yet, little is known about the rearing environment of these dogs, or the attitudes and priorities surrounding breeding practices of these dog breeders. The objective of this study was to explore some of the factors that dog breeders consider important for stock selection, with a particular emphasis on issues relating to the dam. Two-hundred and seventy-four Australian purebred dog breeders, covering 91 breeds across all Australian National Kennel Club breed groups, completed an online survey relating to breeding practices. Most breeders surveyed (76%) reported specialising in one breed of dog, the median number of dogs and bitches per breeder was two and three respectively, and most breeders bred two litters or less a year. We identified four components, relating to the dam, that were considered important to breeders. These were defined as Maternal Care, Offspring Potential, Dam Temperament, and Dam Genetics and Health. Overall, differences were observed in attitudes and beliefs across these components, showing that there is variation according to breed/breed groups. In particular, the importance of Maternal Care varied according to dog breed group. Breeders of brachycephalic breeds tended to differ the most in relation to Offspring Potential and Dam Genetics and Health. The number of breeding dogs/bitches influenced breeding priority, especially in relation to Dam Temperament, however no effect was found relating to the number of puppies bred each year. Only 24% of breeders used their own sire for breeding. The finding that some breeders did not test for diseases relevant to their breed, such as hip dysplasia in Labrador Retrievers and German Shepherds, provides important information on the need to educate some breeders, and also buyers of purebred puppies, that screening for significant diseases should occur. Further research into the selection of breeding dams and sires will inform future strategies to improve the health and behaviour of our best friend.
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Magnus, Roxane M; Tripodi, Amber D; Szalanski, Allen L
2014-06-01
To study the genetic diversity of honey bees (Apis mellifera L.) from unmanaged colonies in the United States, we sequenced a portion of the mitochondrial DNA COI-COII region. From the 530 to 1,230 bp amplicon, we observed 23 haplotypes from 247 samples collected from 12 states, representing three of the four A. mellifera lineages known to have been imported into the United States (C, M, and O). Six of the 13 C lineage haplotypes were not found in previous queen breeder studies in the United States. The O lineage accounted for 9% of unmanaged colonies which have not yet been reported in queen breeder studies. The M lineage accounted for a larger portion of unmanaged samples (7%) than queen breeder samples (3%). Based on our mitochondrial DNA data, the genetic diversity of unmanaged honey bees in the United States differs significantly from that of queen breeder populations (p < 0.00001). The detection of genetically distinct maternal lineages of unmanaged honey bees suggests that these haplotypes may have existed outside the managed honey bee population for a long period.
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Researcher responsibilities and genetic counseling for pure-bred dog populations.
Bell, Jerold S
2011-08-01
Breeders of dogs have ethical responsibilities regarding the testing and management of genetic disease. Molecular genetics researchers have their own responsibilities, highlighted in this article. Laboratories offering commercial genetic testing should have proper sample identification and quality control, official test result certificates, clear explanations of test results and reasonably priced testing fees. Providing test results to a publicly-accessible genetic health registry allows breeders and the public to search for health-tested parents to reduce the risk of producing or purchasing affected offspring. Counseling on the testing and elimination of defective genes must consider the effects of genetic selection on the population. Recommendations to breed quality carriers to normal-testing dogs and replacing them with quality normal-testing offspring will help to preserve breeding lines and breed genetic diversity. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Genome engineering and plant breeding: impact on trait discovery and development.
Nogué, Fabien; Mara, Kostlend; Collonnier, Cécile; Casacuberta, Josep M
2016-07-01
New tools for the precise modification of crops genes are now available for the engineering of new ideotypes. A future challenge in this emerging field of genome engineering is to develop efficient methods for allele mining. Genome engineering tools are now available in plants, including major crops, to modify in a predictable manner a given gene. These new techniques have a tremendous potential for a spectacular acceleration of the plant breeding process. Here, we discuss how genetic diversity has always been the raw material for breeders and how they have always taken advantage of the best available science to use, and when possible, increase, this genetic diversity. We will present why the advent of these new techniques gives to the breeders extremely powerful tools for crop breeding, but also why this will require the breeders and researchers to characterize the genes underlying this genetic diversity more precisely. Tackling these challenges should permit the engineering of optimized alleles assortments in an unprecedented and controlled way.
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Breeder survey, tools, and resources to visualize diversity and pedigree relationships at MaizeGDB
USDA-ARS?s Scientific Manuscript database
In collaboration with maize researchers, the MaizeGDB Team prepared a survey to identify breeder needs for visualizing pedigrees, diversity data, and haplotypes, and distributed it to the maize community on behalf of the Maize Genetics Executive Committee (Summer 2015). We received 48 responses from...
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Buston, Peter M; Bogdanowicz, Steven M; Wong, Alex; Harrison, Richard G
2007-09-01
A central question of evolutionary ecology is: why do animals live in groups? Answering this question requires that the costs and benefits of group living are measured from the perspective of each individual in the group. This, in turn, requires that the group's genetic structure is elucidated, because genetic relatedness can modulate the individuals' costs and benefits. The clown anemonefish, Amphiprion percula, lives in groups composed of a breeding pair and zero to four nonbreeders. Both breeders and nonbreeders stand to gain by associating with relatives: breeders might prefer to tolerate nonbreeders that are relatives because there is little chance that relatives will survive to breed elsewhere; nonbreeders might prefer to associate with breeders that are relatives because of the potential to accrue indirect genetic benefits by enhancing anemone and, consequently, breeder fitness. Given the potential benefits of associating with relatives, we use microsatellite loci to investigate whether or not individuals within groups of A. percula are related. We develop seven polymorphic microsatellite loci, with a number of alleles (range 2-24) and an observed level of heterozygosity (mean = 0.5936) sufficient to assess fine-scale genetic structure. The mean coefficient of relatedness among group members is 0.00 +/- 0.10 (n = 9 groups), and there are no surprising patterns in the distribution of pairwise relatedness. We conclude that A. percula live in groups of unrelated individuals. This study lays the foundation for further investigations of behavioural, population and community ecology of anemonefishes which are emerging as model systems for evolutionary ecology in the marine environment.
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Hatmaker, E. Anne; Wadl, Phillip A.; Mantooth, Kristie; Scheffler, Brian E.; Ownley, Bonnie H.; Trigiano, Robert N.
2015-01-01
Premise of the study: We developed microsatellites from Fothergilla ×intermedia to establish loci capable of distinguishing species and cultivars, and to assess genetic diversity for use by ornamental breeders and to transfer within Hamamelidaceae. Methods and Results: We sequenced a small insert genomic library enriched for microsatellites to develop 12 polymorphic microsatellite loci. The number of alleles detected ranged from four to 15 across five genera within Hamamelidaceae. Shannon’s information index ranged from 0.07 to 0.14. Conclusions: These microsatellite loci provide a set of markers to evaluate genetic diversity of natural and cultivated collections and assist ornamental plant breeders for genetic studies of five popular genera of woody ornamental plants. PMID:25909044
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Exotic germplasm introgression effect on agronomic and fiber properties of upland cotton
USDA-ARS?s Scientific Manuscript database
Genetic diversity is an important breeder’s tool for selection and improvement in crop cultivar development. Any successful breeding program depends on selecting superior quality parents. Lack of genetic diversity limits the potential of the breeder in selecting elite parents. Genetic uniformity pre...
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Freeing Crop Genetics through the Open Source Seed Initiative
Luby, Claire H.; Goldman, Irwin L.
2016-01-01
For millennia, seeds have been freely available to use for farming and plant breeding without restriction. Within the past century, however, intellectual property rights (IPRs) have threatened this tradition. In response, a movement has emerged to counter the trend toward increasing consolidation of control and ownership of plant germplasm. One effort, the Open Source Seed Initiative (OSSI, www.osseeds.org), aims to ensure access to crop genetic resources by embracing an open source mechanism that fosters exchange and innovation among farmers, plant breeders, and seed companies. Plant breeders across many sectors have taken the OSSI Pledge to create a protected commons of plant germplasm for future generations. PMID:27093567
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Freeing Crop Genetics through the Open Source Seed Initiative.
Luby, Claire H; Goldman, Irwin L
2016-04-01
For millennia, seeds have been freely available to use for farming and plant breeding without restriction. Within the past century, however, intellectual property rights (IPRs) have threatened this tradition. In response, a movement has emerged to counter the trend toward increasing consolidation of control and ownership of plant germplasm. One effort, the Open Source Seed Initiative (OSSI, www.osseeds.org), aims to ensure access to crop genetic resources by embracing an open source mechanism that fosters exchange and innovation among farmers, plant breeders, and seed companies. Plant breeders across many sectors have taken the OSSI Pledge to create a protected commons of plant germplasm for future generations.
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Genetic stock identification of Russian honey bees.
Bourgeois, Lelania; Sheppard, Walter S; Sylvester, H Allen; Rinderer, Thomas E
2010-06-01
A genetic stock certification assay was developed to distinguish Russian honey bees from other European (Apis mellifera L.) stocks that are commercially produced in the United States. In total, 11 microsatellite and five single-nucleotide polymorphism loci were used. Loci were selected for relatively high levels of homogeneity within each group and for differences in allele frequencies between groups. A baseline sample consisted of the 18 lines of Russian honey bees released to the Russian Bee Breeders Association and bees from 34 queen breeders representing commercially produced European honey bee stocks. Suitability tests of the baseline sample pool showed high levels of accuracy. The probability of correct assignment was 94.2% for non-Russian bees and 93.3% for Russian bees. A neighbor-joining phenogram representing genetic distance data showed clear distinction of Russian and non-Russian honey bee stocks. Furthermore, a test of appropriate sample size showed a sample of eight bees per colony maximizes accuracy and consistency of the results. An additional 34 samples were tested as blind samples (origin unknown to those collecting data) to determine accuracy of individual assignment tests. Only one of these samples was incorrectly assigned. The 18 current breeding lines were represented among the 2009 blind sampling, demonstrating temporal stability of the genetic stock identification assay. The certification assay will be used through services provided by a service laboratory, by the Russian Bee Breeders Association to genetically certify their stock. The genetic certification will be used in conjunction with continued selection for favorable traits, such as honey production and varroa and tracheal mite resistance.
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Townsend, Andrea K.; Clark, Anne B.; McGowan, Kevin J.; Lovette, Irby J.
2009-01-01
Understanding the benefits of cooperative breeding for group members of different social and demographic classes requires knowledge of their reproductive partitioning and genetic relatedness. From 2004-2007, we examined parentage as a function of relatedness and social interactions among members of 21 American crow (Corvus brachyrhynchos) family groups. Paired female breeders monopolized maternity of all offspring in their broods, whereas paired male breeders sired 82.7% of offspring, within-group auxiliary males sired 6.9% of offspring, and extragroup males sired 10.4% of offspring. Although adult females had fewer opportunities for direct reproduction as auxiliaries than males, they appeared to have earlier opportunities for independent breeding. These different opportunities for direct reproduction probably contributed to the male biased adult auxiliary sex ratio. Patterns of reproductive partitioning and conflict among males were most consistent with a synthetic reproductive skew model, in which auxiliaries struggled with breeders for a limited reproductive share, beyond which breeders could evict them. Counter to a frequent assumption of reproductive skew models, female breeders appeared to influence paternity, although their interests might have agreed with the interests of their paired males. Unusual among cooperative breeders, close inbreeding and incest occurred in this population. Incest avoidance between potential breeders did not significantly affect reproductive skew. PMID:20126287
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Choosy Wolves? Heterozygote Advantage But No Evidence of MHC-Based Disassortative Mating.
Galaverni, Marco; Caniglia, Romolo; Milanesi, Pietro; Lapalombella, Silvana; Fabbri, Elena; Randi, Ettore
2016-03-01
A variety of nonrandom mate choice strategies, including disassortative mating, are used by vertebrate species to avoid inbreeding, maintain heterozygosity and increase fitness. Disassortative mating may be mediated by the major histocompatibility complex (MHC), an important gene cluster controlling immune responses to pathogens. We investigated the patterns of mate choice in 26 wild-living breeding pairs of gray wolf (Canis lupus) that were identified through noninvasive genetic methods and genotyped at 3 MHC class II and 12 autosomal microsatellite (STR) loci. We tested for deviations from random mating and evaluated the covariance of genetic variables at functional and STR markers with fitness proxies deduced from pedigree reconstructions. Results did not show evidences of MHC-based disassortative mating. Rather we found a higher peptide similarity between mates at MHC loci as compared with random expectations. Fitness values were positively correlated with heterozygosity of the breeders at both MHC and STR loci, whereas they decreased with relatedness at STRs. These findings may indicate fitness advantages for breeders that, while avoiding highly related mates, are more similar at the MHC and have high levels of heterozygosity overall. Such a pattern of MHC-assortative mating may reflect local coadaptation of the breeders, while a reduction in genetic diversity may be balanced by heterozygote advantages. © The American Genetic Association 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
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Genotype disclosure in the genomics era: roles and responsibilities.
Denholm, L
2017-09-01
Disclosure of affected breed without disclosure of major progenitors has been the usual practice in scientific papers reporting recessive heritable disorders of cattle. Before molecular genetics, carrier identity could not be used by breeders to control causal mutations because phenotypically normal heterozygotes among genetically related animals could not be detected other than by test mating. Accurate, low-cost DNA tests fundamentally changed this situation. Genomics can provide relief from the old problem of emerging recessive disorders in cattle breeding, but greater transparency of genotype data between breeders is necessary to fully exploit the opportunities for cost-efficient genetic disease control. Effective control of several recessive disorders has been demonstrated in Angus cattle, based entirely on voluntary DNA testing by breeders but mandatory public disclosure of test results and genotype probabilities for all registered animals. When a DNA test is available, major progenitors (particularly bulls from which semen has been distributed) should be identified and disclosed concurrently with the affected breed. As a minimum, whenever possible the closest common ancestors in the pedigrees of the parents of homozygous mutants should be disclosed after confirmation of carrier status. Progenitor disclosure in scientific publications should occur in cooperation with breed societies, which should have the opportunity to advise breeders and initiate management programs before scientific publication. Unless properly managed, genomic enhancement of animal selection using SNP markers may increase inbreeding, co-ancestry and emergence of recessive disorders. The information systems and genotype disclosure policies of some breed societies will be increasingly challenged, particularly with accelerating mutation discovery using next-generation sequencing. © 2017 State of New South Wales.
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USDA-ARS?s Scientific Manuscript database
Genetic diversity is the raw material that plant breeders require to develop cultivars that are productive, nutritious, pest and stress tolerant, and water and nutrient use efficient. The USDA-ARS National Plant Germplasm System (NPGS) contains a wealth of genetic diversity, including improved varie...
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USDA-ARS?s Scientific Manuscript database
Potato breeders focus on ploidy because it impacts breeding methods and ultimately, genetic gains. This paper explains why ploidy it important. It begins with a brief history of cultivated potato from the perspective of ploidy. Then, I describe how we can make genetic improvements in potato by reduc...
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Practical implications for genetic modeling in the genomics era
USDA-ARS?s Scientific Manuscript database
Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...
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Identification of natural high-oleate mutants from the USDA Peanut Germplasm Collection
USDA-ARS?s Scientific Manuscript database
Natural genetic variation may exist in plant germplasm collections. Identifying genetic variation may provide useful materials for breeders to develop new cultivars. After screening 8,846 cultivated peanut germplasm accessions by gas chromatography analysis, we identified three natural mutant lines ...
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Allen, Alexandra M; Winfield, Mark O; Burridge, Amanda J; Downie, Rowena C; Benbow, Harriet R; Barker, Gary L A; Wilkinson, Paul A; Coghill, Jane; Waterfall, Christy; Davassi, Alessandro; Scopes, Geoff; Pirani, Ali; Webster, Teresa; Brew, Fiona; Bloor, Claire; Griffiths, Simon; Bentley, Alison R; Alda, Mark; Jack, Peter; Phillips, Andrew L; Edwards, Keith J
2017-03-01
Targeted selection and inbreeding have resulted in a lack of genetic diversity in elite hexaploid bread wheat accessions. Reduced diversity can be a limiting factor in the breeding of high yielding varieties and crucially can mean reduced resilience in the face of changing climate and resource pressures. Recent technological advances have enabled the development of molecular markers for use in the assessment and utilization of genetic diversity in hexaploid wheat. Starting with a large collection of 819 571 previously characterized wheat markers, here we describe the identification of 35 143 single nucleotide polymorphism-based markers, which are highly suited to the genotyping of elite hexaploid wheat accessions. To assess their suitability, the markers have been validated using a commercial high-density Affymetrix Axiom ® genotyping array (the Wheat Breeders' Array), in a high-throughput 384 microplate configuration, to characterize a diverse global collection of wheat accessions including landraces and elite lines derived from commercial breeding communities. We demonstrate that the Wheat Breeders' Array is also suitable for generating high-density genetic maps of previously uncharacterized populations and for characterizing novel genetic diversity produced by mutagenesis. To facilitate the use of the array by the wheat community, the markers, the associated sequence and the genotype information have been made available through the interactive web site 'CerealsDB'. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.
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Mate choice and genetic monogamy in a biparental, colonial fish.
Schaedelin, Franziska C; van Dongen, Wouter F D; Wagner, Richard H
2015-01-01
In socially monogamous species, in which both sexes provide essential parental care, males as well as females are expected to be choosy. Whereas hundreds of studies have examined monogamy in biparental birds, only several such studies exist in fish. We examined mate choice in the biparental, colonial cichlid fish Neolamprologus caudopunctatus in Lake Tanganyika, Zambia. We genotyped more than 350 individuals at 11 microsatellite loci to investigate their mating system. We found no extrapair paternity, identifying this biparental fish as genetically monogamous. Breeders paired randomly according to their genetic similarity, suggesting a lack of selection against inbreeding avoidance. We further found that breeders paired assortatively by body size, a criterion of quality in fish, suggesting mutual mate choice. In a subsequent mate preference test in an aquarium setup, females showed a strong preference for male size by laying eggs near the larger of 2 males in 13 of 14 trials.
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Mate choice and genetic monogamy in a biparental, colonial fish
van Dongen, Wouter F.D.; Wagner, Richard H.
2015-01-01
In socially monogamous species, in which both sexes provide essential parental care, males as well as females are expected to be choosy. Whereas hundreds of studies have examined monogamy in biparental birds, only several such studies exist in fish. We examined mate choice in the biparental, colonial cichlid fish Neolamprologus caudopunctatus in Lake Tanganyika, Zambia. We genotyped more than 350 individuals at 11 microsatellite loci to investigate their mating system. We found no extrapair paternity, identifying this biparental fish as genetically monogamous. Breeders paired randomly according to their genetic similarity, suggesting a lack of selection against inbreeding avoidance. We further found that breeders paired assortatively by body size, a criterion of quality in fish, suggesting mutual mate choice. In a subsequent mate preference test in an aquarium setup, females showed a strong preference for male size by laying eggs near the larger of 2 males in 13 of 14 trials. PMID:26023276
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NASA Astrophysics Data System (ADS)
Haro von Mogel, Karl J.
Carbohydrate metabolism is a biologically, economically, and culturally important process in crop plants. Humans have selected many crop species such as maize (Zea mays L.) in ways that have resulted in changes to carbohydrate metabolic pathways, and understanding the underlying genetics of this pathway is therefore exceedingly important. A previously uncharacterized starch metabolic pathway mutant, sugary enhancer1 (se1), is a recessive modifier of sugary1 (su1) sweet corn that increases the sugar content while maintaining an appealing creamy texture. This allele has been incorporated into many sweet corn varieties since its discovery in the 1970s, however, testing for the presence of this allele has been difficult. A genetic stock was developed that allowed the presence of se1 to be visually scored in segregating ears, which were used to genetically map se1 to the deletion of a single gene model located on the distal end of the long arm of chromosome 2. An analysis of homology found that this gene is specific to monocots, and the gene is expressed in the endosperm and developing leaf. The se1 allele increased water soluble polysaccharide (WSP) and decreased amylopectin in maize endosperm, but there was no overall effect on starch content in mature leaves due to se1. This discovery will lead to a greater understanding of starch metabolism, and the marker developed will assist in breeding. There is a present need for increased training for plant breeders to meet the growing needs of the human population. To raise the profile of plant breeding among young students, a series of videos called Fields of Study was developed. These feature interviews with plant breeders who talk about what they do as plant breeders and what they enjoy about their chosen profession. To help broaden the education of students in college biology courses, and assist with the training of plant breeders, a second video series, Pollination Methods was developed. Each video focuses on one or two major crops, their genetics, and shows how to make controlled crosses with these plants. Both video series have already made contributions to the recruitment and training of future plant breeders.
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Mining natural variation for maize improvement: Selection on phenotypes and genes
USDA-ARS?s Scientific Manuscript database
Maize is highly genetically and phenotypically diverse. Tropical maize and teosinte are important genetic resources that harbor unique alleles not found in temperate maize hybrids. To access these resources, breeders must be able to extract favorable unique alleles from tropical maize and teosinte f...
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Ellis, David; Chavez, Oswaldo; Coombs, Joseph J; Soto, Julian V; Gomez, Rene; Douches, David S; Panta, Ana; Silvestre, Rocio; Anglin, Noelle Lynette
2018-05-24
Breeders rely on genetic integrity of material from genebanks, however, mislabeling and errors in original data can occur. Paired samples of original material and their in vitro counterparts from 250 diverse potato landrace accessions from the International Potato Center (CIP), were fingerprinted using the Infinium 12K V2 Potato Array to confirm genetic identity and evaluate genetic diversity. Diploid, triploid, and tetraploid accessions were included representing seven cultivated potato taxa (Hawkes, 1990). Fingerprints between mother field plants and in vitro clones, were used to evaluate identity, relatedness, and ancestry. Clones of the same accession grouped together, however eleven (4.4%) accessions were mismatches genetically. SNP genotypes were used to construct a phylogeny to evaluate inter- and intraspecific relationships and population structure. Data suggests that the triploids evaluated are genetically similar. STRUCTURE analysis identified several putative hybrids and suggests six populations with significant gene flow between. This study provides a model for genetic identity of plant genetic resources collections as mistakes in conservation of these collections and in genebanks is a reality and confirmed identity is critical for breeders and other users of these collections, as well as for quality management programs and to provide insights into the diversity of the accessions evaluated.
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Minimal approaches to genetic improvement of growth rates in white spruce
D.T. Lester
1973-01-01
Several features of central importance to genetic improvement of white spruce have been demonstrated by tree breeders. First, white spruce is genetically a highly variable species and much of the existent variation can be readily incorporated in planting stock (Jeffers 1969, Holst and Teich 1969). Second, local seed often is not the best for rapid growth (Nienstaedt...
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Gascuel, Quentin; Diretto, Gianfranco; Monforte, Antonio J.; Fortes, Ana M.; Granell, Antonio
2017-01-01
Improving fruit quality has become a major goal in plant breeding. Direct approaches to tackling fruit quality traits specifically linked to consumer preferences and environmental friendliness, such as improved flavor, nutraceutical compounds, and sustainability, have slowly been added to a breeder priority list that already includes traits like productivity, efficiency, and, especially, pest and disease control. Breeders already use molecular genetic tools to improve fruit quality although most advances have been made in producer and industrial quality standards. Furthermore, progress has largely been limited to simple agronomic traits easy-to-observe, whereas the vast majority of quality attributes, specifically those relating to flavor and nutrition, are complex and have mostly been neglected. Fortunately, wild germplasm, which is used for resistance against/tolerance of environmental stresses (including pathogens), is still available and harbors significant genetic variation for taste and health-promoting traits. Similarly, heirloom/traditional varieties could be used to identify which genes contribute to flavor and health quality and, at the same time, serve as a good source of the best alleles for organoleptic quality improvement. Grape (Vitis vinifera L.) and tomato (Solanum lycopersicum L.) produce fleshy, berry-type fruits, among the most consumed in the world. Both have undergone important domestication and selection processes, that have dramatically reduced their genetic variability, and strongly standardized fruit traits. Moreover, more and more consumers are asking for sustainable production, incompatible with the wide range of chemical inputs. In the present paper, we review the genetic resources available to tomato/grape breeders, and the recent technological progresses that facilitate the identification of genes/alleles of interest within the natural or generated variability gene pool. These technologies include omics, high-throughput phenotyping/phenomics, and biotech approaches. Our review also covers a range of technologies used to transfer to tomato and grape those alleles considered of interest for fruit quality. These include traditional breeding, TILLING (Targeting Induced Local Lesions in Genomes), genetic engineering, or NPBT (New Plant Breeding Technologies). Altogether, the combined exploitation of genetic variability and innovative biotechnological tools may facilitate breeders to improve fruit quality tacking more into account the consumer standards and the needs to move forward into more sustainable farming practices. PMID:28553296
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Practical implications for genetic modeling in the genomics era for the dairy industry
USDA-ARS?s Scientific Manuscript database
Genetic models convert data into estimated breeding values and other information useful to breeders. The goal is to provide accurate and timely predictions of the future performance for each animal (or embryo). Modeling involves defining traits, editing raw data, removing environmental effects, incl...
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USDA-ARS?s Scientific Manuscript database
Genetic diversity is an essential resource for breeders to improve new cultivars with desirable characteristics. Recently genotyping-by-sequencing (GBS), a next generation sequencing (NGS) based technology that can simplify complex genomes, has been used as a high-throughput and cost-effective molec...
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A diploid inbred line strategy to accelerate genetic gain in potato
USDA-ARS?s Scientific Manuscript database
Breeding potato at the tetraploid level is inefficient and slow. Potato breeding has not kept pace with advances in breeding strategies and genomics tools. This project initiates our plan to convert potato into a diploid crop capable of self-pollination. This will allow breeders to realize the genet...
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Genetic variation of the bronze locus (MC1R) in turkeys from Southern Brazil
Corso, Josmael; Hepp, Diego; Ledur, Mônica C.; Peixoto, Jane O.; Fagundes, Nelson J. R.; Freitas, Thales R. O.
2017-01-01
Abstract Domestic turkeys present several color phenotypes controlled by at least five genetic loci, but only one of these has been identified precisely: the bronze locus, which turned out to be the melanocortin-1 receptor (MC1R) gene. MC1R variation is important for breeders interested in maintaining or developing different color varieties. In this study, we sequenced most of the MC1R gene from 16 White Holland (the main commercial turkey variety) and 19 pigmented turkeys from southern Brazil with two purposes. The first was to describe the MC1R diversity in White Holland turkeys, which may serve as reservoirs of genetic diversity at this locus. The second was to test whether the traditional color classification used by Brazilian breeders is related to previously known MC1R alleles. White Holland turkeys had four different haplotypes corresponding to the bronze (b +) and black-winged bronze (b 1) alleles. Pigmented turkeys also had four haplotypes corresponding to the b + and b 1 alleles, but different haplotypes represent the most common b + allele in these two groups. The black (B) allele was absent from our samples. Overall, our results suggest that white and pigmented individuals form two different populations, and that the traditional color classification used by Brazilian breeders cannot accurately predict the genotypes at the bronze locus. PMID:28323301
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Genetic variation of the bronze locus (MC1R) in turkeys from Southern Brazil.
Corso, Josmael; Hepp, Diego; Ledur, Mônica C; Peixoto, Jane O; Fagundes, Nelson J R; Freitas, Thales R O
2017-01-01
Domestic turkeys present several color phenotypes controlled by at least five genetic loci, but only one of these has been identified precisely: the bronze locus, which turned out to be the melanocortin-1 receptor (MC1R) gene. MC1R variation is important for breeders interested in maintaining or developing different color varieties. In this study, we sequenced most of the MC1R gene from 16 White Holland (the main commercial turkey variety) and 19 pigmented turkeys from southern Brazil with two purposes. The first was to describe the MC1R diversity in White Holland turkeys, which may serve as reservoirs of genetic diversity at this locus. The second was to test whether the traditional color classification used by Brazilian breeders is related to previously known MC1R alleles. White Holland turkeys had four different haplotypes corresponding to the bronze (b+) and black-winged bronze (b1) alleles. Pigmented turkeys also had four haplotypes corresponding to the b+ and b1 alleles, but different haplotypes represent the most common b+ allele in these two groups. The black (B) allele was absent from our samples. Overall, our results suggest that white and pigmented individuals form two different populations, and that the traditional color classification used by Brazilian breeders cannot accurately predict the genotypes at the bronze locus.
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F. Thomas Ledig; J. Jesús Vargas-Hernández; Kurt H. Johnsen
1998-01-01
The genetic codes of living organisms are natural resources no less than soil, air, and water. Genetic resources-from nucleotide sequences in DNA to selected genotypes, populations, and species-are the raw material in forestry: for breeders, for the forest manager who produces an economic crop, for society that reaps the environmental benefits provided by forests, and...
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Caste load and the evolution of reproductive skew.
Holman, Luke
2014-01-01
Reproductive skew theory seeks to explain how reproduction is divided among group members in animal societies. Existing theory is framed almost entirely in terms of selection, though nonadaptive processes must also play some role in the evolution of reproductive skew. Here I propose that a genetic correlation between helper fecundity and breeder fecundity may frequently constrain the evolution of reproductive skew. This constraint is part of a wider phenomenon that I term "caste load," which is defined as the decline in mean fitness caused by caste-specific selection pressures, that is, differential selection on breeding and nonbreeding individuals. I elaborate the caste load hypothesis using quantitative and population genetic arguments and individual-based simulations. Although selection can sometimes erode genetic correlations and resolve caste load, this may be constrained when mutations have similar pleiotropic effects on breeder and helper traits. I document evidence for caste load, identify putative genomic adaptations to it, and suggest future research directions. The models highlight the value of considering adaptation within the boundaries imposed by genetic architecture and incidentally reaffirm that monogamy promotes the evolutionary transition to eusociality.
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Resistance gene management: concepts and practice
Christopher C. Mundt
2012-01-01
There is now a very long history of genetics/breeding for disease resistance in annual crops. These efforts have resulted in conceptual advances and frustrations, as well as practical successes and failures. This talk will review this history and its relevance to the genetics of resistance in forest species. All plant breeders and pathologists are familiar with boom-...
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USDA-ARS?s Scientific Manuscript database
‘HoneySweet’ plum was released by the U.S. Department of Agriculture, Agricultural Research Service, to provide U.S. growers and P. domestica plum breeders with a high fruit quality plum cultivar resistant to Plum pox virus (PPV). ‘HoneySweet’ was developed through genetic engineering utilizing the...
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Addition of a breeding database in the Genome Database for Rosaceae
Evans, Kate; Jung, Sook; Lee, Taein; Brutcher, Lisa; Cho, Ilhyung; Peace, Cameron; Main, Dorrie
2013-01-01
Breeding programs produce large datasets that require efficient management systems to keep track of performance, pedigree, geographical and image-based data. With the development of DNA-based screening technologies, more breeding programs perform genotyping in addition to phenotyping for performance evaluation. The integration of breeding data with other genomic and genetic data is instrumental for the refinement of marker-assisted breeding tools, enhances genetic understanding of important crop traits and maximizes access and utility by crop breeders and allied scientists. Development of new infrastructure in the Genome Database for Rosaceae (GDR) was designed and implemented to enable secure and efficient storage, management and analysis of large datasets from the Washington State University apple breeding program and subsequently expanded to fit datasets from other Rosaceae breeders. The infrastructure was built using the software Chado and Drupal, making use of the Natural Diversity module to accommodate large-scale phenotypic and genotypic data. Breeders can search accessions within the GDR to identify individuals with specific trait combinations. Results from Search by Parentage lists individuals with parents in common and results from Individual Variety pages link to all data available on each chosen individual including pedigree, phenotypic and genotypic information. Genotypic data are searchable by markers and alleles; results are linked to other pages in the GDR to enable the user to access tools such as GBrowse and CMap. This breeding database provides users with the opportunity to search datasets in a fully targeted manner and retrieve and compare performance data from multiple selections, years and sites, and to output the data needed for variety release publications and patent applications. The breeding database facilitates efficient program management. Storing publicly available breeding data in a database together with genomic and genetic data will further accelerate the cross-utilization of diverse data types by researchers from various disciplines. Database URL: http://www.rosaceae.org/breeders_toolbox PMID:24247530
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Addition of a breeding database in the Genome Database for Rosaceae.
Evans, Kate; Jung, Sook; Lee, Taein; Brutcher, Lisa; Cho, Ilhyung; Peace, Cameron; Main, Dorrie
2013-01-01
Breeding programs produce large datasets that require efficient management systems to keep track of performance, pedigree, geographical and image-based data. With the development of DNA-based screening technologies, more breeding programs perform genotyping in addition to phenotyping for performance evaluation. The integration of breeding data with other genomic and genetic data is instrumental for the refinement of marker-assisted breeding tools, enhances genetic understanding of important crop traits and maximizes access and utility by crop breeders and allied scientists. Development of new infrastructure in the Genome Database for Rosaceae (GDR) was designed and implemented to enable secure and efficient storage, management and analysis of large datasets from the Washington State University apple breeding program and subsequently expanded to fit datasets from other Rosaceae breeders. The infrastructure was built using the software Chado and Drupal, making use of the Natural Diversity module to accommodate large-scale phenotypic and genotypic data. Breeders can search accessions within the GDR to identify individuals with specific trait combinations. Results from Search by Parentage lists individuals with parents in common and results from Individual Variety pages link to all data available on each chosen individual including pedigree, phenotypic and genotypic information. Genotypic data are searchable by markers and alleles; results are linked to other pages in the GDR to enable the user to access tools such as GBrowse and CMap. This breeding database provides users with the opportunity to search datasets in a fully targeted manner and retrieve and compare performance data from multiple selections, years and sites, and to output the data needed for variety release publications and patent applications. The breeding database facilitates efficient program management. Storing publicly available breeding data in a database together with genomic and genetic data will further accelerate the cross-utilization of diverse data types by researchers from various disciplines. Database URL: http://www.rosaceae.org/breeders_toolbox.
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Spread of avian pathogenic Escherichia coli ST117 O78:H4 in Nordic broiler production.
Ronco, Troels; Stegger, Marc; Olsen, Rikke Heidemann; Sekse, Camilla; Nordstoga, Anne Bang; Pohjanvirta, Tarja; Lilje, Berit; Lyhs, Ulrike; Andersen, Paal Skytt; Pedersen, Karl
2017-01-03
Escherichia coli infections known as colibacillosis constitute a considerable challenge to poultry farmers worldwide, in terms of decreased animal welfare and production economy. Colibacillosis is caused by avian pathogenic E. coli (APEC). APEC strains are extraintestinal pathogenic E. coli and have in general been characterized as being a genetically diverse population. In the Nordic countries, poultry farmers depend on import of Swedish broiler breeders which are part of a breeding pyramid. During 2014 to 2016, an increased occurrence of colibacillosis on Nordic broiler chicken farms was reported. The aim of this study was to investigate the genetic diversity among E. coli isolates collected on poultry farms with colibacillosis issues, using whole genome sequencing. Hundred and fourteen bacterial isolates from both broilers and broiler breeders were whole genome sequenced. The majority of isolates were collected from poultry with colibacillosis on Nordic farms. Subsequently, comparative genomic analyses were carried out. This included in silico typing (sero- and multi-locus sequence typing), identification of virulence and resistance genes and phylogenetic analyses based on single nucleotide polymorphisms. In general, the characterized poultry isolates constituted a genetically diverse population. However, the phylogenetic analyses revealed a major clade of 47 closely related ST117 O78:H4 isolates. The isolates in this clade were collected from broiler chickens and breeders with colibacillosis in multiple Nordic countries. They clustered together with a human ST117 isolate and all carried virulence genes that previously have been associated with human uropathogenic E. coli. The investigation revealed a lineage of ST117 O78:H4 isolates collected in different Nordic countries from diseased broilers and breeders. The data indicate that the closely related ST117 O78:H4 strains have been transferred vertically through the broiler breeding pyramid into distantly located farms across the Nordic countries.
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Harvest and group effects on pup survival in a cooperative breeder
Ausband, David E.; Mitchell, Michael S.; Stansbury, Carisa R.; Stenglein, Jennifer L.; Waits, Lisette P.
2017-01-01
Recruitment in cooperative breeders can be negatively affected by changes in group size and composition. The majority of cooperative breeding studies have not evaluated human harvest; therefore, the effects of recurring annual harvest and group characteristics on survival of young are poorly understood. We evaluated how harvest and groups affect pup survival using genetic sampling and pedigrees for grey wolves in North America. We hypothesized that harvest reduces pup survival because of (i) reduced group size, (ii) increased breeder turnover and/or (iii) reduced number of female helpers. Alternatively, harvest may increase pup survival possibly due to increased per capita food availability or it could be compensatory with other forms of mortality. Harvest appeared to be additive because it reduced both pup survival and group size. In addition to harvest, turnover of breeding males and the presence of older, non-breeding males also reduced pup survival. Large groups and breeder stability increased pup survival when there was harvest, however. Inferences about the effect of harvest on recruitment require knowledge of harvest rate of young as well as the indirect effects associated with changes in group size and composition, as we show. The number of young harvested is a poor measure of the effect of harvest on recruitment in cooperative breeders.
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Dates and places of pollen collection by the Institute of Forest Genetics
John W. Duffield
1947-01-01
During the past few years, the Institute of Forest Genetics has received an increasing number of requests for pollen of various species of pine. Many of these requests have been fulfilled; others which stipulated arrival in time for specific crossing operations could not be fulfilled. The accompanying table is compiled to furnish cooperating pine breeders with the...
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GBIS: the information system of the German Genebank
Oppermann, Markus; Weise, Stephan; Dittmann, Claudia; Knüpffer, Helmut
2015-01-01
The German Federal ex situ Genebank of Agricultural and Horticultural Crop Species is the largest collection of its kind in the countries of the European Union and amongst the 10 largest collections worldwide. Beside its enormous scientific value as a safeguard of plant biodiversity, the plant genetic resources maintained are also of high importance for breeders to provide new impulses. The complex processes of managing such a collection are supported by the Genebank Information System (GBIS). GBIS is an important source of information for researchers and plant breeders, e.g. for identifying appropriate germplasm for breeding purposes. In addition, the access to genebank material as a sovereign task is also of high interest to the general public. Moreover, GBIS acts as a data source for global information systems, such as the Global Biodiversity Information Facility (GBIF) or the European Search Catalogue for Plant Genetic Resources (EURISCO). Database URL: http://gbis.ipk-gatersleben.de/ PMID:25953079
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Wildlife DNA forensics against crime: resolution of a case of tortoise theft.
Mucci, Nadia; Mengoni, Chiara; Randi, Ettore
2014-01-01
A paternity test was used to investigate a robbery case involving captive individuals of Greek tortoise (Testudo graeca). Six tortoises were allegedly stolen from a private breeder and offered for sale on the web by the supposed thief. The stolen tortoises were confiscated by the rangers of the State Forestry Corps (CFS). A panel of 14 autosomal microsatellite loci was used to genotype the seized tortoises and ten individuals assumed to be legally owned by the breeder. Kinship analyses reliably reconstructed the tortoise pedigree, demonstrating parent-offspring relationships among the owned and the stolen tortoises. As correctly declared by the breeder, four of the six stolen individuals belonged to the same family group of the ten legally owned tortoises. Results indicate that genetic identification procedures can provide valuable evidence and give useful support against illegal wildlife traffic. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
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Decuypere, E; Bruggeman, V; Everaert, N; Li, Yue; Boonen, R; De Tavernier, J; Janssens, S; Buys, N
2010-10-01
1. Due to intensive selection, broiler chickens became the most efficient meat-producing animals because of their fast growth, supported by a virtually unlimited voluntary feed intake. These characteristics cause many problems in the management of broiler breeder hens because of the negative correlation between muscle growth and reproduction effectiveness. 2. This problem, namely the fast muscle growth versus reproduction health paradox, induces a second paradox, acceptable reproduction and health versus hunger stress and impaired welfare, because broiler breeder hens require dedicated programmes of feed restriction (1) to maximise egg and chick production and (2) to avoid metabolic disorders and mortality in broiler breeders. 3. Given that poultry selection is a global large-scale business and chickens are a prolific species, improvement in profit can only be obtained by selecting on feed conversion and/or for higher breast meat percentage, which will intensify the broiler-breeder paradox. 4. New feeding strategies are being studied, but it is questionable if the paradox can be solved by management tools alone. Because breeding and selection are long-term processes, involving animals, farmers, consumers, industry, environment etc., a more sustainable breeding goal needs to be determined by a multidisciplinary approach and an open debate between several actors in the discussion. 5. Using dwarf broiler breeder hens could be one alternative, because dwarf hens combine relatively good reproductive fitness with ad libitum feeding. Another possibility is to accept lower broiler productivity by assigning economic values to welfare and including integrity traits in an extended breeding goal.
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Pathak, Bhuvan; Ayala-Silva, Tomas; Yang, Xiping; Todd, James; Glynn, Neil C.; Kuhn, David N.; Glaz, Barry; Gilbert, Robert A.; Comstock, Jack C.; Wang, Jianping
2014-01-01
Sugarcane (Saccharum spp.) and other members of Saccharum spp. are attractive biofuel feedstocks. One of the two World Collections of Sugarcane and Related Grasses (WCSRG) is in Miami, FL. This WCSRG has 1002 accessions, presumably with valuable alleles for biomass, other important agronomic traits, and stress resistance. However, the WCSRG has not been fully exploited by breeders due to its lack of characterization and unmanageable population. In order to optimize the use of this genetic resource, we aim to 1) genotypically evaluate all the 1002 accessions to understand its genetic diversity and population structure and 2) form a core collection, which captures most of the genetic diversity in the WCSRG. We screened 36 microsatellite markers on 1002 genotypes and recorded 209 alleles. Genetic diversity of the WCSRG ranged from 0 to 0.5 with an average of 0.304. The population structure analysis and principal coordinate analysis revealed three clusters with all S. spontaneum in one cluster, S. officinarum and S. hybrids in the second cluster and mostly non-Saccharum spp. in the third cluster. A core collection of 300 accessions was identified which captured the maximum genetic diversity of the entire WCSRG which can be further exploited for sugarcane and energy cane breeding. Sugarcane and energy cane breeders can effectively utilize this core collection for cultivar improvement. Further, the core collection can provide resources for forming an association panel to evaluate the traits of agronomic and commercial importance. PMID:25333358
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Maxine M. Thompson - Dedication
USDA-ARS?s Scientific Manuscript database
This manuscript summarizes the research career of Dr. Maxine M. Thompson, world renown horticulturist, plant breeder, and plant explorer. She became the first women professor at the Oregon State University, Deparment of Horticulture. She studied Rubus cytology and genetics and floral development in ...
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NASA Astrophysics Data System (ADS)
Haghighattalab, Atena
Wheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder's decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.
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Ozerov, Mikhail; Jürgenstein, Tauno; Aykanat, Tutku; Vasemägi, Anti
2015-08-01
Declining trends in the abundance of many fish urgently call for more efficient and informative monitoring methods that would provide necessary demographic data for the evaluation of existing conservation, restoration, and management actions. We investigated how genetic sibship reconstruction from young-of-the-year brown trout (Salmo trutta L.) juveniles provides valuable, complementary demographic information that allowed us to disentangle the effects of habitat quality and number of breeders on juvenile density. We studied restored (n = 15) and control (n = 15) spawning and nursery habitats in 16 brown trout rivers and streams over 2 consecutive years to evaluate the effectiveness of habitat restoration activities. Similar juvenile densities both in restored and control spawning and nursery grounds were observed. Similarly, no differences in the effective number of breeders, Nb(SA) , were detected between habitats, indicating that brown trout readily used recently restored spawning grounds. Only a weak relationship between the Nb(SA) and juvenile density was observed, suggesting that multiple factors affect juvenile abundance. In some areas, very low estimates of Nb(SA) were found at sites with high juvenile density, indicating that a small number of breeders can produce a high number of progeny in favorable conditions. In other sites, high Nb(SA) estimates were associated with low juvenile density, suggesting low habitat quality or lack of suitable spawning substrate in relation to available breeders. Based on these results, we recommend the incorporation of genetic sibship reconstruction to ongoing and future fish evaluation and monitoring programs to gain novel insights into local demographic and evolutionary processes relevant for fisheries management, habitat restoration, and conservation. © 2015 Society for Conservation Biology.
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Congiu, Leonardo; Pujolar, Jose Martin; Forlani, Anna; Cenadelli, Silvia; Dupanloup, Isabelle; Barbisan, Federica; Galli, Andrea; Fontana, Francesco
2011-01-01
While the current expansion of conservation genetics enables to address more efficiently the management of threatened species, alternative methods for genetic relatedness data analysis in polyploid species are necessary. Within this framework, we present a standardized and simple protocol specifically designed for polyploid species that can facilitate management of genetic diversity, as exemplified by the ex situ conservation program for the tetraploid Adriatic sturgeon Acipenser naccarii. A critically endangered endemic species of the Adriatic Sea tributaries, its persistence is strictly linked to the ex situ conservation of a single captive broodstock currently decimated to about 25 individuals, which represents the last remaining population of Adriatic sturgeon of certain wild origin. The genetic variability of three F1 broodstocks available as future breeders was estimated based on mitochondrial and microsatellite information and compared with the variability of the parental generation. Genetic data showed that the F1 stocks have only retained part of the genetic variation present in the original stock due to the few parent pairs used as founders. This prompts for the urgent improvement of the current F1 stocks by incorporating new founders that better represent the genetic diversity available. Following parental allocation based on band sharing values, we set up a user-friendly tool for selection of candidate breeders according to relatedness between all possible parent-pairs that secures the use of non-related individuals. The approach developed here could also be applied to other endangered tetraploid sturgeon species overexploited for caviar production, particularly in regions lacking proper infrastructure and/or expertise. PMID:21483472
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Genotypes are useful for more than genomic evaluation
USDA-ARS?s Scientific Manuscript database
New services that provide pedigree discovery, breed composition, mating programs, genomic inbreeding, fertility defects, and inheritance tracking all are possible from low-cost genotyping in addition to genomic evaluation. Genetic markers let breeders select among sibs before their phenotypes became...
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Advances in marker-assisted breeding of sugarcane
USDA-ARS?s Scientific Manuscript database
Despite the challenges posed by sugarcane, geneticists and breeders have actively sought to use DNA marker technology to enhance breeding efforts. Markers have been used to explore taxonomy, estimate genetic diversity, and to develop unique molecular fingerprints. Numerous studies have been undertak...
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Chak Han Im; Young-Hoon Park; Kenneth E. Hammel; Bokyung Park; Soon Wook Kwon; Hojin Ryu; Jae-San Ryu
2016-01-01
Breeding new strains with improved traits is a long-standing goal of mushroom breeders that can be expedited by marker-assisted selection (MAS). We constructed a genetic linkage map of Pleurotus eryngii based on segregation analysis of markers in postmeiotic monokaryons from KNR2312. In total, 256 loci comprising 226 simple sequence-repeat (SSR) markers, 2 mating-type...
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Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O
2017-05-31
The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.
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Prachantasena, Sakaoporn; Charununtakorn, Petcharatt; Muangnoicharoen, Suthida; Hankla, Luck; Techawal, Natthaporn; Chaveerach, Prapansak; Tuitemwong, Pravate; Chokesajjawatee, Nipa; Williams, Nicola; Humphrey, Tom; Luangtongkum, Taradon
2016-01-01
Poultry and poultry products are commonly considered as the major vehicle of Campylobacter infection in humans worldwide. To reduce the number of human cases, the epidemiology of Campylobacter in poultry must be better understood. Therefore, the objective of the present study was to determine the distribution and genetic relatedness of Campylobacter in the Thai chicken production industry. During June to October 2012, entire broiler production processes (i.e., breeder flock, hatchery, broiler farm and slaughterhouse) of five broiler production chains were investigated chronologically. Representative isolates of C. jejuni from each production stage were characterized by flaA SVR sequencing and multilocus sequence typing (MLST). Amongst 311 selected isolates, 29 flaA SVR alleles and 17 sequence types (STs) were identified. The common clonal complexes (CCs) found in this study were CC-45, CC-353, CC-354 and CC-574. C. jejuni isolated from breeders were distantly related to those isolated from broilers and chicken carcasses, while C. jejuni isolates from the slaughterhouse environment and meat products were similar to those isolated from broiler flocks. Genotypic identification of C. jejuni in slaughterhouses indicated that broilers were the main source of Campylobacter contamination of chicken meat during processing. To effectively reduce Campylobacter in poultry meat products, control and prevention strategies should be aimed at both farm and slaughterhouse levels.
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Core Hunter 3: flexible core subset selection.
De Beukelaer, Herman; Davenport, Guy F; Fack, Veerle
2018-05-31
Core collections provide genebank curators and plant breeders a way to reduce size of their collections and populations, while minimizing impact on genetic diversity and allele frequency. Many methods have been proposed to generate core collections, often using distance metrics to quantify the similarity of two accessions, based on genetic marker data or phenotypic traits. Core Hunter is a multi-purpose core subset selection tool that uses local search algorithms to generate subsets relying on one or more metrics, including several distance metrics and allelic richness. In version 3 of Core Hunter (CH3) we have incorporated two new, improved methods for summarizing distances to quantify diversity or representativeness of the core collection. A comparison of CH3 and Core Hunter 2 (CH2) showed that these new metrics can be effectively optimized with less complex algorithms, as compared to those used in CH2. CH3 is more effective at maximizing the improved diversity metric than CH2, still ensures a high average and minimum distance, and is faster for large datasets. Using CH3, a simple stochastic hill-climber is able to find highly diverse core collections, and the more advanced parallel tempering algorithm further increases the quality of the core and further reduces variability across independent samples. We also evaluate the ability of CH3 to simultaneously maximize diversity, and either representativeness or allelic richness, and compare the results with those of the GDOpt and SimEli methods. CH3 can sample equally representative cores as GDOpt, which was specifically designed for this purpose, and is able to construct cores that are simultaneously more diverse, and either are more representative or have higher allelic richness, than those obtained by SimEli. In version 3, Core Hunter has been updated to include two new core subset selection metrics that construct cores for representativeness or diversity, with improved performance. It combines and outperforms the strengths of other methods, as it (simultaneously) optimizes a variety of metrics. In addition, CH3 is an improvement over CH2, with the option to use genetic marker data or phenotypic traits, or both, and improved speed. Core Hunter 3 is freely available on http://www.corehunter.org .
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Geographic distribution of genetic diversity in populations of Rio Grande Chub Gila pandora
Galindo, Rene; Wilson, Wade; Caldwell, Colleen A.
2016-01-01
In the southwestern United States (US), the Rio Grande chub (Gila pandora) is state-listed as a fish species of greatest conservation need and federally listed as sensitive due to habitat alterations and competition with non-native fishes. Characterizing genetic diversity, genetic population structure, and effective number of breeders will assist with conservation efforts by providing a baseline of genetic metrics. Genetic relatedness within and among G. pandora populations throughout New Mexico was characterized using 11 microsatellite loci among 15 populations in three drainage basins (Rio Grande, Pecos, Canadian). Observed heterozygosity (HO) ranged from 0.71–0.87 and was similar to expected heterozygosity (0.75–0.87). Rio Ojo Caliente (Rio Grande) had the highest allelic richness (AR = 15.09), while Upper Rio Bonito (Pecos) had the lowest allelic richness (AR = 6.75). Genetic differentiation existed among all populations with the lowest genetic variation occurring within the Pecos drainage. STRUCTURE analysis revealed seven genetic clusters. Populations of G. pandora within the upper Rio Grande drainage (Rio Ojo Caliente, Rio Vallecitos, Rio Pueblo de Taos) had high levels of admixture with Q-values ranging from 0.30–0.50. In contrast, populations within the Pecos drainage (Pecos River and Upper Rio Bonito) had low levels of admixture (Q = 0.94 and 0.87, respectively). Estimates of effective number of breeders (N b ) varied from 6.1 (Pecos: Upper Rio Bonito) to 109.7 (Rio Grande: Rio Peñasco) indicating that populations in the Pecos drainage are at risk of extirpation. In the event that management actions are deemed necessary to preserve or increase genetic diversity of G. pandora, consideration must be given as to which populations are selected for translocation.
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Leandro, N M; Ali, R; Koci, M; Moraes, V; Eusebio-Balcazar, P E; Jornigan, J; Malheiros, R D; Wineland, M J; Brake, J; Oviedo-Rondón, E O
2011-12-01
Two experiments were conducted to examine the effects of broiler breeder dietary grain source and cage density on maternal antibody (MatAb) transfer to progeny in 2 genetic strains (A and B). Broiler breeders were assigned to 16 litter floor pens and fed either corn- or wheat-based diets. Breeders were administered 4 live vaccines against Newcastle disease virus (NDV). At 23 wk of age, pullets and cocks, which reflected the full BW distribution from each treatment, were moved to a cage breeder house and placed at 1 or 2 hens/cage. Breeders were artificially inseminated at 44 wk (experiment 1) and 52 wk of age (experiment 2). Eggs were collected for 8 d, incubated, and placed in individual pedigree bags at d 19 of incubation. Blood samples from 5 chicks per treatment combination were collected at hatch in both experiments. Spleen and bursa were collected from the same chicks for histomorphometry analyses in experiment 2. In the second experiment, 12 chicks per treatment were placed in cages. Progeny were provided diets based on the same grain (corn or wheat) as their parents. Serum samples were collected at 5, 9, and 13 d of age and analyzed for anti-NDV MatAb. Data were analyzed as a 2 × 2 × 2 factorial design considering strain, dietary grain source, and cage density as main factors. Interaction effects were observed in breeders and progeny. Experiment 1 showed that strain A chicks had lower levels of MatAb when hens were housed at 2 hens/cage rather than 1 hen/cage. The MatAb levels of strain B chickens were not affected by cage density in either experiment. Experiment 2 demonstrated similar effects of cage density on MatAb levels and the area of bursa follicles for both strains. Progeny of breeders fed corn-based diets had smaller spleen white pulp only when hens were housed at 2 hens/cage compared with 1 hen/cage. The results of these experiments suggest that breeder strain and cage-density conditions affected MatAb transfer to progeny and embryo development of spleen and bursa.
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Revealing gene regulation and association through biological networks
USDA-ARS?s Scientific Manuscript database
This review had first summarized traditional methods used by plant breeders for genetic improvement, such as QTL analysis and transcriptomic analysis. With accumulating data, we can draw a network that comprises all possible links between members of a community, including protein–protein interaction...
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Using Python for Pedigree Analysis
USDA-ARS?s Scientific Manuscript database
A pedigree is a way of describing a population of people or animals in terms of genetic relationships among individuals. Pedigrees are of interest to many people, including scientists, animal and plant breeders, and genealogists. They are used to assess the diversity of populations, in combination ...
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Emerging avenues for utilization of exotic germplasm
USDA-ARS?s Scientific Manuscript database
Breeders have been successful in increasing crop performance by exploiting genetic diversity over time. However, the reported annual yield increases are not sufficient in view of rapid human population growth and global environmental changes. Exotic germplasm such as landraces and wild relatives pos...
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Ensuring and exploiting the genetic diversity of sugarcane
USDA-ARS?s Scientific Manuscript database
Modern sugarcane cultivars are complex interspecific hybrids primarily involving Saccharum officinarum and S. spontaneum. In the late 1800s, early breeders in Java, Indonesia recognized the value of interspecific hybridization and began to hybridize the two species, resulting in vigorous and diseas...
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Classical genetics and traditional breeding
USDA-ARS?s Scientific Manuscript database
Cucurbit crops simultaneously bestow upon the breeder several advantages and disadvantages. As pointed out by Whitaker and Bohn (1950), cucurbit crops are easily grown with indeterminant plants which typically offer plenty of large flowers to work with over a fairly long period of time. Probably t...
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Use of ATR FT-IR spectroscopy in non-destructive and rapid assessment of developmental cotton fibers
USDA-ARS?s Scientific Manuscript database
The knowledge of chemical and compositional components in cotton fibers is of value to cotton breeders and growers for cotton enhancement and to textile processors for quality control. In this work, we applied the previously proposed simple algorithms to analyze the attenuated total reflection Fouri...
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Fascist labscapes: geneticists, wheat, and the landscapes of Fascism in Italy and Portugal.
Saraiva, Tiago
2010-01-01
This paper explores the role of scientists in the building of fascist regimes in Italy and Portugal by focusing on plant geneticists' participation in the Italian and Portuguese wheat wars for bread self-sufficiency. It looks closely at the work undertaken by Nazareno Strampelli at the National Institute of Genetics for Grain Cultivation (Italy) and by António Sousa da Câmara at the National Agronomic Experiment Station (Portugal), both of whom took wheat as their prime experimental object of genetics research. The main argument is that the production of standardized organisms—the breeder's elite seeds—in laboratory spaces is deeply entangled with their circulation through extended distribution networks that allowed for their massive presence in Italian and Portuguese landscapes such as the Po Valley and the Alentejo. The narrative pays particular attention to the historical development of fascist regimes in the two countries, advancing the argument that breeders' artifacts were key components of the institutionalization of the new political regimes.
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Collaborative project to identify direct and distant pedigree relationships in apple
USDA-ARS?s Scientific Manuscript database
Pedigree information is fundamentally important in breeding programs, enabling breeders to know the source of valuable attributes and underlying alleles and to enlarge genetic diversity in a directed way. Many apple cultivars are related to each other through both recent and distant common ancestors...
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Genetic fingerprinting of potato varieties from the Northwest Potato Variety Development Program
USDA-ARS?s Scientific Manuscript database
The Northwest Potato Variety Development Program using conventional breeding has successfully released more than 40 improved varieties of potato since its inception in 1983. Potato breeders rely primarily on morphological and phenotypic data for selection and breeding of potato cultivars. With the a...
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Genome Variation Within Triticale in Comparison to its Wheat and Rye Progenitors
USDA-ARS?s Scientific Manuscript database
Genome variation in the intergeneric wheat-rye hybrid triticale (X Triticosecale Wittmack) has been a puzzle to scientists and plant breeders since the first triticale was synthesized. The existence of unexplained genetic variation in triticale as compared to the parents has been a hindrance to bre...
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Dedication: Major M. Goodman, Maize Breeder and Geneticist
USDA-ARS?s Scientific Manuscript database
Major M. Goodman is the leading expert on the classification and use of the diverse genetic resources of maize. He pioneered the development and use of mathematical approaches to classification of diverse plant materials; had a primary role in the development of one of the first comprehensive plant ...
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USDA-ARS?s Scientific Manuscript database
Premise of the study: Develop microsatellites from Fothergilla ×intermedia to establish loci capable of distinguishing species and cultivars, and assess genetic diversity for use by ornamental breeders, and for transfer within Hamamelidaceae. Methods and Results: A small insert genomic library enric...
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USDA-ARS?s Scientific Manuscript database
Random mating (i.e., panmixis) is a fundamental assumption in quantitative genetics. In outcrossing bee-pollinated perennial forage legume polycrosses, mating is assumed by default to follow theoretical random mating. This assumption informs breeders of expected inbreeding estimates based on polycro...
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Heritability estimations for inner muscular fat in Hereford cattle using random regressions
USDA-ARS?s Scientific Manuscript database
Random regressions make possible to make genetic predictions and parameters estimation across a gradient of environments, allowing a more accurate and beneficial use of animals as breeders in specific environments. The objective of this study was to use random regression models to estimate heritabil...
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Genetic Improvement of Potato for Tuber Calcium Uptake
USDA-ARS?s Scientific Manuscript database
Tuber internal quality is a major limiting factor for the U.S. potato industry. Breeders invest time and money in producing advanced selections which, in the end, often fail because of tuber internal defects, tuber bruising, or storage quality issues. In-season fertilization with calcium is known to...
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USDA-ARS?s Scientific Manuscript database
Genetic marker haplotypes for the Waxy and alk genes are associated with amylose content and gelatinization temperature, respectively, and are used by breeders to develop rice cultivars that have physicochemical properties desired by the parboiling and canning industries. Cultivars that provide cons...
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Allele-Specific Transcription Factor Binding in Pig Calpastatin Promoter Regions
USDA-ARS?s Scientific Manuscript database
The identification of predictive DNA markers for pork quality would allow U.S. pork producers and breeders to more quickly and efficiently select genetically superior animals for production of consistent, high quality meat. Genome scans have identified QTL for tenderness on pig chromosome 2 which ha...
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Predictive markers in calpastatin for tenderness in commercial pig populations
USDA-ARS?s Scientific Manuscript database
The identification of predictive DNA markers for pork quality would allow U.S. pork producers and breeders to more quickly and efficiently select genetically superior animals for production of consistent, high quality meat. Genome scans have identified QTL for tenderness on pig chromosome 2 which ha...
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Association of Functional SNPs in Pig Calpastatin Regulatory Regions with Tenderness
USDA-ARS?s Scientific Manuscript database
The identification of predictive DNA markers for pork quality would allow U.S. pork producers and breeders to more quickly and efficiently select genetically superior animals for production of consistent, high quality meat. Genome scans have identified QTL for tenderness on pig chromosome 2 which ha...
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A simple language to script and simulate breeding schemes: the breeding scheme language
USDA-ARS?s Scientific Manuscript database
It is difficult for plant breeders to determine an optimal breeding strategy given that the problem involves many factors, such as target trait genetic architecture and breeding resource availability. There are many possible breeding schemes for each breeding program. Although simulation study may b...
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The Austrian x red pine hybrid
W. B. Critchfield
1963-01-01
The genetic improvement of red pine (Pinus resinosa Ait.) presents tree breeders with one of their most difficult problems. Not only is this valuable species remarkably uniform, but until 1955 it resisted all attempts to cross it with other pines. In that year red pine and Austrian pine (P. nigra var. austriaca [...
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Henning, Yoshiyuki; Vole, Christiane; Begall, Sabine; Bens, Martin; Broecker-Preuss, Martina; Sahm, Arne; Szafranski, Karol; Burda, Hynek; Dammann, Philip
2014-01-01
Ansell's mole-rats (Fukomys anselli) are subterranean, long-lived rodents, which live in eusocial families, where the maximum lifespan of breeders is twice as long as that of non-breeders. Their metabolic rate is significantly lower than expected based on allometry, and their retinae show a high density of S-cone opsins. Both features may indicate naturally low thyroid hormone levels. In the present study, we sequenced several major components of the thyroid hormone pathways and analyzed free and total thyroxine and triiodothyronine in serum samples of breeding and non-breeding F. anselli to examine whether a) their thyroid hormone system shows any peculiarities on the genetic level, b) these animals have lower hormone levels compared to euthyroid rodents (rats and guinea pigs), and c) reproductive status, lifespan and free hormone levels are correlated. Genetic analyses confirmed that Ansell's mole-rats have a conserved thyroid hormone system as known from other mammalian species. Interspecific comparisons revealed that free thyroxine levels of F. anselli were about ten times lower than of guinea pigs and rats, whereas the free triiodothyronine levels, the main biologically active form, did not differ significantly amongst species. The resulting fT4:fT3 ratio is unusual for a mammal and potentially represents a case of natural hypothyroxinemia. Comparisons with total thyroxine levels suggest that mole-rats seem to possess two distinct mechanisms that work hand in hand to downregulate fT4 levels reliably. We could not find any correlation between free hormone levels and reproductive status, gender or weight. Free thyroxine may slightly increase with age, based on sub-significant evidence. Hence, thyroid hormones do not seem to explain the different ageing rates of breeders and non-breeders. Further research is required to investigate the regulatory mechanisms responsible for the unusual proportion of free thyroxine and free triiodothyronine. PMID:25409169
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Henning, Yoshiyuki; Vole, Christiane; Begall, Sabine; Bens, Martin; Broecker-Preuss, Martina; Sahm, Arne; Szafranski, Karol; Burda, Hynek; Dammann, Philip
2014-01-01
Ansell's mole-rats (Fukomys anselli) are subterranean, long-lived rodents, which live in eusocial families, where the maximum lifespan of breeders is twice as long as that of non-breeders. Their metabolic rate is significantly lower than expected based on allometry, and their retinae show a high density of S-cone opsins. Both features may indicate naturally low thyroid hormone levels. In the present study, we sequenced several major components of the thyroid hormone pathways and analyzed free and total thyroxine and triiodothyronine in serum samples of breeding and non-breeding F. anselli to examine whether a) their thyroid hormone system shows any peculiarities on the genetic level, b) these animals have lower hormone levels compared to euthyroid rodents (rats and guinea pigs), and c) reproductive status, lifespan and free hormone levels are correlated. Genetic analyses confirmed that Ansell's mole-rats have a conserved thyroid hormone system as known from other mammalian species. Interspecific comparisons revealed that free thyroxine levels of F. anselli were about ten times lower than of guinea pigs and rats, whereas the free triiodothyronine levels, the main biologically active form, did not differ significantly amongst species. The resulting fT4:fT3 ratio is unusual for a mammal and potentially represents a case of natural hypothyroxinemia. Comparisons with total thyroxine levels suggest that mole-rats seem to possess two distinct mechanisms that work hand in hand to downregulate fT4 levels reliably. We could not find any correlation between free hormone levels and reproductive status, gender or weight. Free thyroxine may slightly increase with age, based on sub-significant evidence. Hence, thyroid hormones do not seem to explain the different ageing rates of breeders and non-breeders. Further research is required to investigate the regulatory mechanisms responsible for the unusual proportion of free thyroxine and free triiodothyronine.
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Installation of automatic control at experimental breeder reactor II
DOE Office of Scientific and Technical Information (OSTI.GOV)
Larson, H.A.; Booty, W.F.; Chick, D.R.
1985-08-01
The Experimental Breeder Reactor II (EBR-II) has been modified to permit automatic control capability. Necessary mechanical and electrical changes were made on a regular control rod position; motor, gears, and controller were replaced. A digital computer system was installed that has the programming capability for varied power profiles. The modifications permit transient testing at EBR-II. Experiments were run that increased power linearly as much as 4 MW/s (16% of initial power of 25 MW(thermal)/s), held power constant, and decreased power at a rate no slower than the increase rate. Thus the performance of the automatic control algorithm, the mechanical andmore » electrical control equipment, and the qualifications of the driver fuel for future power change experiments were all demonstrated.« less
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Fan, Marie; Teunissen, Niki; Hall, Michelle L; Hidalgo Aranzamendi, Nataly; Kingma, Sjouke A; Roast, Michael; Delhey, Kaspar; Peters, Anne
2018-06-25
The evolution of conspicuous male traits is thought to be driven by female mate choice or male-male competition. These two mechanisms are often viewed as distinct processes, with most studies focusing on female choice. However, both mechanisms of sexual selection can act simultaneously on the same trait (i.e., dual function) and/or interact in a synergistic or conflicting way. Dual-function traits are commonly assumed to originate through male-male competition before being used in female choice; yet, most studies focusing on such traits could not determine the direction of change, lacking phylogenetic information. We investigated the role of conspicuous male seasonal plumage in male-male competitive interactions in the purple-crowned fairy-wren Malurus coronatus, a cooperatively breeding bird. Male breeding plumage in most Malurus species is selected by female choice through extra-pair mate choice, but unlike its congeners, M. coronatus is genetically monogamous, and females do not seem to choose males based on breeding plumage acquisition. Our study shows that, within groups, subordinate males that were older, and therefore higher-ranked in the queue for breeder position inheritance, produced a more complete breeding plumage. In line with this, subordinate males that were older and/or displayed a more complete breeding plumage were more successful in competitively acquiring a breeder position. A role as a signal of competitive ability was experimentally confirmed by presenting models of males: in breeding colours, these received more aggression from resident breeder males than in nonbreeding colours, but elicited limited response from females, consistent with competitors in breeding plumage being perceived as a bigger threat to the breeder male. The role of the conspicuous breeding plumage in mediating male-male interactions might account for its presence in this genetically monogamous species. As phylogenetic reconstructions suggest a past female choice function in M. coronatus, this could represent a sexual trait that shifted functions, or a dual-function trait that lost one function. These evolutionary scenarios imply that intra- and intersexual functions of ornaments may be gained or lost independently and offer new perspectives in understanding the complex dynamics of sexual selection. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.
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Detection and characterization of chicken anemia virus from commercial broiler breeder chickens
Hailemariam, Zerihun; Omar, Abdul Rahman; Hair-Bejo, Mohd; Giap, Tan Ching
2008-01-01
Background Chicken anemia virus (CAV) is the causative agent of chicken infectious anemia (CIA). Study on the type of CAV isolates present and their genetic diversity, transmission to their progeny and level of protection afforded in the breeder farms is lacking in Malaysia. Hence, the present study was aimed to detect CAV from commercial broiler breeder farms and characterize CAV positive samples based on sequence and phylogenetic analysis of partial VP1 gene. Results A total of 12 CAV isolates from different commercial broiler breeder farms were isolated and characterized. Detection of CAV positive embryos by the PCR assay in the range of 40 to 100% for different farms indicated high level of occurrence of vertical transmission of viral DNA to the progeny. CAV antigen was detected in the thymus and in the bone marrow but not in spleen, liver, duodenum, ovary and oviduct by indirect immunoperoxidase staining. The 12 CAV isolates were characterized based on partial sequences of VP1 gene. Six isolates (MF1A, MF3C, M3B5, NF4A, P12B and P24A) were found to have maximum homology with previously characterized Malaysian isolate SMSC-1, four isolates (M1B1, NF3A, PYT4 and PPW4) with isolate BL-5 and the remaining two (NF1D and NF2C) have maximum homology both with isolates 3-1 and BL-5. Meanwhile, seven of the isolates with amino acid profile of 75-I, 97-L, 139-Q and 144-Q were clustered together in cluster I together with other isolates from different geographical places. The remaining five isolates with amino acid profile of 75-V, 97-M, 139-K and 144-E were grouped under cluster II. All the CAV isolates demonstrated omega values (Ka/Ks) of less than one (the values ranging from 0.07 to 0.5) suggesting the occurrence of purifying (negative) selection in all the studied isolates. Conclusion The present study showed that CAV is widespread in the studied commercial broiler breeder farms. The result also indicated the occurrence of genetic variability in local CAV isolates that can be divided at least into two groups based on characteristic amino acid substitutions at positions 75, 97, 139 and 144 of the VP1 protein. PMID:18954433
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Detection and characterization of chicken anemia virus from commercial broiler breeder chickens.
Hailemariam, Zerihun; Omar, Abdul Rahman; Hair-Bejo, Mohd; Giap, Tan Ching
2008-10-27
Chicken anemia virus (CAV) is the causative agent of chicken infectious anemia (CIA). Study on the type of CAV isolates present and their genetic diversity, transmission to their progeny and level of protection afforded in the breeder farms is lacking in Malaysia. Hence, the present study was aimed to detect CAV from commercial broiler breeder farms and characterize CAV positive samples based on sequence and phylogenetic analysis of partial VP1 gene. A total of 12 CAV isolates from different commercial broiler breeder farms were isolated and characterized. Detection of CAV positive embryos by the PCR assay in the range of 40 to 100% for different farms indicated high level of occurrence of vertical transmission of viral DNA to the progeny. CAV antigen was detected in the thymus and in the bone marrow but not in spleen, liver, duodenum, ovary and oviduct by indirect immunoperoxidase staining. The 12 CAV isolates were characterized based on partial sequences of VP1 gene. Six isolates (MF1A, MF3C, M3B5, NF4A, P12B and P24A) were found to have maximum homology with previously characterized Malaysian isolate SMSC-1, four isolates (M1B1, NF3A, PYT4 and PPW4) with isolate BL-5 and the remaining two (NF1D and NF2C) have maximum homology both with isolates 3-1 and BL-5. Meanwhile, seven of the isolates with amino acid profile of 75-I, 97-L, 139-Q and 144-Q were clustered together in cluster I together with other isolates from different geographical places. The remaining five isolates with amino acid profile of 75-V, 97-M, 139-K and 144-E were grouped under cluster II. All the CAV isolates demonstrated omega values (Ka/Ks) of less than one (the values ranging from 0.07 to 0.5) suggesting the occurrence of purifying (negative) selection in all the studied isolates. The present study showed that CAV is widespread in the studied commercial broiler breeder farms. The result also indicated the occurrence of genetic variability in local CAV isolates that can be divided at least into two groups based on characteristic amino acid substitutions at positions 75, 97, 139 and 144 of the VP1 protein.
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LDHA gene is associated with pigeon survivability during racing competitions
Ramadan, Sherif; Miyake, Takeshi; Yamaura, Junichi
2018-01-01
Pigeon racing is a popular sport worldwide. Pigeons are under continuous selection to improve speed, spatial orientation, and endurance during long flights. However, numerous genetic and non-genetic factors affect survivability and homing ability, making such traits difficult for breeders to control. Polymorphisms in the lactate dehydrogenase A gene (LDHA) likely affects pigeon racing and homing abilities, due to its role in physical and mental performance. Additionally, the adenylate cyclase activating polypeptide 1 gene (ADCYAP1) has been associated with physiological and behavioral shifts that occur during avian migration. In this study, we examined the association between LDHA and ADCYAP1 genotypes with pigeon survivability during racing competitions. Survivability was evaluated through the estimated breeding value (EBV) of each individual’s total race distances during its athletic life. ADCYAP1 was not polymorphic among our samples, while LDHA genotypes were significantly associated with deviated EBV values of longer total race distance; individuals carrying the S+ genotype had higher EBV (i.e., greater survivability). Thus, the LDHA locus might be useful for marker-assisted selection, empowering breeders and trainers to maximize pigeon quality. Moreover, data obtained from breeding will also improve our understanding of the genetic mechanism underlying navigation and flight for wild migrating bird species. PMID:29775483
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Perrier, Charles; Normandeau, Éric; Dionne, Mélanie; Richard, Antoine; Bernatchez, Louis
2014-01-01
While nonanadromous males (stream-resident and/or mature male parr) contribute to reproduction in anadromous salmonids, little is known about their impacts on key population genetic parameters. Here, we evaluated the contribution of Atlantic salmon mature male parr to the effective number of breeders (Nb) using both demographic (variance in reproductive success) and genetic (linkage disequilibrium) methods, the number of alleles, and the relatedness among breeders. We used a recently published pedigree reconstruction of a wild anadromous Atlantic salmon population in which 2548 fry born in 2010 were assigned parentage to 144 anadromous female and 101 anadromous females that returned to the river to spawn in 2009 and to 462 mature male parr. Demographic and genetic methods revealed that mature male parr increased population Nb by 1.79 and 1.85 times, respectively. Moreover, mature male parr boosted the number of alleles found among progenies. Finally, mature male parr were in average less related to anadromous females than were anadromous males, likely because of asynchronous sexual maturation between mature male parr and anadromous fish of a given cohort. By increasing Nb and allelic richness, and by decreasing inbreeding, the reproductive contribution of mature male parr has important evolutionary and conservation implications for declining Atlantic salmon populations. PMID:25553070
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NASA Astrophysics Data System (ADS)
Shi, Y.; Veeranampalayam-Sivakumar, A. N.; Li, J.; Ge, Y.; Schnable, J. C.; Rodriguez, O.; Liang, Z.; Miao, C.
2017-12-01
Low-altitude aerial imagery collected by unmanned aircraft systems (UAS) at centimeter-level spatial resolution provides great potential to collect high throughput plant phenotyping (HTP) data and accelerate plant breeding. This study is focused on UAS-based HTP for breeding increased water use efficiency in corn in eastern Nebraska. The field trail is part of an effort by the Genomes to Fields consortium effort to grow and phenotype many of the same corn (maize) hybrids at approximately 40 locations across the United States and Canada in order to stimulate new research in crop modeling, the development of new plant phenotyping technologies and the identification of genetic loci that control the adaptation of specific corn (maize) lines to specific environments. It included approximately 250 maize hybrids primary generated using recently off patent material from major seed companies. These lines are the closest material to what farmers are growing today which can be legally used for research purposes and genotyped by the public sector. During the growing season, a hexacopter equipped with a multispectral and a RGB cameras was flown and used to image this 1-hectare field trial near Mead, NE. Sensor data from the UAS were correlated directly with grain yield, measured at the end of the growing season, and were also be used to quantify other traits of interest to breeders including flowering date, plant height, leaf orientation, canopy spectral, and stand count. The existing challenges of field data acquisition (to ensure data quality) and development of effective image processing algorithms (such as detecting corn tassels) will be discussed. The success of this study and others like it will speed up the process of phenotypic data collection, and provide more accurate and detailed trait data for plant biologists, plant breeders, and other agricultural scientists. Employing advanced UAS-based machine vision technologies in agricultural applications have the potential to increase the rate of genetic gain in plant breeding applications, as well as guide the optimization of management practices in precision agriculture.
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Resistance to the Gal-M gametophyte factor in maize: A genetic solution to an undervalued risk
USDA-ARS?s Scientific Manuscript database
Due to maize’s wind-driven pollination, non-target pollen contamination is problematic for producers and breeders. Maize gametophyte factors have long been used to produce selectively pollinating phenotypes. The use of these factors is the cornerstone of commercial popcorn production, and they are u...
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A unified SNP map of sunflower (Helianthus annuus L.) derived from current genomic resources
USDA-ARS?s Scientific Manuscript database
Dense genetic maps are critical tools for plant breeders and geneticists. While many maps have been developed for sunflower in the last few decades, most have been based on low-throughput technologies and include markers numbers in the hundreds. However, two maps with reasonably dense coverage of a...
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USDA-ARS?s Scientific Manuscript database
Plant breeders require genetic diversity in their breeding programs to develop cultivars that are productive, nutritious, tolerant of biotic and abiotic stresses, and make efficient use of water and fertilizer. The USDA-ARS National Plant Germplasm System (NPGS) is a major source for global plant ge...
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Pine pollen collections dates - annual and geographic variation
J. W. Duffield
1953-01-01
Activity in pine breeding has increased throughout the temperate forest regions of the world since the Institute of Forest Genetics issued its first summary of pollen collection dates in 1947. Cooperation between pine breeders has increased at the same time. The information most essential for conducting cooperative breeding operations are the dates of pollen collection...
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USDA-ARS?s Scientific Manuscript database
Rice (Oryza sativa L.) is often exposed to cool or cold temperatures during spring planting in a temperate climate. A better understanding of the genetic pathways regulating this chilling tolerance will enable breeders to develop varieties with improved tolerance during the germination and young see...
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USDA-ARS?s Scientific Manuscript database
Plant breeders continually generate ever-higher yielding cultivars, but also want to improve seed constituent value, which in soybean [Glycine max (L.) Merr.] is seed protein and oil. Identification of genetic loci governing those two traits would facilitate that effort, and though genome-wide asso...
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USDA-ARS?s Scientific Manuscript database
The ability to access alleles from unadapted germplasm collections is a long-standing problem for geneticists and breeders. Here we developed, characterized, and demonstrated the utility of a wild barley advanced backcross-nested association mapping (AB-NAM) population. We developed this population ...
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Hybridizing pines with diluted pollen
Robert Z. Callaham
1967-01-01
Diluted pollens would have many uses by the tree breeder. Dilutions would be particularly advantageous in making many controlled pollinations with a limited amount of pollen. They also would be useful in artificial mass pollinations of orchards or single trees. Diluted pollens might help overcome troublesome genetic barriers to crossing. Feasibility o,f using diluted...
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Alipour, Hadi; Bihamta, Mohammad R.; Mohammadi, Valiollah; Peyghambari, Seyed A.; Bai, Guihua; Zhang, Guorong
2017-01-01
Background: Genetic diversity is an essential resource for breeders to improve new cultivars with desirable characteristics. Recently, genotyping-by-sequencing (GBS), a next-generation sequencing (NGS) technology that can simplify complex genomes, has now be used as a high-throughput and cost-effective molecular tool for routine breeding and screening in many crop species, including the species with a large genome. Results: We genotyped a diversity panel of 369 Iranian hexaploid wheat accessions including 270 landraces collected between 1931 and 1968 in different climate zones and 99 cultivars released between 1942 to 2014 using 16,506 GBS-based single nucleotide polymorphism (GBS-SNP) markers. The B genome had the highest number of mapped SNPs while the D genome had the lowest on both the Chinese Spring and W7984 references. Structure and cluster analyses divided the panel into three groups with two landrace groups and one cultivar group, suggesting a high differentiation between landraces and cultivars and between landraces. The cultivar group can be further divided into four subgroups with one subgroup was mostly derived from Iranian ancestor(s). Similarly, landrace groups can be further divided based on years of collection and climate zones where the accessions were collected. Molecular analysis of variance indicated that the genetic variation was larger between groups than within group. Conclusion: Obvious genetic diversity in Iranian wheat was revealed by analysis of GBS-SNPs and thus breeders can select genetically distant parents for crossing in breeding. The diverse Iranian landraces provide rich genetic sources of tolerance to biotic and abiotic stresses, and they can be useful resources for the improvement of wheat production in Iran and other countries. PMID:28912785
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What drives seasonal fluctuations of body condition in a semelparous income breeder octopus?
NASA Astrophysics Data System (ADS)
Quetglas, Antoni; Ordines, Francesc; Valls, Maria
2011-09-01
The vast majority of modern cephalopods is single-season breeders, or semelparous in the strict sense, that die soon after the reproduction takes place. Individual body condition in these marine invertebrates is expected to be highly affected by reproduction because: 1) the gonad weight of females, which represents <1% of body weight when immature, increases up to 20-50% during maturation; and 2) octopus females reduce or even cease their food intake during breeding. Based on this expectation, we analysed the interrelationship between condition and reproduction in the temperate octopus Eledone cirrhosa. Results from a previous work using biochemical analyses showed that reproduction in this species is not fuelled by stored reserves (capital breeder), but by food intakes (income breeder). Since income breeders depend strongly on food resources, the effect of several environmental variables related to food availability such as primary production, sea temperature (ST) and river discharges were also analysed. Condition showed a marked intrannual cycle independently of the sex and, noteworthy, the maturity stage. Given that immature individuals are not expected to display seasonal fluctuations in body condition related to maturation, these results preclude reproduction as a driving factor for the observed circannual cycle. Condition was significantly correlated with all the environmental variables analysed, except with ST at the depths where the species lives. Although this last result also precludes concurrent ST as a driving factor of body condition, those correlations suggest that condition might display an intrinsic seasonal cycle, as many other life-history traits in most species such as reproduction, migration or moulting. Finally, there also remains the possibility that condition in this octopus species is determined genetically, as has been reported in recent studies across different taxonomical groups.
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Ortiz, Rodomiro; Simon, Philipp; Jansky, Shelley; Stelly, David
2009-01-01
Background Emeritus Campbell-Bascom Professor Stanley J. Peloquin was an internationally renowned plant geneticist and breeder who made exceptional contributions to the quantity, quality and sustainable supply of food for the world from his innovative and extensive scientific contributions. For five decades, Dr Peloquin merged basic research in plant reproduction, cytology, cytogenetics, genetics, potato (Solanum tuberosum) improvement and education at the University of Wisconsin-Madison. Successive advances across these five decades redefined scientific comprehension of reproductive variation, its genetic control, genetic effects, evolutionary impact and utility for breeding. In concert with the International Potato Center (CIP), he and others translated the advances into application, resulting in large benefits on food production worldwide, exemplifying the importance of integrated innovative university research and graduate education to meet domestic and international needs. Scope Dr Peloquin is known to plant breeders, geneticists, international agricultural economists and potato researchers for his enthusiastic and incisive contributions to genetic enhancement of potato using haploids, 2n gametes and wild Solanum species; for his pioneering work on potato cultivation through true seed; and as mentor of a new generation of plant breeders worldwide. The genetic enhancement of potato, the fourth most important food crop worldwide, benefited significantly from expanded germplasm utilization and advanced reproductive genetic knowledge, which he and co-workers, including many former students, systematically transformed into applied breeding methods. His research on plant sexual reproduction included subjects such as haploidization and polyploidization, self- and cross-incompatibility, cytoplasmic male sterility and restorer genes, gametophytic/sporophytic heterozygosity and male fertility, as well as endosperm dosages and seed development. By defining methods of half-tetrad analysis and new cytological techniques, he elucidated modes, mechanisms and genetic controls and effects of 2n gametes in Solanum. Ramifications extend to many other crops and plants, in both basic and applied sciences. Achievements Based upon a foundation of genetics, cytogenetics and plant reproductive biology, Dr Peloquin and co-workers developed methods to use 2n gametes and haploids for breeding, and used them to move genes for important horticultural traits from wild tuber-bearing Solanum species to cultivated potato for the betterment of agriculture. The resulting potato germplasm included combinations of yield, adaptation, quality and disease resistance traits that were previously unavailable. This elite plant germplasm was utilized and distributed to 85 countries by the CIP, because it not only increased potato yields and quality, it also broadened the adaptation of potato to lowland tropical regions, where humanity has benefited from this addition to their food supply. PMID:19689972
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An integrated approach for increasing breeding efficiency in apple and peach in Europe.
Laurens, Francois; Aranzana, Maria José; Arus, Pere; Bassi, Daniele; Bink, Marco; Bonany, Joan; Caprera, Andrea; Corelli-Grappadelli, Luca; Costes, Evelyne; Durel, Charles-Eric; Mauroux, Jehan-Baptiste; Muranty, Hélène; Nazzicari, Nelson; Pascal, Thierry; Patocchi, Andrea; Peil, Andreas; Quilot-Turion, Bénédicte; Rossini, Laura; Stella, Alessandra; Troggio, Michela; Velasco, Riccardo; van de Weg, Eric
2018-01-01
Despite the availability of whole genome sequences of apple and peach, there has been a considerable gap between genomics and breeding. To bridge the gap, the European Union funded the FruitBreedomics project (March 2011 to August 2015) involving 28 research institutes and private companies. Three complementary approaches were pursued: (i) tool and software development, (ii) deciphering genetic control of main horticultural traits taking into account allelic diversity and (iii) developing plant materials, tools and methodologies for breeders. Decisive breakthroughs were made including the making available of ready-to-go DNA diagnostic tests for Marker Assisted Breeding, development of new, dense SNP arrays in apple and peach, new phenotypic methods for some complex traits, software for gene/QTL discovery on breeding germplasm via Pedigree Based Analysis (PBA). This resulted in the discovery of highly predictive molecular markers for traits of horticultural interest via PBA and via Genome Wide Association Studies (GWAS) on several European genebank collections. FruitBreedomics also developed pre-breeding plant materials in which multiple sources of resistance were pyramided and software that can support breeders in their selection activities. Through FruitBreedomics, significant progresses were made in the field of apple and peach breeding, genetics, genomics and bioinformatics of which advantage will be made by breeders, germplasm curators and scientists. A major part of the data collected during the project has been stored in the FruitBreedomics database and has been made available to the public. This review covers the scientific discoveries made in this major endeavour, and perspective in the apple and peach breeding and genomics in Europe and beyond.
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NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
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Genetic Algorithms and Local Search
NASA Technical Reports Server (NTRS)
Whitley, Darrell
1996-01-01
The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.
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USDA-ARS?s Scientific Manuscript database
Development and utilization of genetic markers play a pivotal role in marker assisted breeding of wheat cultivars with pyramids of disease resistance genes. The objective of this study is to develop a closed tube, gel-free assay for high throughput genotyping of leaf rust resistance locus Lr21. Poly...
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Methodology for creating alloplasmic soybean lines by using Glycine tomentella as a maternal parent
USDA-ARS?s Scientific Manuscript database
Soybean breeders have not exploited the diversity of the 26 wild perennial species of the subgenus Glycine Willd. that are distantly related to soybean [G. max (L.) Merr.]. The objectives of this study were to introgress cytoplasmic and genetic diversity from G. tomentella PI 441001 (2n=78) into the...
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USDA-ARS?s Scientific Manuscript database
Leaf shape varies spectacularly among plants. Leaves are the primary source of photo-assimilate in crop plants and understanding the genetic basis of variation in leaf morphology is critical to improving agricultural productivity. Leaf shape played a unique role in cotton improvement, as breeders ha...
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USDA-ARS?s Scientific Manuscript database
Birth weight (BW) records of 28,638 Brangus and Simbrah calves (12,295 of which were produced by embryo transfer) were provided by a private seedstock breeder. The objectives were to determine the genetic mechanism(s) responsible for previously observed 12.3 and 6.9 kg differences in birth weight b...
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USDA-ARS?s Scientific Manuscript database
The economic value of broken rice is about half that of whole milled rice, so one goal of producers, millers, and rice breeders is to reduce broken grains that result from the dehusking and milling processes One of the primary causes of rice breakage is fissuring, or cracking, of the rice before it ...
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USDA-ARS?s Scientific Manuscript database
Wheat quality is defined by culinary end-uses and processing characteristics. Wheat breeders are interested to identify quantitative trait loci for grain, milling, and end-use quality traits because it is imperative to understand the genetic complexity underlying quantitatively inherited traits to ...
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USDA-ARS?s Scientific Manuscript database
Among the fundamental evolutionary forces, recombination arguably has the largest impact on the practical work of plant breeders. Varying over 1,000-fold across the maize genome, the local meiotic recombination rate limits the resolving power of quantitative trait mapping and the precision of favora...
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USDA-ARS?s Scientific Manuscript database
Tuber quality traits are a major interest for breeders and the potato chip industry. This research intended to generate populations that can be suitable for the genetic study of tuber calcium, internal quality, common scab, and other commercially important traits such as yield, specific gravity and ...
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Emerging Avenues for Utilization of Exotic Germplasm.
Wang, Cuiling; Hu, Songlin; Gardner, Candice; Lübberstedt, Thomas
2017-07-01
Breeders have been successful in increasing crop performance by exploiting genetic diversity over time. However, the reported annual yield increases are not sufficient in view of rapid human population growth and global environmental changes. Exotic germplasm possesses high levels of genetic diversity for valuable traits. However, only a small fraction of naturally occurring genetic diversity is utilized. Moreover, the yield gap between elite and exotic germplasm widens, which increases the effort needed to use exotic germplasm and to identify beneficial alleles and for their introgression. The advent of high-throughput genotyping and phenotyping technologies together with emerging biotechnologies provide new opportunities to explore exotic genetic variation. This review will summarize potential challenges for utilization of exotic germplasm and provide solutions. Copyright © 2017 Elsevier Ltd. All rights reserved.
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Eitan, Y; Lipkin, Ehud; Soller, M
2014-05-01
During the 1990s, various disturbances arose affecting broiler breeder females at entry into lay. These disturbances were associated with even slight overfeeding during release of feed restriction in this critical maturation period. The present experiment was carried out to gain some insight into the causes of these disturbances by comparing the effect of fast (FF) and slow (SF) release from feed restriction at entry into lay in 2 broiler breeder populations: B1980, representing the genetic level of 1980, and B2000, the genetic level of 2000. Under the FF treatment, B1980 entered lay 19.2 d earlier than B2000; this increased to 37.4 d earlier under SF. The B1980 population entered lay at virtually the same mean age for SF and FF, whereas B2000 entered lay 15.7 d earlier under the FF. Body weight at first egg were 2,621 g for the B1980 and 3,591 g for B2000. Differences in BW at first egg between feeding treatments within lines were minor. As a percentage of BW, ovary, oviduct, and follicle weights were the same for B1980 and B2000; breast weight was 14.9% for B1980 and 21.2% for B2000; abdominal fat pad weight was 5.37% for B1980 and 2.67% for B2000. Follicle weight and absolute difference in weight between successive follicles was greater in B2000 than in B1980. It is concluded that body fat content does not limit entry into lay, and that threshold BW for onset of sexual maturity of broiler breeder hens increased by about 1,000 g between 1980 and 2000, indicating a tight association between juvenile growth rate and threshold BW for onset of sexual maturity. It is also concluded that disturbances at entry into lay due to overfeeding are not due to smaller differences between successive follicles in B2000 compared with B1980. There are hints, however, that overfeeding may contribute to these disturbances by decreasing differences between successive follicles.
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Genetic and economic evaluation of Japanese Black (Wagyu) cattle breeding schemes.
Kahi, A K; Hirooka, H
2005-09-01
Deterministic simulation was used to evaluate 10 breeding schemes for genetic gain and profitability and in the context of maximizing returns from investment in Japanese Black cattle breeding. A breeding objective that integrated the cow-calf and feedlot segments was considered. Ten breeding schemes that differed in the records available for use as selection criteria were defined. The schemes ranged from one that used carcass traits currently available to Japanese Black cattle breeders (Scheme 1) to one that also included linear measurements and male and female reproduction traits (Scheme 10). The latter scheme represented the highest level of performance recording. In all breeding schemes, sires were chosen from the proportion selected during the first selection stage (performance testing), modeling a two-stage selection process. The effect on genetic gain and profitability of varying test capacity and number of progeny per sire and of ultrasound scanning of live animals was examined for all breeding schemes. Breeding schemes that selected young bulls during performance testing based on additional individual traits and information on carcass traits from their relatives generated additional genetic gain and profitability. Increasing test capacity resulted in an increase in genetic gain in all schemes. Profitability was optimal in Scheme 2 (a scheme similar to Scheme 1, but selection of young bulls also was based on information on carcass traits from their relatives) to 10 when 900 to 1,000 places were available for performance testing. Similarly, as the number of progeny used in the selection of sires increased, genetic gain first increased sharply and then gradually in all schemes. Profit was optimal across all breeding schemes when sires were selected based on information from 150 to 200 progeny. Additional genetic gain and profitability were generated in each breeding scheme with ultrasound scanning of live animals for carcass traits. Ultrasound scanning of live animals was more important than the addition of any other traits in the selection criteria. These results may be used to provide guidance to Japanese Black cattle breeders.
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Humans are not cooperative breeders but practice biocultural reproduction.
Bogin, Barry; Bragg, Jared; Kuzawa, Christopher
2014-01-01
Alloparental care and feeding of young is often called "cooperative breeding" and humans are increasingly described as being a cooperative breeding species. To critically evaluate whether the human offspring care system is best grouped with that of other cooperative breeders. (1) Review of the human system of offspring care in the light of definitions of cooperative, communal and social breeding; (2) re-analysis of human lifetime reproductive effort. Human reproduction and offspring care are distinct from other species because alloparental behaviour is defined culturally rather than by genetic kinship alone. This system allows local flexibility in provisioning strategies and ensures that care and resources often flow between unrelated individuals. This review proposes the term "biocultural reproduction" to describe this unique human reproductive system. In a re-analysis of human life history data, it is estimated that the intense alloparenting typical of human societies lowers the lifetime reproductive effort of individual women by 14-29% compared to expectations based upon other mammals. Humans are not cooperative breeders as classically defined; one effect of the unique strategy of human biocultural reproduction is a lowering of human lifetime reproductive effort, which could help explain lifespan extension.
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2010-11-01
total breeding population size for both F. p. tundrius and F. p. anatum is between 4,300 and 10,400 [31]; considering immature and floater (non... floaters ) can outnumber the actual breeders in some areas by severalfold [13,139], and the numbers of migrants recorded at specific monitoring sites
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USDA-ARS?s Scientific Manuscript database
Powdery mildew of pea is caused by Erysiphe pisi DC and is a serious threat to pea (Pisum sativum L.) production throughout much of the world. Development and utilization of genetic resistance to powdery mildew is considered an effective and sustainable strategy to manage this disease. One gene, er1...
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Molecular basis of adaptation to high soil boron in wheat landraces and elite cultivars.
Pallotta, Margaret; Schnurbusch, Thorsten; Hayes, Julie; Hay, Alison; Baumann, Ute; Paull, Jeff; Langridge, Peter; Sutton, Tim
2014-10-02
Environmental constraints severely restrict crop yields in most production environments, and expanding the use of variation will underpin future progress in breeding. In semi-arid environments boron toxicity constrains productivity, and genetic improvement is the only effective strategy for addressing the problem. Wheat breeders have sought and used available genetic diversity from landraces to maintain yield in these environments; however, the identity of the genes at the major tolerance loci was unknown. Here we describe the identification of near-identical, root-specific boron transporter genes underlying the two major-effect quantitative trait loci for boron tolerance in wheat, Bo1 and Bo4 (ref. 2). We show that tolerance to a high concentration of boron is associated with multiple genomic changes including tetraploid introgression, dispersed gene duplication, and variation in gene structure and transcript level. An allelic series was identified from a panel of bread and durum wheat cultivars and landraces originating from diverse agronomic zones. Our results demonstrate that, during selection, breeders have matched functionally different boron tolerance alleles to specific environments. The characterization of boron tolerance in wheat illustrates the power of the new wheat genomic resources to define key adaptive processes that have underpinned crop improvement.
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Genetic counseling in the era of molecular diagnostics.
Traas, Anne M; Casal, Margret; Haskins, Mark; Henthorn, Paula
2006-08-01
Veterinarians with an interest in theriogenology will often be asked by small animal clients for advice concerning hereditary diseases in their breeds. Many new DNA-based tests for analysis of genetic diseases and traits (e.g. coat color) are now available for use by both breeders and veterinarians. With appropriate interpretation, these tests can be invaluable tools in a breeding program. For example, they can be used to produce animals free of specific diseases, to quickly eliminate a disease from an entire breed, or to select for specific traits in breeding stock. Selection strategies that do not take into account maintaining genetic diversity of the breed may be detrimental and reduce the potential for future improvement.
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Wilbe, M; Andersson, G
2012-01-01
Major histocompatibility complex (MHC) class II genes are important genetic risk factors for development of immune-mediated diseases in mammals. Recently, the dog (Canis lupus familiaris) has emerged as a useful model organism to identify critical MHC class II genotypes that contribute to development of these diseases. Therefore, a study aimed to evaluate a potential genetic association between the dog leukocyte antigen (DLA) class II region and an immune-mediated disease complex in dogs of the Nova Scotia duck tolling retriever breed was performed. We show that DLA is one of several genetic risk factors for this disease complex and that homozygosity of the risk haplotype is disadvantageous. Importantly, the disease is complex and has many genetic risk factors and therefore we cannot provide recommendations for breeders exclusively on the basis of genetic testing for DLA class II genotype. © 2012 Blackwell Verlag GmbH.
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Nishio, Sogo; Takada, Norio; Saito, Toshihiro; Yamamoto, Toshiya; Iketani, Hiroyuki
2016-06-14
Pears (Pyrus spp.) are one of the most important fruit crops in temperate regions. Japanese pear breeding has been carried out for over 100 years, working to release new cultivars that have good fruit quality and other desirable traits. Local cultivar 'Nijisseiki' and its relatives, which have excellent fruit texture, have been repeatedly used as parents in the breeding program. This strategy has led to inbreeding within recent cultivars and selections. To avoid inbreeding depression, we need to clarify the degree of inbreeding among crossbred cultivars and to introgress genetic resources that are genetically different from modern cultivars and selections. The objective of the present study was to clarify the genetic relatedness between modern Japanese pear cultivars and diverse Asian pear genetic resources. We genotyped 207 diverse accessions by using 19 simple sequence repeat (SSR) markers. The heterozygosity and allelic richness of modern cultivars was obviously decreased compared with that of wild individuals, Chinese pear cultivars, and local cultivars. In analyses using Structure software, the 207 accessions were classified into four clusters (K = 4): one consisting primarily of wild individuals, one of Chinese pear cultivars, one of local cultivars from outside the Kanto region, and one containing both local cultivars from the Kanto region and crossbred cultivars. The results of principal coordinate analysis (PCoA) were similar to those from the Structure analysis. Wild individuals and Chinese pears appeared to be distinct from other groups, and crossbred cultivars became closer to 'Nijisseiki' as the year of release became more recent. Both Structure and PCoA results suggest that the modern Japanese pear cultivars are genetically close to local cultivars that originated in the Kanto region, and that the genotypes of the modern cultivars were markedly biased toward 'Nijisseiki'. Introgression of germplasm from Chinese pear and wild individuals that are genetically different from modern cultivars seems to be key to broadening the genetic diversity of Japanese pear. The information obtained in this study will be useful for pear breeders and other fruit breeders who have observed inbreeding depression.
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Korbelik, Juraj; Rand, Jacquie S; Morton, John M
2011-10-15
OBJECTIVE-To compare early socialization practices between litters of breeders registered with the Canine Control Council (CCC) and litters of nonregistered breeders advertising puppies for sale in a local newspaper. DESIGN-Retrospective cohort study. Animals-80 litters of purebred and mixed-breed dogs from registered (n = 40) and non-registered (40) breeders. PROCEDURES-Registered breeders were randomly selected from the CCC website, and nonregistered breeders were randomly selected from a weekly advertising newspaper. The litter sold most recently by each breeder was then enrolled in the study. Information pertaining to socialization practices for each litter was obtained through a questionnaire administered over the telephone. RESULTS-Registered breeders generally had more breeding bitches and had more litters than did nonregistered breeders. Litters of registered breeders were more likely to have been socialized with adult dogs, people of different appearances, and various environmental stimuli, compared with litters of nonregistered breeders. Litters from registered breeders were also much less likely to have been the result of an unplanned pregnancy. CONCLUSIONS AND CLINICAL RELEVANCE-Among those breeders represented, litters of registered breeders received more socialization experience, compared with litters of nonregistered breeders. People purchasing puppies from nonregistered breeders should focus on socializing their puppies between the time of purchase and 14 weeks of age. Additional research is required to determine whether puppies from nonregistered breeders are at increased risk of behavioral problems and are therefore more likely to be relinquished to animal shelters or euthanized, relative to puppies from registered breeders.
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Problem solving with genetic algorithms and Splicer
NASA Technical Reports Server (NTRS)
Bayer, Steven E.; Wang, Lui
1991-01-01
Genetic algorithms are highly parallel, adaptive search procedures (i.e., problem-solving methods) loosely based on the processes of population genetics and Darwinian survival of the fittest. Genetic algorithms have proven useful in domains where other optimization techniques perform poorly. The main purpose of the paper is to discuss a NASA-sponsored software development project to develop a general-purpose tool for using genetic algorithms. The tool, called Splicer, can be used to solve a wide variety of optimization problems and is currently available from NASA and COSMIC. This discussion is preceded by an introduction to basic genetic algorithm concepts and a discussion of genetic algorithm applications.
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Genetic algorithms using SISAL parallel programming language
DOE Office of Scientific and Technical Information (OSTI.GOV)
Tejada, S.
1994-05-06
Genetic algorithms are a mathematical optimization technique developed by John Holland at the University of Michigan [1]. The SISAL programming language possesses many of the characteristics desired to implement genetic algorithms. SISAL is a deterministic, functional programming language which is inherently parallel. Because SISAL is functional and based on mathematical concepts, genetic algorithms can be efficiently translated into the language. Several of the steps involved in genetic algorithms, such as mutation, crossover, and fitness evaluation, can be parallelized using SISAL. In this paper I will l discuss the implementation and performance of parallel genetic algorithms in SISAL.
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Fister, Karin; Fister, Iztok; Murovec, Jana; Bohanec, Borut
2017-02-01
Plant breeders' rights are undergoing dramatic changes due to changes in patent rights in terms of plant variety rights protection. Although differences in the interpretation of »breeder's exemption«, termed research exemption in the 1991 UPOV, did exist in the past in some countries, allowing breeders to use protected varieties as parents in the creation of new varieties of plants, current developments brought about by patenting conventionally bred varieties with the European Patent Office (such as EP2140023B1) have opened new challenges. Legal restrictions on germplasm availability are therefore imposed on breeders while, at the same time, no practical information on how to distinguish protected from non-protected varieties is given. We propose here a novel approach that would solve this problem by the insertion of short DNA stretches (labels) into protected plant varieties by genetic transformation. This information will then be available to breeders by a simple and standardized procedure. We propose that such a procedure should consist of using a pair of universal primers that will generate a sequence in a PCR reaction, which can be read and translated into ordinary text by a computer application. To demonstrate the feasibility of such approach, we conducted a case study. Using the Agrobacterium tumefaciens transformation protocol, we inserted a stretch of DNA code into Nicotiana benthamiana. We also developed an on-line application that enables coding of any text message into DNA nucleotide code and, on sequencing, decoding it back into text. In the presented case study, a short command line coding the phrase »Hello world« was transformed into a DNA sequence that was inserted in the plant genome. The encoded message was reconstructed from the resulting T1 seedlings with 100 % accuracy. The feasibility and possible other applications of this approach are discussed.
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Genetic diversity trend in Indian rice varieties: an analysis using SSR markers.
Singh, Nivedita; Choudhury, Debjani Roy; Tiwari, Gunjan; Singh, Amit Kumar; Kumar, Sundeep; Srinivasan, Kalyani; Tyagi, R K; Sharma, A D; Singh, N K; Singh, Rakesh
2016-09-05
The knowledge of the extent and pattern of diversity in the crop species is a prerequisite for any crop improvement as it helps breeders in deciding suitable breeding strategies for their future improvement. Rice is the main staple crop in India with the large number of varieties released every year. Studies based on the small set of rice genotypes have reported a loss in genetic diversity especially after green revolution. However, a detailed study of the trend of diversity in Indian rice varieties is lacking. SSR markers have proven to be a marker of choice for studying the genetic diversity. Therefore, the present study was undertaken with the aim to characterize and assess trends of genetic diversity in a large set of Indian rice varieties (released between 1940-2013), conserved in the National Gene Bank of India using SSR markers. A set of 729 Indian rice varieties were genotyped using 36 HvSSR markers to assess the genetic diversity and genetic relationship. A total of 112 alleles was amplified with an average of 3.11 alleles per locus with mean Polymorphic Information Content (PIC) value of 0.29. Cluster analysis grouped these varieties into two clusters whereas the model based population structure divided them into three populations. AMOVA study based on hierarchical cluster and model based approach showed 3 % and 11 % variation between the populations, respectively. Decadal analysis for gene diversity and PIC showed increasing trend from 1940 to 2005, thereafter values for both the parameters showed decreasing trend between years 2006-2013. In contrast to this, allele number demonstrated increasing trend in these varieties released and notified between1940 to 1985, it remained nearly constant during 1986 to 2005 and again showed an increasing trend. Our results demonstrated that the Indian rice varieties harbors huge amount of genetic diversity. However, the trait based improvement program in the last decades forced breeders to rely on few parents, which resulted in loss of gene diversity during 2006 to 2013. The present study indicates the need for broadening the genetic base of Indian rice varieties through the use of diverse parents in the current breeding program.
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Cushman, R A; Miles, J R; Rempel, L A; McDaneld, T G; Kuehn, L A; Chitko-McKown, C G; Nonneman, D; Echternkamp, S E
2013-06-01
A proposed functional polymorphism in the ionotropic glutamate receptor AMPA1 (GRIA1) has been reported to influence antral follicle numbers and fertility in cows. Repeat breeder cows that fail to produce a calf in multiple seasons have been reported to have reduced numbers of small (1 to 3 mm) antral follicles in their ovaries. Therefore, we tested the hypothesis that this GRIA1 polymorphism was affecting antral follicle numbers in repeat breeder cows. Repeat breeder cows (n = 64) and control cows (n = 72) that had always produced a calf were housed in a dry lot and observed twice daily for behavioral estrus. Blood samples were collected, and cows were genotyped for this GRIA1 polymorphism and for a polymorphism in the GnRH receptor (GnRHR) that was proposed to influence age at puberty. On d 3 to 8 after estrus cows were slaughtered, and reproductive organs were collected to determine antral follicle count, ovary size, and uterine horn diameter. Repeat breeder cows were older at first calving than control cows (P = 0.006). The length (P = 0.03) and height (P = 0.02) of the ovary contralateral to the corpus luteum (CL) were greater in control cows than repeat breeder cows. The endometrial diameter in the horn ipsilateral to the CL was greater in the control cows than the repeat breeder cows. Repeat breeder cows had fewer small (1 to 5 mm) antral follicles than control cows (P = 0.003); however, there was no association between GRIA1 genotype and antral follicle number. The GnRHR polymorphism was associated with age at first calving because cows that were homozygous for the C allele had a greater age at first calving than heterozygous cows or cows that were homozygous for the T allele (P = 0.01). In the granulosa cells from small (1 to 5 mm) antral follicles, mRNA abundances of 2 markers of oocyte quality, anti-Müllerian hormone and pentraxin 3, did not differ between fertility groups (P ≥ 0.12). We conclude that this GRIA1 polymorphism exists in beef cows but that it does not influence antral follicle numbers. The association between GnRHR genotype and age at first calving is likely not causal as this polymorphism is not functional. The utility of this polymorphism as a genetic marker for early conception in heifers will require further validation. Screening postpartum cows by ultrasonography to determine antral follicle numbers may aid in making culling decisions.
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Congenital Malformations in River Buffalo (Bubalus bubalis)
Albarella, Sara; Ciotola, Francesca; D’Anza, Emanuele; Coletta, Angelo; Zicarelli, Luigi; Peretti, Vincenzo
2017-01-01
Simple Summary Congenital malformations (due to genetic causes) represent a hidden danger for animal production, above all when genetic selection is undertaken for production improvements. These malformations are responsible for economic losses either because they reduce the productivity of the farm, or because their spread in the population would decrease the total productivity of that species/breed. River buffalo is a species of increasing interest all over the world for its production abilities, as proved by the buffalo genome project and the genetic selection plans that are currently performed in different countries. The aim of this review is to provide a general view of different models of congenital malformations in buffalo and their world distribution. This would be useful either for those who performed buffalo genetic selection or for researchers in genetic diseases, which would be an advantage to their studies with respect to the knowledge of gene mutations and interactions in this species. Abstract The world buffalo population is about 168 million, and it is still growing, in India, China, Brazil, and Italy. In these countries, buffalo genetic breeding programs have been performed for many decades. The occurrence of congenital malformations has caused a slowing of the genetic progress and economic loss for the breeders, due to the death of animals, or damage to their reproductive ability or failing of milk production. Moreover, they cause animal welfare reduction because they can imply foetal dystocia and because the affected animals have a reduced fitness with little chances of survival. This review depicts, in the river buffalo (Bubalus bubalis) world population, the present status of the congenital malformations, due to genetic causes, to identify their frequency and distribution in order to develop genetic breeding plans able to improve the productive and reproductive performance, and avoid the spreading of detrimental gene variants. Congenital malformations most frequently reported in literature or signaled by breeders to the Department of Veterinary Medicine and Animal Production of the University Federico II (Naples, Italy) in river buffalo are: musculoskeletal defects (transverse hemimelia, arthrogryposis, umbilical hernia) and disorders of sexual development. In conclusion this review put in evidence that river buffalo have a great variety of malformations due to genetic causes, and TH and omphalocele are the most frequent and that several cases are still not reported, leading to an underestimation of the real weight of genetic diseases in this species. PMID:28208595
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Borchert, Thomas; Krueger, Joerg; Hohe, Annette
2008-08-20
Variety protection is of high relevance for the horticultural community and juridical cases have become more frequent in a globalized economy due to essential derivation of varieties. This applies equally to Calluna vulgaris, a vegetatively propagated species from the Ericaceae family that belongs to the top-selling pot plants in Europe. We therefore analyzed the genetic diversity of 74 selected varieties and genotypes of C. vulgaris and 3 of Erica spp. by means of RAPD and iSSR fingerprinting using 168 mono- and polymorphisms. The same data set was utilized to generate a system to reliably identify Essentially Derived Varieties (EDVs) in C. vulgaris, which was adapted from a method suggested for lettuce and barley. This system was developed, validated and used for selected tests of interest in C. vulgaris. As expected following personal communications with breeders, a very small genetic diversity became evident within C. vulgaris when investigated using our molecular methods. Thus, a dendrogram-based assay to detect Essentially Derived Varieties in this species is not suitable, although varieties are propagated vegetatively. In contrast, the system applied in lettuce, which itself applies pairwise comparisons using appropriate reference sets, proved functional with this species. The narrow gene pool detected in C. vulgaris may be the genetic basis for juridical conflicts between breeders. We successfully tested a methodology for identification of Essentially Derived Varieties in highly identical C. vulgaris genotypes and recommend this for future proof of essential derivation in C. vulgaris and other vegetatively propagated crops.
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Deadly outbreak of iron storage disease (ISD) in Italian birds of the family Turdidae.
Pavone, Silvia; Salamida, Sonia; Pecorelli, Ivan; Rossi, Elisabetta; Manuali, Elisabetta
2014-09-01
A widespread deadly outbreak occurred in captive birds belonging to the family Turdidae in Italy. The present study was performed on 46 dead birds coming from 3 small decoy-bird breeders in central Italy. Only Turdus pilaris, Turdus iliacus, Turdus philomelos and Turdus merula were affected. No other species of bird held by these breeders died. A change of diet before the hunting season was reported from all breeders. Full necropsy of the animals and histological investigations of representative tissue samples were performed. Microscopical examination showed marked iron deposits in liver samples. Bacteriological investigations and molecular analysis to exclude bacterial and viral diseases were carried out. Contamination of food pellet samples by mycotoxins and analysis to detect heavy metal contaminants in food pellet samples were considered. An interesting result was the high iron content found in food pellets. It was higher than that considered suitable for birds, especially for species susceptible to development iron storage disease (ISD). Taken together, the results suggested an outbreak of ISD caused by the high iron content of food given to the birds before the hunting season. The high mortality recorded only in species belonging to the family Turdidae suggests a genetic predisposition in the affected birds.
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Optimal Design of Passive Power Filters Based on Pseudo-parallel Genetic Algorithm
NASA Astrophysics Data System (ADS)
Li, Pei; Li, Hongbo; Gao, Nannan; Niu, Lin; Guo, Liangfeng; Pei, Ying; Zhang, Yanyan; Xu, Minmin; Chen, Kerui
2017-05-01
The economic costs together with filter efficiency are taken as targets to optimize the parameter of passive filter. Furthermore, the method of combining pseudo-parallel genetic algorithm with adaptive genetic algorithm is adopted in this paper. In the early stages pseudo-parallel genetic algorithm is introduced to increase the population diversity, and adaptive genetic algorithm is used in the late stages to reduce the workload. At the same time, the migration rate of pseudo-parallel genetic algorithm is improved to change with population diversity adaptively. Simulation results show that the filter designed by the proposed method has better filtering effect with lower economic cost, and can be used in engineering.
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Rindsel: an R package for phenotypic and molecular selection indices used in plant breeding.
Perez-Elizalde, Sergío; Cerón-Rojas, Jesús J; Crossa, José; Fleury, Delphine; Alvarado, Gregorio
2014-01-01
Selection indices are estimates of the net genetic merit of the individual candidates for selection and are calculated based on phenotyping and molecular marker information collected on plants under selection in a breeding program. They reflect the breeding value of the plants and help breeders to choose the best ones for next generation. Rindsel is an R package that calculates phenotypic and molecular selection indices.
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USDA-ARS?s Scientific Manuscript database
Tuber internal quality is a major limiting factor for the U.S. potato industry. Breeders invest time and money in producing advanced selections which, in the end, often fail because of tuber internal defects, tuber bruising, or storage quality issues. In-season fertilization with calcium is known to...
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Negrín-Báez, D; Navarro, A; Lee-Montero, I; Soula, M; Afonso, J M; Zamorano, M J
2015-01-01
Morphological abnormalities in farmed gilthead seabream (Sparus aurata) are a major problem as it entails significant economic losses. In this study, 3 large scale experiments under different conditions of spawning, offspring handling and breeders phenotype were performed to analyze the inheritance of 4 types of deformities in this species: lack of operculum, lordosis, vertebral fusion, which are 3 of the most important skeletal deformities, and LSK, which is a consecutive repetition of lordosis/scoliosis/kyphosis. In Exp. [1] (mass spawning and fingerling sorting), 900 fish were analyzed at 509 d post-hatching: 846 fish that had been on-grown in a farm and 54 LSK-deformed fish that had been reared separately after being selected during the fingerling sorting process. A total of 89 families were represented. A statistically significant association between 5 of these families (from 6 breeders) and LSK-deformed fish was found. In Exp. [2] (mass spawning and no fingerling sorting), 810 fish were analyzed at 2 ages: 179 and 689 d post-hatching. Significant relationships between 2 of the breeders and 2 of the families with the lack of operculum prevalence of their descendants were found at 689 d but not at 179 d. Heritabilities: 0.09 ± 0.09 at 179 d and 0.17 ± 0.08 at 689 d. Column deformities prevalence was low and no association with family was observed. Family relationships were determined by microsatellites multiplex PCR in both experiments. In Exp. [3] (designed mating), sires suffering from lordosis or lack of operculum or vertebral fusion deformities were mated with non-deformed dams and a mass-spawning mating was considered as a control. After analyzing 11,503 offspring at 159 d post-hatching, a significant relationship between each deformity prevalence and the mating of breeders suffering from the same deformity was observed. In addition, a significant prevalence of lack of operculum in offspring from lordotic matings was observed. Heritabilities ranged from 0.34 to 0.46 for the 3 deformities. The results of the present study suggest that these deformities have a genetic origin. They also suggest that the sorting process is not recommended and that producers should consider these deformities in genetic breeding programs to significantly improve their fish morphological quality and to minimize farmed fish deformities incidence.
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NASA Technical Reports Server (NTRS)
Rogers, David
1991-01-01
G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.
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Howard, David M; Pong-Wong, Ricardo; Knap, Pieter W; Kremer, Valentin D; Woolliams, John A
2018-05-10
Optimal contributions selection (OCS) provides animal breeders with a framework for maximising genetic gain for a predefined rate of inbreeding. Simulation studies have indicated that the source of the selective advantage of OCS is derived from breeding decisions being more closely aligned with estimates of Mendelian sampling terms ([Formula: see text]) of selection candidates, rather than estimated breeding values (EBV). This study represents the first attempt to assess the source of the selective advantage provided by OCS using a commercial pig population and by testing three hypotheses: (1) OCS places more emphasis on [Formula: see text] compared to EBV for determining which animals were selected as parents, (2) OCS places more emphasis on [Formula: see text] compared to EBV for determining which of those parents were selected to make a long-term genetic contribution (r), and (3) OCS places more emphasis on [Formula: see text] compared to EBV for determining the magnitude of r. The population studied also provided an opportunity to investigate the convergence of r over time. Selection intensity limited the number of males available for analysis, but females provided some evidence that the selective advantage derived from applying an OCS algorithm resulted from greater weighting being placed on [Formula: see text] during the process of decision-making. Male r were found to converge initially at a faster rate than female r, with approximately 90% convergence achieved within seven generations across both sexes. This study of commercial data provides some support to results from theoretical and simulation studies that the source of selective advantage from OCS comes from [Formula: see text]. The implication that genomic selection (GS) improves estimation of [Formula: see text] should allow for even greater genetic gains for a predefined rate of inbreeding, once the synergistic benefits of combining OCS and GS are realised.
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Comparison of genetic algorithms with conjugate gradient methods
NASA Technical Reports Server (NTRS)
Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.
1972-01-01
Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.
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Software For Genetic Algorithms
NASA Technical Reports Server (NTRS)
Wang, Lui; Bayer, Steve E.
1992-01-01
SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.
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New knowledge-based genetic algorithm for excavator boom structural optimization
NASA Astrophysics Data System (ADS)
Hua, Haiyan; Lin, Shuwen
2014-03-01
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.
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Current status and biotechnological advances in genetic engineering of ornamental plants.
Azadi, Pejman; Bagheri, Hedayat; Nalousi, Ayoub Molaahmad; Nazari, Farzad; Chandler, Stephen F
2016-11-01
Cut flower markets are developing in many countries as the international demand for cut flowers is rapidly growing. Developing new varieties with modified characteristics is an important aim in floriculture. Production of transgenic ornamental plants can shorten the time required in the conventional breeding of a cultivar. Biotechnology tools in combination with conventional breeding methods have been used by cut flower breeders to change flower color, plant architecture, post-harvest traits, and disease resistance. In this review, we describe advances in genetic engineering that have led to the development of new cut flower varieties. Copyright © 2016 Elsevier Inc. All rights reserved.
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Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping
NASA Astrophysics Data System (ADS)
Balakrishnan, D.; Quan, C.; Tay, C. J.
2013-06-01
The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.
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Mobile robot dynamic path planning based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Wang, Yong; Zhou, Heng; Wang, Ying
2017-08-01
In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.
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An Efficient Rank Based Approach for Closest String and Closest Substring
2012-01-01
This paper aims to present a new genetic approach that uses rank distance for solving two known NP-hard problems, and to compare rank distance with other distance measures for strings. The two NP-hard problems we are trying to solve are closest string and closest substring. For each problem we build a genetic algorithm and we describe the genetic operations involved. Both genetic algorithms use a fitness function based on rank distance. We compare our algorithms with other genetic algorithms that use different distance measures, such as Hamming distance or Levenshtein distance, on real DNA sequences. Our experiments show that the genetic algorithms based on rank distance have the best results. PMID:22675483
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A hybrid genetic algorithm for resolving closely spaced objects
NASA Technical Reports Server (NTRS)
Abbott, R. J.; Lillo, W. E.; Schulenburg, N.
1995-01-01
A hybrid genetic algorithm is described for performing the difficult optimization task of resolving closely spaced objects appearing in space based and ground based surveillance data. This application of genetic algorithms is unusual in that it uses a powerful domain-specific operation as a genetic operator. Results of applying the algorithm to real data from telescopic observations of a star field are presented.
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Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
NASA Technical Reports Server (NTRS)
Burchett, Bradley T.
2003-01-01
The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.
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Genetic Improvement of Switchgrass and Other Herbaceous Plants for Use as Biomass Fuel Feedstock
DOE Office of Scientific and Technical Information (OSTI.GOV)
Vogel, K.P.
2001-01-11
It should be highly feasible to genetically modify the feedstock quality of switchgrass and other herbaceous plants using both conventional and molecular breeding techniques. Effectiveness of breeding to modify herbages of switchgrass and other perennial and annual herbaceous species has already been demonstrated. The use of molecular markers and transformation technology will greatly enhance the capability of breeders to modify the plant structure and cell walls of herbaceous plants. It will be necessary to monitor gene flow to remnant wild populations of plants and have strategies available to curtail gene flow if it becomes a potential problem. It also willmore » be necessary to monitor plant survival and long-term productivity as affected by genetic changes that improve forage quality. Information on the conversion processes that will be used and the biomass characteristics that affect conversion efficiency and rate is absolutely essential as well as information on the relative economic value of specific traits. Because most forage or biomass quality characteristics are highly affected by plant maturity, it is suggested that plant material of specific maturity stages be used in research to determining desirable feedstock quality characteristics. Plant material could be collected at various stages of development from an array of environments and storage conditions that could be used in conversion research. The same plant material could be used to develop NIRS calibrations that could be used by breeders in their selection programs and also to develop criteria for a feedstock quality assessment program. Breeding for improved feedstock quality will likely affect the rate of improvement of biomass production per acre. If the same level of resources are used, multi-trait breeding simply reduces the selection pressure and hence the breeding progress that can be made for a single trait unless all the traits are highly correlated. Since desirable feedstock traits are likely to be similar to IVDMD, it is likely that they will not be highly positively correlated with yield. Hence to achieve target yields and improve specific quality traits, it will likely be necessary to increase the resources available to plant breeders. Marker assisted selection will be extremely useful in breeding for quality traits, particularly for traits that can be affected by modifying a few genes. Genetic markers are going to be needed for monitoring gene flow to wild populations. Transformation will be a very useful tool for determining the affects of specific genes on biomass feedstock quality.« less
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Ashraf, Kamran; Ahmad, Altaf; Chaudhary, Anis; Mujeeb, Mohd; Ahmad, Sayeed; Amir, Mohd; Mallick, N
2014-04-01
The present investigation was undertaken for the assessment of 12 accessions of Zingiber officinale Rosc. collected from subcontinent of India by RAPD markers. DNA was isolated using CTAB method. Thirteen out of twenty primers screened were informative and produced 275 amplification products, among which 261 products (94.90%) were found to be polymorphic. The percentage polymorphism of all 12 accessions ranged from 88.23% to 100%. Most of the RAPD markers studied showed different levels of genetic polymorphism. The data of 275 RAPD bands were used to generate Jaccard's similarity coefficients and to construct a dendrogram by means of UPGMA. Results showed that ginger undergoes genetic variation due to a wide range of ecological conditions. This investigation was an understanding of genetic variation within the accessions. It will also provide an important input into determining resourceful management strategies and help to breeders for ginger improvement program.
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Ashraf, Kamran; Ahmad, Altaf; Chaudhary, Anis; Mujeeb, Mohd.; Ahmad, Sayeed; Amir, Mohd.; Mallick, N.
2013-01-01
The present investigation was undertaken for the assessment of 12 accessions of Zingiber officinale Rosc. collected from subcontinent of India by RAPD markers. DNA was isolated using CTAB method. Thirteen out of twenty primers screened were informative and produced 275 amplification products, among which 261 products (94.90%) were found to be polymorphic. The percentage polymorphism of all 12 accessions ranged from 88.23% to 100%. Most of the RAPD markers studied showed different levels of genetic polymorphism. The data of 275 RAPD bands were used to generate Jaccard’s similarity coefficients and to construct a dendrogram by means of UPGMA. Results showed that ginger undergoes genetic variation due to a wide range of ecological conditions. This investigation was an understanding of genetic variation within the accessions. It will also provide an important input into determining resourceful management strategies and help to breeders for ginger improvement program. PMID:24600309
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Genomics of crop wild relatives: expanding the gene pool for crop improvement.
Brozynska, Marta; Furtado, Agnelo; Henry, Robert J
2016-04-01
Plant breeders require access to new genetic diversity to satisfy the demands of a growing human population for more food that can be produced in a variable or changing climate and to deliver the high-quality food with nutritional and health benefits demanded by consumers. The close relatives of domesticated plants, crop wild relatives (CWRs), represent a practical gene pool for use by plant breeders. Genomics of CWR generates data that support the use of CWR to expand the genetic diversity of crop plants. Advances in DNA sequencing technology are enabling the efficient sequencing of CWR and their increased use in crop improvement. As the sequencing of genomes of major crop species is completed, attention has shifted to analysis of the wider gene pool of major crops including CWR. A combination of de novo sequencing and resequencing is required to efficiently explore useful genetic variation in CWR. Analysis of the nuclear genome, transcriptome and maternal (chloroplast and mitochondrial) genome of CWR is facilitating their use in crop improvement. Genome analysis results in discovery of useful alleles in CWR and identification of regions of the genome in which diversity has been lost in domestication bottlenecks. Targeting of high priority CWR for sequencing will maximize the contribution of genome sequencing of CWR. Coordination of global efforts to apply genomics has the potential to accelerate access to and conservation of the biodiversity essential to the sustainability of agriculture and food production. © 2015 Society for Experimental Biology, Association of Applied Biologists and John Wiley & Sons Ltd.
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Fernández, Jesús; Toro, Miguel Á; Sonesson, Anna K; Villanueva, Beatriz
2014-01-01
The success of an aquaculture breeding program critically depends on the way in which the base population of breeders is constructed since all the genetic variability for the traits included originally in the breeding goal as well as those to be included in the future is contained in the initial founders. Traditionally, base populations were created from a number of wild strains by sampling equal numbers from each strain. However, for some aquaculture species improved strains are already available and, therefore, mean phenotypic values for economically important traits can be used as a criterion to optimize the sampling when creating base populations. Also, the increasing availability of genome-wide genotype information in aquaculture species could help to refine the estimation of relationships within and between candidate strains and, thus, to optimize the percentage of individuals to be sampled from each strain. This study explores the advantages of using phenotypic and genome-wide information when constructing base populations for aquaculture breeding programs in terms of initial and subsequent trait performance and genetic diversity level. Results show that a compromise solution between diversity and performance can be found when creating base populations. Up to 6% higher levels of phenotypic performance can be achieved at the same level of global diversity in the base population by optimizing the selection of breeders instead of sampling equal numbers from each strain. The higher performance observed in the base population persisted during 10 generations of phenotypic selection applied in the subsequent breeding program.
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Learning Intelligent Genetic Algorithms Using Japanese Nonograms
ERIC Educational Resources Information Center
Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen
2012-01-01
An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…
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Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.
Yang, Shengxiang
2008-01-01
In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.
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Boiler-turbine control system design using a genetic algorithm
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dimeo, R.; Lee, K.Y.
1995-12-01
This paper discusses the application of a genetic algorithm to control system design for a boiler-turbine plant. In particular the authors study the ability of the genetic algorithm to develop a proportional-integral (PI) controller and a state feedback controller for a non-linear multi-input/multi-output (MIMO) plant model. The plant model is presented along with a discussion of the inherent difficulties in such controller development. A sketch of the genetic algorithm (GA) is presented and its strategy as a method of control system design is discussed. Results are presented for two different control systems that have been designed with the genetic algorithm.
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Method for hyperspectral imagery exploitation and pixel spectral unmixing
NASA Technical Reports Server (NTRS)
Lin, Ching-Fang (Inventor)
2003-01-01
An efficiently hybrid approach to exploit hyperspectral imagery and unmix spectral pixels. This hybrid approach uses a genetic algorithm to solve the abundance vector for the first pixel of a hyperspectral image cube. This abundance vector is used as initial state in a robust filter to derive the abundance estimate for the next pixel. By using Kalman filter, the abundance estimate for a pixel can be obtained in one iteration procedure which is much fast than genetic algorithm. The output of the robust filter is fed to genetic algorithm again to derive accurate abundance estimate for the current pixel. The using of robust filter solution as starting point of the genetic algorithm speeds up the evolution of the genetic algorithm. After obtaining the accurate abundance estimate, the procedure goes to next pixel, and uses the output of genetic algorithm as the previous state estimate to derive abundance estimate for this pixel using robust filter. And again use the genetic algorithm to derive accurate abundance estimate efficiently based on the robust filter solution. This iteration continues until pixels in a hyperspectral image cube end.
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Zurfluh, Katrin; Wang, Juan; Klumpp, Jochen; Nüesch-Inderbinen, Magdalena; Fanning, Séamus; Stephan, Roger
2014-01-01
Objectives: The purpose of this study was to characterize sets of extended-spectrum β-lactamases (ESBL)-producing Enterobacteriaceae collected longitudinally from different flocks of broiler breeders, meconium of 1-day-old broilers from theses breeder flocks, as well as from these broiler flocks before slaughter. Methods: Five sets of ESBL-producing Escherichia coli were studied by multi-locus sequence typing (MLST), phylogenetic grouping, PCR-based replicon typing and resistance profiling. The blaCTX-M-1-harboring plasmids of one set (pHV295.1, pHV114.1, and pHV292.1) were fully sequenced and subjected to comparative analysis. Results: Eleven different MLST sequence types (ST) were identified with ST1056 the predominant one, isolated in all five sets either on the broiler breeder or meconium level. Plasmid sequencing revealed that blaCTX-M-1 was carried by highly similar IncI1/ST3 plasmids that were 105 076 bp, 110 997 bp, and 117 269 bp in size, respectively. Conclusions: The fact that genetically similar IncI1/ST3 plasmids were found in ESBL-producing E. coli of different MLST types isolated at the different levels in the broiler production pyramid provides strong evidence for a vertical transmission of these plasmids from a common source (nucleus poultry flocks). PMID:25324838
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Zurfluh, Katrin; Wang, Juan; Klumpp, Jochen; Nüesch-Inderbinen, Magdalena; Fanning, Séamus; Stephan, Roger
2014-01-01
The purpose of this study was to characterize sets of extended-spectrum β-lactamases (ESBL)-producing Enterobacteriaceae collected longitudinally from different flocks of broiler breeders, meconium of 1-day-old broilers from theses breeder flocks, as well as from these broiler flocks before slaughter. Five sets of ESBL-producing Escherichia coli were studied by multi-locus sequence typing (MLST), phylogenetic grouping, PCR-based replicon typing and resistance profiling. The bla CTX-M-1-harboring plasmids of one set (pHV295.1, pHV114.1, and pHV292.1) were fully sequenced and subjected to comparative analysis. Eleven different MLST sequence types (ST) were identified with ST1056 the predominant one, isolated in all five sets either on the broiler breeder or meconium level. Plasmid sequencing revealed that bla CTX-M-1 was carried by highly similar IncI1/ST3 plasmids that were 105 076 bp, 110 997 bp, and 117 269 bp in size, respectively. The fact that genetically similar IncI1/ST3 plasmids were found in ESBL-producing E. coli of different MLST types isolated at the different levels in the broiler production pyramid provides strong evidence for a vertical transmission of these plasmids from a common source (nucleus poultry flocks).
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Pre-breeding for diversification of primary gene pool and genetic enhancement of grain legumes
Sharma, Shivali; Upadhyaya, H. D.; Varshney, R. K.; Gowda, C. L. L.
2013-01-01
The narrow genetic base of cultivars coupled with low utilization of genetic resources are the major factors limiting grain legume production and productivity globally. Exploitation of new and diverse sources of variation is needed for the genetic enhancement of grain legumes. Wild relatives with enhanced levels of resistance/tolerance to multiple stresses provide important sources of genetic diversity for crop improvement. However, their exploitation for cultivar improvement is limited by cross-incompatibility barriers and linkage drags. Pre-breeding provides a unique opportunity, through the introgression of desirable genes from wild germplasm into genetic backgrounds readily used by the breeders with minimum linkage drag, to overcome this. Pre-breeding activities using promising landraces, wild relatives, and popular cultivars have been initiated at International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) to develop new gene pools in chickpea, pigeonpea, and groundnut with a high frequency of useful genes, wider adaptability, and a broad genetic base. The availability of molecular markers will greatly assist in reducing linkage drags and increasing the efficiency of introgression in pre-breeding programs. PMID:23970889
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Genetics-based control of a mimo boiler-turbine plant
DOE Office of Scientific and Technical Information (OSTI.GOV)
Dimeo, R.M.; Lee, K.Y.
1994-12-31
A genetic algorithm is used to develop an optimal controller for a non-linear, multi-input/multi-output boiler-turbine plant. The algorithm is used to train a control system for the plant over a wide operating range in an effort to obtain better performance. The results of the genetic algorithm`s controller designed from the linearized plant model at a nominal operating point. Because the genetic algorithm is well-suited to solving traditionally difficult optimization problems it is found that the algorithm is capable of developing the controller based on input/output information only. This controller achieves a performance comparable to the standard linear quadratic regulator.
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Improved classification accuracy by feature extraction using genetic algorithms
NASA Astrophysics Data System (ADS)
Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.
2003-05-01
A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.
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Avise, John C; Jones, Adam G; Walker, DeEtte; DeWoody, J Andrew
2002-01-01
Fish species have diverse breeding behaviors that make them valuable for testing theories on genetic mating systems and reproductive tactics. Here we review genetic appraisals of paternity and maternity in wild fish populations. Behavioral phenomena quantified by genetic markers in various species include patterns of multiple mating by both sexes; frequent cuckoldry by males and rare cuckoldry by females in nest-tending species; additional routes to surrogate parentage via nest piracy and egg-thievery; egg mimicry by nest-tending males; brood parasitism by helper males in cooperative breeders; clutch mixing in oral brooders; kinship in schooling fry of broadcast spawners; sperm storage by dams in female-pregnant species; and sex-role reversal, polyandry, and strong sexual selection on females in some male-pregnant species. Additional phenomena addressed by genetic parentage analyses in fishes include clustered mutations, filial cannibalism, and local population size. All results are discussed in the context of relevant behavioral and evolutionary theory.
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NASA Astrophysics Data System (ADS)
Moon, Byung-Young
2005-12-01
The hybrid neural-genetic multi-model parameter estimation algorithm was demonstrated. This method can be applied to structured system identification of electro-hydraulic servo system. This algorithms consist of a recurrent incremental credit assignment(ICRA) neural network and a genetic algorithm. The ICRA neural network evaluates each member of a generation of model and genetic algorithm produces new generation of model. To evaluate the proposed method, electro-hydraulic servo system was designed and manufactured. The experiment was carried out to figure out the hybrid neural-genetic multi-model parameter estimation algorithm. As a result, the dynamic characteristics were obtained such as the parameters(mass, damping coefficient, bulk modulus, spring coefficient), which minimize total square error. The result of this study can be applied to hydraulic systems in industrial fields.
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Chang, Melanie L.; Yokoyama, Jennifer S.; Branson, Nick; Dyer, Donna J.; Hitte, Christophe; Overall, Karen L.
2009-01-01
Until recently, canine genetic research has not focused on population structure within breeds, which may confound the results of case–control studies by introducing spurious correlations between phenotype and genotype that reflect population history. Intrabreed structure may exist when geographical origin or divergent selection regimes influence the choices of potential mates for breeding dogs. We present evidence for intrabreed stratification from a genome-wide marker survey in a sample of unrelated dogs. We genotyped 76 Border Collies, 49 Australian Shepherds, 17 German Shepherd Dogs, and 17 Portuguese Water Dogs for our primary analyses using Affymetrix Canine v2.0 single-nucleotide polymorphism (SNP) arrays. Subsets of autosomal markers were examined using clustering algorithms to facilitate assignment of individuals to populations and estimation of the number of populations represented in the sample. SNPs passing stringent quality control filters were employed for explicitly phylogenetic analyses reconstructing relationships between individuals using maximum parsimony and Bayesian methods. We used simulation studies to explore the possible effects of intrabreed stratification on genome-wide association studies. These analyses demonstrate significant stratification in at least one of our primary breeds of interest, the Border Collie. Demographic and pedigree data suggest that this population substructure may result from geographic isolation or divergent selection regimes practiced by breeders with different breeding program goals. Simulation studies indicate that such stratification could result in false discovery rates significant enough to confound genome-wide association analyses. Intrabreed stratification should be accounted for when designing and interpreting the results of case–control association studies using purebred dogs.
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Comparison of genetic algorithm methods for fuel management optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
DeChaine, M.D.; Feltus, M.A.
1995-12-31
The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.
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Training product unit neural networks with genetic algorithms
NASA Technical Reports Server (NTRS)
Janson, D. J.; Frenzel, J. F.; Thelen, D. C.
1991-01-01
The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.
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New Results in Astrodynamics Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Coverstone-Carroll, V.; Hartmann, J. W.; Williams, S. N.; Mason, W. J.
1998-01-01
Generic algorithms have gained popularity as an effective procedure for obtaining solutions to traditionally difficult space mission optimization problems. In this paper, a brief survey of the use of genetic algorithms to solve astrodynamics problems is presented and is followed by new results obtained from applying a Pareto genetic algorithm to the optimization of low-thrust interplanetary spacecraft missions.
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Evidence that pairing with genetically similar mates is maladaptive in a monogamous bird
Mulard, Hervé; Danchin, E.; Talbot, S.L.; Ramey, A.M.; Hatch, Shyla A.; White, J.F.; Helfenstein, F.; Wagner, R.H.
2009-01-01
Background. Evidence of multiple genetic criteria of mate choice is accumulating in numerous taxa. In many species, females have been shown to pair with genetically dissimilar mates or with extra-pair partners that are more genetically compatible than their social mates, thereby increasing their offsprings' heterozygosity which often correlates with offspring fitness. While most studies have focused on genetically promiscuous species, few studies have addressed genetically monogamous species, in which mate choice tends to be mutual. Results. Here, we used microsatellite markers to assess individual global heterozygosity and genetic similarity of pairs in a socially and genetically monogamous seabird, the black-legged kittiwake Rissa tridactyla. We found that pairs were more genetically dissimilar than expected by chance. We also identified fitness costs of breeding with genetically similar partners: (i) genetic similarity of pairs was negatively correlated with the number of chicks hatched, and (ii) offspring heterozygosity was positively correlated with growth rate and survival. Conclusion. These findings provide evidence that breeders in a genetically monogamous species may avoid the fitness costs of reproducing with a genetically similar mate. In such species that lack the opportunity to obtain extra-pair fertilizations, mate choice may therefore be under high selective pressure. ?? 2009 Mulard et al; licensee BioMed Central Ltd.
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Fast Breeder Reactors in Sweden: Vision and Reality.
Fjaestad, Maja
2015-01-01
The fast breeder is a type of nuclear reactor that aroused much attention in the 1950s and '60s. Its ability to produce more nuclear fuel than it consumes offered promises of cheap and reliable energy. Sweden had advanced plans for a nuclear breeder program, but canceled them in the middle of the 1970s with the rise of nuclear skepticism. The article investigates the nuclear breeder as a technological vision. The nuclear breeder reactor is an example of a technological future that did not meet its industrial expectations. But that does not change the fact that the breeder was an influential technology. Decisions about the contemporary reactors were taken with the idea that in a foreseeable future they would be replaced with the efficient breeder. The article argues that general themes in the history of the breeder reactor can deepen our understanding of the mechanisms behind technological change.
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Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm
Chen, C.; Xia, J.; Liu, J.; Feng, G.
2006-01-01
Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.
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Assessment of exchange of crop in view of change climate and International Treaties.
Singh, Anil Kumar; Pedapati, Annitaa; Manibhushan
2015-01-01
To meet the UN millennium development goal of reducing the number of hungry people to half by 2015, there is utmost need to breed potentially high yielding varieties to match up the requirement along with corrective measures to bridge the gap between the potential yield and yield harvested by farmers. The scenario has changed from free access to limited access of plant genetic resources (PGR) and therefore, it is important to understand the issues in view of national and international agreements, intellectual property rights (IPR'S), climate change conditions and expanded scope of breeders and farmers rights for developed genotypes. For efficient management of PGR, developing countries need to understand the implications of PGR related IPR'S as stronger IPR'S in developed countries could have harmful effects by reduced exchange of genetic resources from developed countries. Keeping in view the existing realities every possible effort should be taken for enrichment of crop gene pool by introducing them from each and every corner of the globe. Keeping these facts in view this paper describes the priorities for introduction and exchange of important crop groups/crops along with some of their potential wild and weedy relatives and thrust has been given to generate awareness among the workers engaged in the breeders/crop improvement works. Information provided in this presentation can be utilized by prospective crop improvement works to plan to meet out the nationalfood security.
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Design and Management of Field Trials of Transgenic Cereals
NASA Astrophysics Data System (ADS)
Bedő, Zoltán; Rakszegi, Mariann; Láng, László
The development of gene transformation systems has allowed the introgression of alien genes into plant genomes, thus providing a mechanism for broadening the genetic resources available to plant breeders. The design and the management of field trials vary according to the purpose for which transgenic cereals are developed. Breeders study the phenotypic and genotypic stability of transgenic plants, monitor the increase in homozygosity of transgenic genotypes under field conditions, and develop backcross generations to transfer the introduced genes into secondary transgenic cereal genotypes. For practical purposes, they may also multiply seed of the transgenic lines to produce sufficient amounts of grain for the detailed analysis of trait(s) of interest, to determine the field performance of transgenic lines, and to compare them with the non-transformed parental genotypes. Prior to variety registration, the Distinctness, Uniformity and Stability (DUS) tests and Value for Cultivation and Use (VCU) experiments are carried out in field trials. Field testing includes specific requirements for transgenic cereals to assess potential environmental risks. The capacity of the pollen to survive, establish and disseminate in the field test environment, the potential for gene transfer, the effects of products expressed by the introduced sequences and phenotypic and genotypic instability that might cause deleterious effects must all be specifically monitored, as required by EU Directives 2003/701/EC (1) on the release of genetically modified higher plants in the environment.
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Chevassus, Bernard; Quillet, Edwige; Krieg, Francine; Hollebecq, Marie-Gwénola; Mambrini, Muriel; Fauré, André; Labbé, Laurent; Hiseux, Jean-Pierre; Vandeputte, Marc
2004-01-01
Growth rate is the main breeding goal of fish breeders, but individual selection has often shown poor responses in fish species. The PROSPER method was developed to overcome possible factors that may contribute to this low success, using (1) a variable base population and high number of breeders (Ne > 100), (2) selection within groups with low non-genetic effects and (3) repeated growth challenges. Using calculations, we show that individual selection within groups, with appropriate management of maternal effects, can be superior to mass selection as soon as the maternal effect ratio exceeds 0.15, when heritability is 0.25. Practically, brown trout were selected on length at the age of one year with the PROSPER method. The genetic gain was evaluated against an unselected control line. After four generations, the mean response per generation in length at one year was 6.2% of the control mean, while the mean correlated response in weight was 21.5% of the control mean per generation. At the 4th generation, selected fish also appeared to be leaner than control fish when compared at the same size, and the response on weight was maximal (≈130% of the control mean) between 386 and 470 days post fertilisation. This high response is promising, however, the key points of the method have to be investigated in more detail. PMID:15496285
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NUCLEAR REACTOR FUEL-BREEDER FUEL ELEMENT
Currier, E.L. Jr.; Nicklas, J.H.
1962-08-14
A fuel-breeder fuel element was developed for a nuclear reactor wherein discrete particles of fissionable material are dispersed in a matrix of fertile breeder material. The fuel element combines the advantages of a dispersion type and a breeder-type. (AEC)
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2016-12-01
Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street Concord, NH 03301 under contract W911SR...Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm prepared by Justin L Paul Academy of Applied Science 24 Warren Street... Genetic Algorithm 5a. CONTRACT NUMBER W199SR-15-2-001 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Justin L Paul 5d. PROJECT
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Huang, Xiaobiao; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
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Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)
NASA Astrophysics Data System (ADS)
Li, X. R.; Wang, X.
2016-03-01
When using the genetic algorithm to solve the problem of too-short-arc (TSA) determination, due to the difference of computing processes between the genetic algorithm and classical method, the methods for outliers editing are no longer applicable. In the genetic algorithm, the robust estimation is acquired by means of using different loss functions in the fitness function, then the outlier problem of TSAs is solved. Compared with the classical method, the application of loss functions in the genetic algorithm is greatly simplified. Through the comparison of results of different loss functions, it is clear that the methods of least median square and least trimmed square can greatly improve the robustness of TSAs, and have a high breakdown point.
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Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.
Zhang, Jie; Kang, Man; Li, Xiaojuan; Liu, Geng-Yang
2017-01-01
Genetic algorithms are widely adopted to solve optimization problems in robotic applications. In such safety-critical systems, it is vitally important to formally prove the correctness when genetic algorithms are applied. This paper focuses on formal modeling of crossover operations that are one of most important operations in genetic algorithms. Specially, we for the first time formalize crossover operations with higher-order logic based on HOL4 that is easy to be deployed with its user-friendly programing environment. With correctness-guaranteed formalized crossover operations, we can safely apply them in robotic applications. We implement our technique to solve a path planning problem using a genetic algorithm with our formalized crossover operations, and the results show the effectiveness of our technique.
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Defining a genetic ideotype for crop improvement.
Trethowan, Richard M
2014-01-01
While plant breeders traditionally base selection on phenotype, the development of genetic ideotypes can help focus the selection process. This chapter provides a road map for the establishment of a refined genetic ideotype. The first step is an accurate definition of the target environment including the underlying constraints, their probability of occurrence, and impact on phenotype. Once the environmental constraints are established, the wealth of information on plant physiological responses to stresses, known gene information, and knowledge of genotype ×environment and gene × environment interaction help refine the target ideotype and form a basis for cross prediction.Once a genetic ideotype is defined the challenge remains to build the ideotype in a plant breeding program. A number of strategies including marker-assisted recurrent selection and genomic selection can be used that also provide valuable information for the optimization of genetic ideotype. However, the informatics required to underpin the realization of the genetic ideotype then becomes crucial. The reduced cost of genotyping and the need to combine pedigree, phenotypic, and genetic data in a structured way for analysis and interpretation often become the rate-limiting steps, thus reducing genetic gain. Systems for managing these data and an example of ideotype construction for a defined environment type are discussed.
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NASA Astrophysics Data System (ADS)
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.
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Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.
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A Test of Genetic Algorithms in Relevance Feedback.
ERIC Educational Resources Information Center
Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de
2002-01-01
Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…
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Transonic Wing Shape Optimization Using a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)
2002-01-01
A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.
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Portfolio optimization by using linear programing models based on genetic algorithm
NASA Astrophysics Data System (ADS)
Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.
2018-01-01
In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.
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An improved genetic algorithm and its application in the TSP problem
NASA Astrophysics Data System (ADS)
Li, Zheng; Qin, Jinlei
2011-12-01
Concept and research actuality of genetic algorithm are introduced in detail in the paper. Under this condition, the simple genetic algorithm and an improved algorithm are described and applied in an example of TSP problem, where the advantage of genetic algorithm is adequately shown in solving the NP-hard problem. In addition, based on partial matching crossover operator, the crossover operator method is improved into extended crossover operator in order to advance the efficiency when solving the TSP. In the extended crossover method, crossover operator can be performed between random positions of two random individuals, which will not be restricted by the position of chromosome. Finally, the nine-city TSP is solved using the improved genetic algorithm with extended crossover method, the efficiency of whose solution process is much higher, besides, the solving speed of the optimal solution is much faster.
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Solving TSP problem with improved genetic algorithm
NASA Astrophysics Data System (ADS)
Fu, Chunhua; Zhang, Lijun; Wang, Xiaojing; Qiao, Liying
2018-05-01
The TSP is a typical NP problem. The optimization of vehicle routing problem (VRP) and city pipeline optimization can use TSP to solve; therefore it is very important to the optimization for solving TSP problem. The genetic algorithm (GA) is one of ideal methods in solving it. The standard genetic algorithm has some limitations. Improving the selection operator of genetic algorithm, and importing elite retention strategy can ensure the select operation of quality, In mutation operation, using the adaptive algorithm selection can improve the quality of search results and variation, after the chromosome evolved one-way evolution reverse operation is added which can make the offspring inherit gene of parental quality improvement opportunities, and improve the ability of searching the optimal solution algorithm.
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Exploiting Wild Relatives for Genomics-assisted Breeding of Perennial Crops
Migicovsky, Zoë; Myles, Sean
2017-01-01
Perennial crops are vital contributors to global food production and nutrition. However, the breeding of new perennial crops is an expensive and time-consuming process due to the large size and lengthy juvenile phase of many species. Genomics provides a valuable tool for improving the efficiency of breeding by allowing progeny possessing a trait of interest to be selected at the seed or seedling stage through marker-assisted selection (MAS). The benefits of MAS to a breeder are greatest when the targeted species takes a long time to reach maturity and is expensive to grow and maintain. Thus, MAS holds particular promise in perennials since they are often costly and time-consuming to grow to maturity and evaluate. Well-characterized germplasm that breeders can tap into for improving perennials is often limited in genetic diversity. Wild relatives are a largely untapped source of desirable traits including disease resistance, fruit quality, and rootstock characteristics. This review focuses on the use of genomics-assisted breeding in perennials, especially as it relates to the introgression of useful traits from wild relatives. The identification of genetic markers predictive of beneficial phenotypes derived from wild relatives is hampered by genomic tools designed for domesticated species that are often ill-suited for use in wild relatives. There is therefore an urgent need for better genomic resources from wild relatives. A further barrier to exploiting wild diversity through genomics is the phenotyping bottleneck: well-powered genetic mapping requires accurate and cost-effective characterization of large collections of diverse wild germplasm. While genomics will always be used in combination with traditional breeding methods, it is a powerful tool for accelerating the speed and reducing the costs of breeding while harvesting the potential of wild relatives for improving perennial crops. PMID:28421095
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Exploiting Wild Relatives for Genomics-assisted Breeding of Perennial Crops.
Migicovsky, Zoë; Myles, Sean
2017-01-01
Perennial crops are vital contributors to global food production and nutrition. However, the breeding of new perennial crops is an expensive and time-consuming process due to the large size and lengthy juvenile phase of many species. Genomics provides a valuable tool for improving the efficiency of breeding by allowing progeny possessing a trait of interest to be selected at the seed or seedling stage through marker-assisted selection (MAS). The benefits of MAS to a breeder are greatest when the targeted species takes a long time to reach maturity and is expensive to grow and maintain. Thus, MAS holds particular promise in perennials since they are often costly and time-consuming to grow to maturity and evaluate. Well-characterized germplasm that breeders can tap into for improving perennials is often limited in genetic diversity. Wild relatives are a largely untapped source of desirable traits including disease resistance, fruit quality, and rootstock characteristics. This review focuses on the use of genomics-assisted breeding in perennials, especially as it relates to the introgression of useful traits from wild relatives. The identification of genetic markers predictive of beneficial phenotypes derived from wild relatives is hampered by genomic tools designed for domesticated species that are often ill-suited for use in wild relatives. There is therefore an urgent need for better genomic resources from wild relatives. A further barrier to exploiting wild diversity through genomics is the phenotyping bottleneck: well-powered genetic mapping requires accurate and cost-effective characterization of large collections of diverse wild germplasm. While genomics will always be used in combination with traditional breeding methods, it is a powerful tool for accelerating the speed and reducing the costs of breeding while harvesting the potential of wild relatives for improving perennial crops.
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Recombination and genetic variance among maize doubled haploids induced from F1 and F2 plants.
Sleper, Joshua A; Bernardo, Rex
2016-12-01
Inducing maize doubled haploids from F 2 plants (DHF2) instead of F 1 plants (DHF1) led to more recombination events. However, the best DHF2 lines did not outperform the best DHF1 lines. Maize (Zea mays L.) breeders rely on doubled haploid (DH) technology for fast and efficient production of inbreds. Breeders can induce DH lines most quickly from F 1 plants (DHF1), or induce DH lines from F 2 plants (DHF2) to allow selection prior to DH induction and have more recombinations. Our objective was to determine if the additional recombinations in maize DHF2 lines lead to a larger genetic variance and a superior mean of the best lines. A total of 311 DHF1 and 241 DHF2 lines, derived from the same biparental cross, were crossed to two testers and evaluated in multilocation trials in Europe and the US. The mean number of recombinations per genome was 14.48 among the DHF1 lines and 21.38 among the DHF1 lines. The means of the DHF1 and DHF2 lines did not differ for yield, moisture, and plant height. The genetic variance was higher among DHF2 lines than among DHF1 lines for moisture, but not for yield and plant height. The ratio of repulsion to coupling linkages, which was estimated from genomewide marker effects, was higher among DHF1 lines than among DHF2 lines for moisture, but not for yield and plant height. The higher genetic variance for moisture among DHF2 lines did not lead to lower moisture of the best 10 % of the lines. Our results indicated that the decision of inducing DH lines from F 1 or F 2 plants needs to be made from considerations other than the performance of the resulting DHF1 or DHF2 lines.
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Genetic algorithm based fuzzy control of spacecraft autonomous rendezvous
NASA Technical Reports Server (NTRS)
Karr, C. L.; Freeman, L. M.; Meredith, D. L.
1990-01-01
The U.S. Bureau of Mines is currently investigating ways to combine the control capabilities of fuzzy logic with the learning capabilities of genetic algorithms. Fuzzy logic allows for the uncertainty inherent in most control problems to be incorporated into conventional expert systems. Although fuzzy logic based expert systems have been used successfully for controlling a number of physical systems, the selection of acceptable fuzzy membership functions has generally been a subjective decision. High performance fuzzy membership functions for a fuzzy logic controller that manipulates a mathematical model simulating the autonomous rendezvous of spacecraft are learned using a genetic algorithm, a search technique based on the mechanics of natural genetics. The membership functions learned by the genetic algorithm provide for a more efficient fuzzy logic controller than membership functions selected by the authors for the rendezvous problem. Thus, genetic algorithms are potentially an effective and structured approach for learning fuzzy membership functions.
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A "Hands on" Strategy for Teaching Genetic Algorithms to Undergraduates
ERIC Educational Resources Information Center
Venables, Anne; Tan, Grace
2007-01-01
Genetic algorithms (GAs) are a problem solving strategy that uses stochastic search. Since their introduction (Holland, 1975), GAs have proven to be particularly useful for solving problems that are "intractable" using classical methods. The language of genetic algorithms (GAs) is heavily laced with biological metaphors from evolutionary…
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The potential of genetic algorithms for conceptual design of rotor systems
NASA Technical Reports Server (NTRS)
Crossley, William A.; Wells, Valana L.; Laananen, David H.
1993-01-01
The capabilities of genetic algorithms as a non-calculus based, global search method make them potentially useful in the conceptual design of rotor systems. Coupling reasonably simple analysis tools to the genetic algorithm was accomplished, and the resulting program was used to generate designs for rotor systems to match requirements similar to those of both an existing helicopter and a proposed helicopter design. This provides a comparison with the existing design and also provides insight into the potential of genetic algorithms in design of new rotors.
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Genetic Algorithm for Initial Orbit Determination with Too Short Arc (Continued)
NASA Astrophysics Data System (ADS)
Li, Xin-ran; Wang, Xin
2017-04-01
When the genetic algorithm is used to solve the problem of too short-arc (TSA) orbit determination, due to the difference of computing process between the genetic algorithm and the classical method, the original method for outlier deletion is no longer applicable. In the genetic algorithm, the robust estimation is realized by introducing different loss functions for the fitness function, then the outlier problem of the TSA orbit determination is solved. Compared with the classical method, the genetic algorithm is greatly simplified by introducing in different loss functions. Through the comparison on the calculations of multiple loss functions, it is found that the least median square (LMS) estimation and least trimmed square (LTS) estimation can greatly improve the robustness of the TSA orbit determination, and have a high breakdown point.
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NASA Technical Reports Server (NTRS)
Wang, Lui; Valenzuela-Rendon, Manuel
1993-01-01
The Space Station Freedom will require the supply of items in a regular fashion. A schedule for the delivery of these items is not easy to design due to the large span of time involved and the possibility of cancellations and changes in shuttle flights. This paper presents the basic concepts of a genetic algorithm model, and also presents the results of an effort to apply genetic algorithms to the design of propellant resupply schedules. As part of this effort, a simple simulator and an encoding by which a genetic algorithm can find near optimal schedules have been developed. Additionally, this paper proposes ways in which robust schedules, i.e., schedules that can tolerate small changes, can be found using genetic algorithms.
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Coated ceramic breeder materials
Tam, Shiu-Wing; Johnson, Carl E.
1987-01-01
A breeder material for use in a breeder blanket of a nuclear reactor is disclosed. The breeder material comprises a core material of lithium containing ceramic particles which has been coated with a neutron multiplier such as Be or BeO, which coating has a higher thermal conductivity than the core material.
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Coated ceramic breeder materials
Tam, Shiu-Wing; Johnson, Carl E.
1987-04-07
A breeder material for use in a breeder blanket of a nuclear reactor is disclosed. The breeder material comprises a core material of lithium containing ceramic particles which has been coated with a neutron multiplier such as Be or BeO, which coating has a higher thermal conductivity than the core material.
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Genetic drift and collective dispersal can result in chaotic genetic patchiness.
Broquet, Thomas; Viard, Frédérique; Yearsley, Jonathan M
2013-06-01
Chaotic genetic patchiness denotes unexpected patterns of genetic differentiation that are observed at a fine scale and are not stable in time. These patterns have been described in marine species with free-living larvae, but are unexpected because they occur at a scale below the dispersal range of pelagic larvae. At the scale where most larvae are immigrants, theory predicts spatially homogeneous, temporally stable genetic variation. Empirical studies have suggested that genetic drift interacts with complex dispersal patterns to create chaotic genetic patchiness. Here we use a co-ancestry model and individual-based simulations to test this idea. We found that chaotic genetic patterns (qualified by global FST and spatio-temporal variation in FST's between pairs of samples) arise from the combined effects of (1) genetic drift created by the small local effective population sizes of the sessile phase and variance in contribution among breeding groups and (2) collective dispersal of related individuals in the larval phase. Simulations show that patchiness levels qualitatively comparable to empirical results can be produced by a combination of strong variance in reproductive success and mild collective dispersal. These results call for empirical studies of the effective number of breeders producing larval cohorts, and population genetics at the larval stage. © 2012 The Author(s). Evolution © 2012 The Society for the Study of Evolution.
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An Improved Heuristic Method for Subgraph Isomorphism Problem
NASA Astrophysics Data System (ADS)
Xiang, Yingzhuo; Han, Jiesi; Xu, Haijiang; Guo, Xin
2017-09-01
This paper focus on the subgraph isomorphism (SI) problem. We present an improved genetic algorithm, a heuristic method to search the optimal solution. The contribution of this paper is that we design a dedicated crossover algorithm and a new fitness function to measure the evolution process. Experiments show our improved genetic algorithm performs better than other heuristic methods. For a large graph, such as a subgraph of 40 nodes, our algorithm outperforms the traditional tree search algorithms. We find that the performance of our improved genetic algorithm does not decrease as the number of nodes in prototype graphs.
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Genetic algorithms for adaptive real-time control in space systems
NASA Technical Reports Server (NTRS)
Vanderzijp, J.; Choudry, A.
1988-01-01
Genetic Algorithms that are used for learning as one way to control the combinational explosion associated with the generation of new rules are discussed. The Genetic Algorithm approach tends to work best when it can be applied to a domain independent knowledge representation. Applications to real time control in space systems are discussed.
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Zago, B W; Barelli, M A A; Hoogerheide, E S S; Corrêa, C L; Delforno, G I S; da Silva, C J
2017-08-17
Genetic variability of cassava (Manihot esculenta Crantz) in Brazil is wide, being this the result of natural and cultural selection during pre- and post-domestication of the species in different environments. Given the number of species of the genus found in the region (38 of a total of 98 species), the central region of Brazil was defined as the primary center of cassava diversity. Therefore, genetic diversity characterization of cassava accessions is fundamental, both for farmers and for plant breeders, because it allows the organization of genetic resources and better utilization of available genetic diversity. This research aims to assess genetic divergence of cassava accessions from the south-central region of the State of Mato Grosso, based on multi-categorical morphological traits. For this purpose, 38 qualitative and quantitative morphological descriptors were used. Genetic diversity was expressed by the genetic similarity index, with subsequent clustering of accessions by the modified Tocher's procedure and UPGMA. Of 38 descriptors, only growth habit of stem showed no variability. Tocher and UPGMA methods were efficient and corroborated on group composition. Both methods were able to group accessions of different localities in distinct group consistency.
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2013-01-01
intelligently selecting waveform parameters using adaptive algorithms. The adaptive algorithms optimize the waveform parameters based on (1) the EM...the environment. 15. SUBJECT TERMS cognitive radar, adaptive sensing, spectrum sensing, multi-objective optimization, genetic algorithms, machine...detection and classification block diagram. .........................................................6 Figure 5. Genetic algorithm block diagram
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JPRS Report, Science & Technology, China: Energy.
1992-03-30
breeder reactors should become...the primary type of reactors . In developing breeder reactors , we should follow the path of using metal fuel. Breeder reactors give us more time to...first reactor used for power generation was a fast reactor : the " Breeder 1" reactor at the Idaho National Reactor Test Center which was used to
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Accelerating the domestication of forest trees in a changing world.
Harfouche, Antoine; Meilan, Richard; Kirst, Matias; Morgante, Michele; Boerjan, Wout; Sabatti, Maurizio; Scarascia Mugnozza, Giuseppe
2012-02-01
In light of impending water and arable land shortages, population growth and climate change, it is more important than ever to examine how forest tree domestication can be accelerated to sustainably meet future demands for wood, biomass, paper, fuel and biomaterials. Because of long breeding cycles, tree domestication cannot be rapidly achieved through traditional genetic improvement methods alone. Integrating modern genetic and genomic techniques with conventional breeding will expedite tree domestication. Breeders will only embrace these technologies if they are cost-effective and readily accessible, and forest landowners will only adopt end-products that meet with regulatory approval and public acceptance. All parties involved must work together to achieve these objectives for the benefit of society. Copyright © 2011 Elsevier Ltd. All rights reserved.
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GENETIC EFFECTS OF X IRRADIATION OF 10, 15, AND 20 GENERATIONS OF MALE MICE
DOE Office of Scientific and Technical Information (OSTI.GOV)
Spalding, J.F.; Brooks, M.R.; Archuleta, R.F.
1963-01-01
Male mice were exposed to 200 rads of x rays (acute whole body exposures) for 20 consecutive generations. Comparative studies were done on breeding characteristics of offspring from 10 and 15 generations of irradinted males. Irradiated line mice were less efficient breeders than were control line mice, and the decrement increased with the number of generations irradiated. Female mice from 10 to 20 generations of irradiated males were studied for resistance to low intensity gamma -rays and were found to be less resistant than control line mice. It was concluded that x irradiation to consecutive generations of male mice producesmore » a genetic decrement affecting both breeding and efficiency and stamina. (auth)« less
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Warehouse stocking optimization based on dynamic ant colony genetic algorithm
NASA Astrophysics Data System (ADS)
Xiao, Xiaoxu
2018-04-01
In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.
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A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.
Hajri, S; Liouane, N; Hammadi, S; Borne, P
2000-01-01
Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.
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Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship s flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm s design, along with mathematical models of the algorithm s performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
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Experimental Performance of a Genetic Algorithm for Airborne Strategic Conflict Resolution
NASA Technical Reports Server (NTRS)
Karr, David A.; Vivona, Robert A.; Roscoe, David A.; DePascale, Stephen M.; Consiglio, Maria
2009-01-01
The Autonomous Operations Planner, a research prototype flight-deck decision support tool to enable airborne self-separation, uses a pattern-based genetic algorithm to resolve predicted conflicts between the ownship and traffic aircraft. Conflicts are resolved by modifying the active route within the ownship's flight management system according to a predefined set of maneuver pattern templates. The performance of this pattern-based genetic algorithm was evaluated in the context of batch-mode Monte Carlo simulations running over 3600 flight hours of autonomous aircraft in en-route airspace under conditions ranging from typical current traffic densities to several times that level. Encountering over 8900 conflicts during two simulation experiments, the genetic algorithm was able to resolve all but three conflicts, while maintaining a required time of arrival constraint for most aircraft. Actual elapsed running time for the algorithm was consistent with conflict resolution in real time. The paper presents details of the genetic algorithm's design, along with mathematical models of the algorithm's performance and observations regarding the effectiveness of using complimentary maneuver patterns when multiple resolutions by the same aircraft were required.
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Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011
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Scalability problems of simple genetic algorithms.
Thierens, D
1999-01-01
Scalable evolutionary computation has become an intensively studied research topic in recent years. The issue of scalability is predominant in any field of algorithmic design, but it became particularly relevant for the design of competent genetic algorithms once the scalability problems of simple genetic algorithms were understood. Here we present some of the work that has aided in getting a clear insight in the scalability problems of simple genetic algorithms. Particularly, we discuss the important issue of building block mixing. We show how the need for mixing places a boundary in the GA parameter space that, together with the boundary from the schema theorem, delimits the region where the GA converges reliably to the optimum in problems of bounded difficulty. This region shrinks rapidly with increasing problem size unless the building blocks are tightly linked in the problem coding structure. In addition, we look at how straightforward extensions of the simple genetic algorithm-namely elitism, niching, and restricted mating are not significantly improving the scalability problems.
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Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng
2015-01-01
The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.
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Challenges and opportunities in genetic improvement of local livestock breeds
Biscarini, Filippo; Nicolazzi, Ezequiel L.; Stella, Alessandra; Boettcher, Paul J.; Gandini, Gustavo
2015-01-01
Sufficient genetic variation in livestock populations is necessary both for adaptation to future changes in climate and consumer demand, and for continual genetic improvement of economically important traits. Unfortunately, the current trend is for reduced genetic variation, both within and across breeds. The latter occurs primarily through the loss of small, local breeds. Inferior production is a key driver for loss of small breeds, as they are replaced by high-output international transboundary breeds. Selection to improve productivity of small local breeds is therefore critical for their long term survival. The objective of this paper is to review the technology options available for the genetic improvement of small local breeds and discuss their feasibility. Most technologies have been developed for the high-input breeds and consequently are more favorably applied in that context. Nevertheless, their application in local breeds is not precluded and can yield significant benefits, especially when multiple technologies are applied in close collaboration with farmers and breeders. Breeding strategies that require cooperation and centralized decision-making, such as optimal contribution selection, may in fact be more easily implemented in small breeds. PMID:25763010
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An investigation of messy genetic algorithms
NASA Technical Reports Server (NTRS)
Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley
1990-01-01
Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.
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Global Optimization of a Periodic System using a Genetic Algorithm
NASA Astrophysics Data System (ADS)
Stucke, David; Crespi, Vincent
2001-03-01
We use a novel application of a genetic algorithm global optimizatin technique to find the lowest energy structures for periodic systems. We apply this technique to colloidal crystals for several different stoichiometries of binary and trinary colloidal crystals. This application of a genetic algorithm is decribed and results of likely candidate structures are presented.
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Research and application of multi-agent genetic algorithm in tower defense game
NASA Astrophysics Data System (ADS)
Jin, Shaohua
2018-04-01
In this paper, a new multi-agent genetic algorithm based on orthogonal experiment is proposed, which is based on multi-agent system, genetic algorithm and orthogonal experimental design. The design of neighborhood competition operator, orthogonal crossover operator, Son and self-learning operator. The new algorithm is applied to mobile tower defense game, according to the characteristics of the game, the establishment of mathematical models, and finally increases the value of the game's monster.
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Aerodynamic Shape Optimization Using A Real-Number-Encoded Genetic Algorithm
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2001-01-01
A new method for aerodynamic shape optimization using a genetic algorithm with real number encoding is presented. The algorithm is used to optimize three different problems, a simple hill climbing problem, a quasi-one-dimensional nozzle problem using an Euler equation solver and a three-dimensional transonic wing problem using a nonlinear potential solver. Results indicate that the genetic algorithm is easy to implement and extremely reliable, being relatively insensitive to design space noise.
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Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
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Genetic algorithms as global random search methods
NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.
1995-01-01
Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that that schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solution and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.
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Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity
Louis, S.J.; Raines, G.L.
2003-01-01
We use a genetic algorithm to calibrate a spatially and temporally resolved cellular automata to model mining activity on public land in Idaho and western Montana. The genetic algorithm searches through a space of transition rule parameters of a two dimensional cellular automata model to find rule parameters that fit observed mining activity data. Previous work by one of the authors in calibrating the cellular automaton took weeks - the genetic algorithm takes a day and produces rules leading to about the same (or better) fit to observed data. These preliminary results indicate that genetic algorithms are a viable tool in calibrating cellular automata for this application. Experience gained during the calibration of this cellular automata suggests that mineral resource information is a critical factor in the quality of the results. With automated calibration, further refinements of how the mineral-resource information is provided to the cellular automaton will probably improve our model.
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Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem
NASA Astrophysics Data System (ADS)
Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf
2017-08-01
Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.
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Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator
Mohamd Shoukry, Alaa; Gani, Showkat
2017-01-01
Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364
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Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.
Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat
2017-01-01
Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.
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Wasson, Anton P; Chiu, Grace S; Zwart, Alexander B; Binns, Timothy R
2017-01-01
Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly "above ground," little progress has been made "below ground"; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an "idealized" relative intensity function for the root distribution over depth. Our approach was used to determine heritability : how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity.
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A modified genetic algorithm with fuzzy roulette wheel selection for job-shop scheduling problems
NASA Astrophysics Data System (ADS)
Thammano, Arit; Teekeng, Wannaporn
2015-05-01
The job-shop scheduling problem is one of the most difficult production planning problems. Since it is in the NP-hard class, a recent trend in solving the job-shop scheduling problem is shifting towards the use of heuristic and metaheuristic algorithms. This paper proposes a novel metaheuristic algorithm, which is a modification of the genetic algorithm. This proposed algorithm introduces two new concepts to the standard genetic algorithm: (1) fuzzy roulette wheel selection and (2) the mutation operation with tabu list. The proposed algorithm has been evaluated and compared with several state-of-the-art algorithms in the literature. The experimental results on 53 JSSPs show that the proposed algorithm is very effective in solving the combinatorial optimization problems. It outperforms all state-of-the-art algorithms on all benchmark problems in terms of the ability to achieve the optimal solution and the computational time.
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A New Challenge for Compression Algorithms: Genetic Sequences.
ERIC Educational Resources Information Center
Grumbach, Stephane; Tahi, Fariza
1994-01-01
Analyzes the properties of genetic sequences that cause the failure of classical algorithms used for data compression. A lossless algorithm, which compresses the information contained in DNA and RNA sequences by detecting regularities such as palindromes, is presented. This algorithm combines substitutional and statistical methods and appears to…
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NASA Astrophysics Data System (ADS)
Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.
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Refined genetic algorithm -- Economic dispatch example
DOE Office of Scientific and Technical Information (OSTI.GOV)
Sheble, G.B.; Brittig, K.
1995-02-01
A genetic-based algorithm is used to solve an economic dispatch (ED) problem. The algorithm utilizes payoff information of perspective solutions to evaluate optimality. Thus, the constraints of classical LaGrangian techniques on unit curves are eliminated. Using an economic dispatch problem as a basis for comparison, several different techniques which enhance program efficiency and accuracy, such as mutation prediction, elitism, interval approximation and penalty factors, are explored. Two unique genetic algorithms are also compared. The results are verified for a sample problem using a classical technique.
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Immune allied genetic algorithm for Bayesian network structure learning
NASA Astrophysics Data System (ADS)
Song, Qin; Lin, Feng; Sun, Wei; Chang, KC
2012-06-01
Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.
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Flexible Space-Filling Designs for Complex System Simulations
2013-06-01
interior of the experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with...Computer Experiments, Design of Experiments, Genetic Algorithm , Latin Hypercube, Response Surface Methodology, Nearly Orthogonal 15. NUMBER OF PAGES 147...experimental region and cannot fit higher-order models. We present a genetic algorithm that constructs space-filling designs with minimal correlations
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Genetic algorithms in conceptual design of a light-weight, low-noise, tilt-rotor aircraft
NASA Technical Reports Server (NTRS)
Wells, Valana L.
1996-01-01
This report outlines research accomplishments in the area of using genetic algorithms (GA) for the design and optimization of rotorcraft. It discusses the genetic algorithm as a search and optimization tool, outlines a procedure for using the GA in the conceptual design of helicopters, and applies the GA method to the acoustic design of rotors.
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Self-calibration of a noisy multiple-sensor system with genetic algorithms
NASA Astrophysics Data System (ADS)
Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua
1996-01-01
This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.
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Increasing Prediction the Original Final Year Project of Student Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Saragih, Rijois Iboy Erwin; Turnip, Mardi; Sitanggang, Delima; Aritonang, Mendarissan; Harianja, Eva
2018-04-01
Final year project is very important forgraduation study of a student. Unfortunately, many students are not seriouslydidtheir final projects. Many of studentsask for someone to do it for them. In this paper, an application of genetic algorithms to predict the original final year project of a studentis proposed. In the simulation, the data of the final project for the last 5 years is collected. The genetic algorithm has several operators namely population, selection, crossover, and mutation. The result suggest that genetic algorithm can do better prediction than other comparable model. Experimental results of predicting showed that 70% was more accurate than the previous researched.
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Morshed, Rima; Hosseini, Hossein; Langeroudi, Arash Ghalyanchi; Fard, Mohammad Hassan Bozorgmehri; Charkhkar, Saeid
2017-06-01
Twenty-four fowl adenoviruses (FAdVs) were isolated from broiler and broiler breeder pullet flocks in Iran during 2013-2016 and were identified and characterized. All FAdVs were from inclusion body hepatitis (IBH) cases, showing an enlarged and pale yellow liver with multiple petechial hemorrhages. Phylogenetic analyses of partial hexon gene sequences are an adequate and quick method for differentiation and genotyping. The isolates were subjected to PCR to amplify a 590-bp fragment from the hexon gene. Sequence analysis revealed the presence of two species D and E. Eighty FAdV isolates were genetically related to the strain EU979378 of FAdV-11 (96.5% to 97.6% identity), and six isolates were related to the strain EU979375 of FAdV-8b (97% identity). The results indicated that two FAdV serotypes (11 and 8b) are high prevalence serotypes of FAdVs in Iran and are pathogenic enough to cause IBH in young chicks. Therefore, preventive measures against FAdV infection on poultry farms should be implemented.
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3D Protein structure prediction with genetic tabu search algorithm
2010-01-01
Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256
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Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2004-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
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Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.
2005-01-01
A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.
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Genetic algorithm dynamics on a rugged landscape
NASA Astrophysics Data System (ADS)
Bornholdt, Stefan
1998-04-01
The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.
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MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali
2017-01-01
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms. PMID:28979308
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MotieGhader, Habib; Gharaghani, Sajjad; Masoudi-Sobhanzadeh, Yosef; Masoudi-Nejad, Ali
2017-01-01
Feature selection is of great importance in Quantitative Structure-Activity Relationship (QSAR) analysis. This problem has been solved using some meta-heuristic algorithms such as GA, PSO, ACO and so on. In this work two novel hybrid meta-heuristic algorithms i.e. Sequential GA and LA (SGALA) and Mixed GA and LA (MGALA), which are based on Genetic algorithm and learning automata for QSAR feature selection are proposed. SGALA algorithm uses advantages of Genetic algorithm and Learning Automata sequentially and the MGALA algorithm uses advantages of Genetic Algorithm and Learning Automata simultaneously. We applied our proposed algorithms to select the minimum possible number of features from three different datasets and also we observed that the MGALA and SGALA algorithms had the best outcome independently and in average compared to other feature selection algorithms. Through comparison of our proposed algorithms, we deduced that the rate of convergence to optimal result in MGALA and SGALA algorithms were better than the rate of GA, ACO, PSO and LA algorithms. In the end, the results of GA, ACO, PSO, LA, SGALA, and MGALA algorithms were applied as the input of LS-SVR model and the results from LS-SVR models showed that the LS-SVR model had more predictive ability with the input from SGALA and MGALA algorithms than the input from all other mentioned algorithms. Therefore, the results have corroborated that not only is the predictive efficiency of proposed algorithms better, but their rate of convergence is also superior to the all other mentioned algorithms.
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U.S. Nuclear Cooperation with India: Issues for Congress
2008-11-03
separation list: ! 8 indigenous Indian power reactors ! Fast Breeder test Reactor (FTBR) and Prototype Fast Breeder Reactors (PFBR) under construction...facilities like reprocessing and enrichment plants and breeder reactors could be viewed as providing a significant nonproliferation benefit because the... breeder reactors would support the 2002 U.S. National Strategy to Combat Weapons of Mass Destruction, in which the United States pledged to “continue to
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U.S. Nuclear Cooperation with India: Issues for Congress
2008-10-02
8 indigenous Indian power reactors ! Fast Breeder test Reactor (FTBR) and Prototype Fast Breeder Reactors (PFBR) under construction ! Enrichment... breeder reactors could be viewed as providing a significant nonproliferation benefit because the materials produced by these plants are a few steps closer...to potential use in a bomb. In addition, safeguards on enrichment, reprocessing plants, and breeder reactors would support the 2002 U.S. National
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Voris, H C; Wittum, T E; Rajala-Schultz, P J; Lord, L K
2011-07-01
The Internet is an increasingly common way for consumers to purchase puppies. Yet very little information is available about the types of puppies sold via the Internet. In addition these sales are not subject to United States Depart of Agriculture (USDA) regulation. The objectives of the study were to describe puppies sold via the Internet, to assess the characteristics that contribute to the cost of a puppy, and to compare puppies sold via the Internet with puppies sold by American Kennel Club (AKC) Parent Club breeders. Over 14 weeks in 2008, Yorkshire Terrier, Shih Tzu, English Bulldog, Boxer, and Labrador Retriever puppies for sale on two large-scale online puppy sales sites were categorized based on their Internet advertisements. Data were collected in three categories: puppy characteristics, health characteristics, and policies (such as spay/neuter requirement, health guarantee, and return policy). After the survey was completed, 25 AKC Parent Club breeders and 25 other breeders who advertised via one of the puppy sales websites were randomly selected and interviewed over the phone. Small breed puppies were most frequently advertised with 35.2% (1228/3485) of advertisements for Yorkshire Terriers and 23.0% (802/3485) for Shih Tzus. Almost one quarter of Internet breeders 768/3474 (22.2%) advertised four or more different dog breeds. Champion bloodlines increased the cost of a puppy of all breeds. AKC Parent Club breeders 21/25 (84%) were more likely to mention breed-specific health screening tests when compared to Internet breeders 7/25 (28%). Consumers should apply the same standards for purchasing from a breeder found through a puppy sales site as they would for purchasing from a local breeder. Breeders who advertise at one of the large-scale puppy sales websites are less knowledgeable about breed-specific health issues compared to an AKC Parent Club breeder. Internet breeders are less likely to perform these screening tests on their breeding dogs and may breed dogs with undesirable heritable health risks. © 2011 Elsevier B.V. All rights reserved.
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An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints
Rao, Yunqing; Qi, Dezhong; Li, Jinling
2013-01-01
For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem. PMID:24489491
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An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.
Rao, Yunqing; Qi, Dezhong; Li, Jinling
2013-01-01
For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.
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Galli, Luigina; Facchetti, Susanna; Raffetti, Elena; Donato, Francesco; D'Anna, Mauro
2015-11-01
The daily occupation as a swine breeder involves exposure to several bacterial components and organic dusts and inhalation of a large amount of allergens. To investigate the risk of respiratory diseases and atopy in swine breeders compared with the general population living in the same area. A population-based cross-sectional study was conducted in an agricultural area of northern Italy that enrolled a random sample of resident male breeders and non-breeders. Demographic features, comorbidities, and presence of allergic respiratory disease were retrieved through interview. Prick tests for common allergens were performed. An evaluation of pollen and mold in air samples taken inside and outside some swine confinement buildings also was performed. One hundred one male breeders (78 native-born, mean age ± SD 43.0 ± 11.1 years) and 82 non-breeders (43.0 ± 11.1 years) were enrolled. When restricting the analysis to native-born subjects, breeders vs non-breeders showed a lower prevalence of respiratory allergy (12.8% vs 31.1%, respectively, P = .002), asthma (6.4% vs 15.8%, P = .059), rhinitis (16.7% vs 51.2%, P < .001), persistent cough (5.1% vs 15.9%, P = .028), and sensitization to grass (7.7% vs 25.6%, P = .002). There was no difference in prick test positivity, polysensitization, nasal cytologic pattern, forced expiratory volume in 1 second, and the ratio of forced expiratory volume in 1 second to forced vital capacity between breeders and non-breeders. Air concentration of molds and pollens was lower inside than outside the swine buildings investigated, particularly when the pigs were inside vs outside the buildings. This study suggests that swine breeding does not increase, and might decrease, the risk of pollen sensitization and allergic disease. Copyright © 2015 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.
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Pose estimation for augmented reality applications using genetic algorithm.
Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen
2005-12-01
This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.
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Optimization of laminated stacking sequence for buckling load maximization by genetic algorithm
NASA Technical Reports Server (NTRS)
Le Riche, Rodolphe; Haftka, Raphael T.
1992-01-01
The use of a genetic algorithm to optimize the stacking sequence of a composite laminate for buckling load maximization is studied. Various genetic parameters including the population size, the probability of mutation, and the probability of crossover are optimized by numerical experiments. A new genetic operator - permutation - is proposed and shown to be effective in reducing the cost of the genetic search. Results are obtained for a graphite-epoxy plate, first when only the buckling load is considered, and then when constraints on ply contiguity and strain failure are added. The influence on the genetic search of the penalty parameter enforcing the contiguity constraint is studied. The advantage of the genetic algorithm in producing several near-optimal designs is discussed.
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Development of a Tool for an Efficient Calibration of CORSIM Models
DOT National Transportation Integrated Search
2014-08-01
This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...
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Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater
2010-12-01
German Collection of Microorganisms and Cell Cultures) GA Genetic Algorithms GA-ANN Genetic Algorithm Artificial Neural Network GMO genetically...for in situ treatment of perchlorate in groundwater. This is accomplished without the addition of genetically engineered microorganisms ( GMOs ) to the...perchlorate, even in the presence of oxygen and without the addition of genetically engineered microorganisms ( GMOs ) to the environment. This approach
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Strategic growth decisions in helper cichlids.
Heg, Dik; Bender, Nicole; Hamilton, Ian
2004-01-01
Recently, it has been shown that group-living subordinate clownfish Amphiprion percula increase their growth rate after acquiring the dominant breeder male position in the group. Evidence was found for strategic growth adjustments of subordinate fishes depending on the threat of eviction, i.e. subordinates adjust their growth rates so they remain smaller than the dominant fish and thereby limit the threat of being expelled from the territory. However, it is impossible to exclude several alternative factors that potentially could have influenced the observed changes in growth, owing to the nature of that experiment (removing the second-ranking fish--the breeder male--caused the third-ranking fish to change sex to become breeder male and change rank). We studied strategic growth decisions in the group-living Lake Tanganyika cichlid Neolamprologus pulcher under controlled laboratory conditions with ad libitum food availability. First, we show that male breeders grow faster than subordinate male helpers of the same initial size and confirm that N. pulcher shows status-dependent growth. Second, we improved on the experimental design by not removing the dominant breeder male in the group; instead we replaced the breeder male with a new breeder male in a full factorial design and measured growth of the subordinate male helpers is a function of the size difference with the old and the new breeder male. As predicted, male helpers showed strategic growth adjustments, i.e. growing faster when the size difference with the breeder male is large. Strategic growth adjustments were less pronounced than status-dependent growth adjustments. PMID:15801617
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Strategic growth decisions in helper cichlids.
Heg, Dik; Bender, Nicole; Hamilton, Ian
2004-12-07
Recently, it has been shown that group-living subordinate clownfish Amphiprion percula increase their growth rate after acquiring the dominant breeder male position in the group. Evidence was found for strategic growth adjustments of subordinate fishes depending on the threat of eviction, i.e. subordinates adjust their growth rates so they remain smaller than the dominant fish and thereby limit the threat of being expelled from the territory. However, it is impossible to exclude several alternative factors that potentially could have influenced the observed changes in growth, owing to the nature of that experiment (removing the second-ranking fish--the breeder male--caused the third-ranking fish to change sex to become breeder male and change rank). We studied strategic growth decisions in the group-living Lake Tanganyika cichlid Neolamprologus pulcher under controlled laboratory conditions with ad libitum food availability. First, we show that male breeders grow faster than subordinate male helpers of the same initial size and confirm that N. pulcher shows status-dependent growth. Second, we improved on the experimental design by not removing the dominant breeder male in the group; instead we replaced the breeder male with a new breeder male in a full factorial design and measured growth of the subordinate male helpers is a function of the size difference with the old and the new breeder male. As predicted, male helpers showed strategic growth adjustments, i.e. growing faster when the size difference with the breeder male is large. Strategic growth adjustments were less pronounced than status-dependent growth adjustments.
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[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].
Smirnova, N I; Agafonov, D A; Zadnova, S P; Cherkasov, A V; Kutyrev, V V
2014-01-01
Development of an algorithm of genetically altered Vibrio cholerae biovar El Tor strai identification that ensures determination of serogroup, serovar and biovar of the studied isolate based on pheno- and genotypic properties, detection of genetically altered cholera El Tor causative agents, their differentiation by epidemic potential as well as evaluation of variability of key pathogenicity genes. Complex analysis of 28 natural V. cholerae strains was carried out by using traditional microbiological methods, PCR and fragmentary sequencing. An algorithm of toxigenic genetically altered V. cholerae biovar El Tor strain identification was developed that includes 4 stages: determination of serogroup, serovar and biovar based on phenotypic properties, confirmation of serogroup and biovar based on molecular-genetic properties determination of strains as genetically altered, differentiation of genetically altered strains by their epidemic potential and detection of ctxB and tcpA key pathogenicity gene polymorphism. The algorithm is based on the use of traditional microbiological methods, PCR and sequencing of gene fragments. The use of the developed algorithm will increase the effectiveness of detection of genetically altered variants of the cholera El Tor causative agent, their differentiation by epidemic potential and will ensure establishment of polymorphism of genes that code key pathogenicity factors for determination of origins of the strains and possible routes of introduction of the infection.
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Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed
2017-01-05
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.
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Distributed genetic algorithms for the floorplan design problem
NASA Technical Reports Server (NTRS)
Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.
1991-01-01
Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.
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Genetic-economic evaluation of traits in a goose meat enterprise.
Shalev, B A; Pasternak, H
1999-05-01
1. Goose can be considered as an additional and inexpensive meat source, provided that the marketing age does not exceed 8 weeks. Using the ability of geese to eat grass may reduce the intake of concentrated food up to 30%. 2. According to an equation developed, growth rate accounts for about 58% of the annual breeding gains, egg number 28%, feather yield 10%, fertility and mortality about 2%. These values are about the same for a wide range of food prices. 3. Employing realistic values for expected annual genetic gains reveals that the customary practice of keeping breeders for 5 to 6 years should be replaced by a much shorter cycle of 3 years because the economic gain from the shorter generation interval of selection exceeds the replacement costs.
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Long, C R; Walker, S C; Tang, R T; Westhusin, M E
2003-01-01
As advanced reproductive technologies become more efficient and repeatable in livestock and laboratory species, new opportunities will evolve to apply these techniques to alternative and non-traditional species. This will result in new markets requiring unique business models that address issues of animal welfare and consumer acceptance on a much different level than the livestock sector. Advanced reproductive technologies and genetic engineering will be applied to each species in innovative ways to provide breeders more alternatives for the preservation and propagation of elite animals in each sector. The commercialization of advanced reproductive techniques in these niche markets should be considered a useful tool for conservation of genetic material from endangered or unique animals as well as production of biomedical models of human disease. Copyright 2002 Elsevier Science Inc.
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Monteiro, Filipa; Vidigal, Patrícia; Barros, André B.; Monteiro, Ana; Oliveira, Hugo R.; Viegas, Wanda
2016-01-01
Rye (Secale cereale L.) is a cereal crop of major importance in many parts of Europe and rye breeders are presently very concerned with the restrict pool of rye genetic resources available. Such narrowing of rye genetic diversity results from the presence of “Petkus” pool in most modern rye varieties as well as “Petkus” × “Carsten” heterotic pool in hybrid rye breeding programs. Previous studies on rye's genetic diversity revealed moreover a common genetic background on landraces (ex situ) and cultivars, regardless of breeding level or geographical origin. Thus evaluation of in situ populations is of utmost importance to unveil “on farm” diversity, which is largely undervalued. Here, we perform the first comprehensive assessment of rye's genetic diversity and population structuring using cultivars, ex situ landraces along a comprehensive sampling of in situ accessions from Portugal, through a molecular-directed analysis using SSRs markers. Rye genetic diversity and population structure analysis does not present any geographical trend but disclosed marked differences between genetic backgrounds of in situ accessions and those of cultivars/ex situ collections. Such genetic distinctiveness of in situ accessions highlights their unexplored potential as new genetic resources, which can be used to boost rye breeding strategies and the production of new varieties. Overall, our study successfully demonstrates the high prospective impact of comparing genetic diversity and structure of cultivars, ex situ, and in situ samples in ascertaining the status of plant genetic resources (PGR). PMID:27630658
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Evolving aerodynamic airfoils for wind turbines through a genetic algorithm
NASA Astrophysics Data System (ADS)
Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI
2017-01-01
Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.
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An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm
NASA Technical Reports Server (NTRS)
Weir, John M.; Wells, B. Earl
2003-01-01
Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.
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Phase Reconstruction from FROG Using Genetic Algorithms[Frequency-Resolved Optical Gating
DOE Office of Scientific and Technical Information (OSTI.GOV)
Omenetto, F.G.; Nicholson, J.W.; Funk, D.J.
1999-04-12
The authors describe a new technique for obtaining the phase and electric field from FROG measurements using genetic algorithms. Frequency-Resolved Optical Gating (FROG) has gained prominence as a technique for characterizing ultrashort pulses. FROG consists of a spectrally resolved autocorrelation of the pulse to be measured. Typically a combination of iterative algorithms is used, applying constraints from experimental data, and alternating between the time and frequency domain, in order to retrieve an optical pulse. The authors have developed a new approach to retrieving the intensity and phase from FROG data using a genetic algorithm (GA). A GA is a generalmore » parallel search technique that operates on a population of potential solutions simultaneously. Operators in a genetic algorithm, such as crossover, selection, and mutation are based on ideas taken from evolution.« less
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Territory inheritance in clownfish.
Buston, Peter M
2004-01-01
Animal societies composed of breeders and non-breeders present a challenge to evolutionary theory because it is not immediately apparent how natural selection can preserve the genes that underlie non-breeding strategies. The clownfish Amphiprion percula forms groups composed of a breeding pair and 0-4 non-breeders. Non-breeders gain neither present direct, nor present indirect benefits from the association. To determine whether non-breeders obtain future direct benefits, I investigated the pattern of territory inheritance. I show that non-breeders stand to inherit the territory within which they reside. Moreover, they form a perfect queue for breeding positions; a queue from which nobody disperses and within which nobody contests. I suggest that queuing might be favoured by selection because it confers a higher probability of attaining breeding status than either dispersing or contesting. This study illustrates that, within animal societies, individuals may tolerate non-breeding positions solely because of their potential to realize benefits in the future. PMID:15252999
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Territory inheritance in clownfish.
Buston, Peter M
2004-05-07
Animal societies composed of breeders and non-breeders present a challenge to evolutionary theory because it is not immediately apparent how natural selection can preserve the genes that underlie non-breeding strategies. The clownfish Amphiprion percula forms groups composed of a breeding pair and 0-4 non-breeders. Non-breeders gain neither present direct, nor present indirect benefits from the association. To determine whether non-breeders obtain future direct benefits, I investigated the pattern of territory inheritance. I show that non-breeders stand to inherit the territory within which they reside. Moreover, they form a perfect queue for breeding positions; a queue from which nobody disperses and within which nobody contests. I suggest that queuing might be favoured by selection because it confers a higher probability of attaining breeding status than either dispersing or contesting. This study illustrates that, within animal societies, individuals may tolerate non-breeding positions solely because of their potential to realize benefits in the future.
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NASA Astrophysics Data System (ADS)
Adya Zizwan, Putra; Zarlis, Muhammad; Budhiarti Nababan, Erna
2017-12-01
The determination of Centroid on K-Means Algorithm directly affects the quality of the clustering results. Determination of centroid by using random numbers has many weaknesses. The GenClust algorithm that combines the use of Genetic Algorithms and K-Means uses a genetic algorithm to determine the centroid of each cluster. The use of the GenClust algorithm uses 50% chromosomes obtained through deterministic calculations and 50% is obtained from the generation of random numbers. This study will modify the use of the GenClust algorithm in which the chromosomes used are 100% obtained through deterministic calculations. The results of this study resulted in performance comparisons expressed in Mean Square Error influenced by centroid determination on K-Means method by using GenClust method, modified GenClust method and also classic K-Means.
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Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.
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Ren, Zhouzheng; Jiang, Shizhen; Zeng, Qiufeng; Ding, Xuemei; Bai, Shiping; Wang, Jianping; Luo, Yuheng; Su, Zhuowei; Xuan, Yue; Yao, Bing; Cisneros, Fernando; Zhang, Keying
2016-01-01
Dietary canthaxanthin (CX), 25-hydroxycholecalciferol (25-OH-D 3 ) and vitamins have been widely reported to be involved in productive and reproductive performance of broiler breeders. However, limited information is available for duck breeders. In this study, a total of 1,560 Cherry Valley SM3 duck breeder females and 312 males were used to assess if the addition of CX and 25-OH-D3 could increase the performance of duck breeders under two different dietary vitamin regimens. Four diets were used under a 2 × 2 factorial arrangement with 2 kinds of vitamin premixes (REGULAR and HIGH; HIGH premix had higher levels of all vitamins except K3 than REGULAR premix), and with or without the supplementation of the mixture of CX (6 mg/kg) and 25-OH-D3 (0.069 mg/kg). The ducks were fed ad libitum with pelleted diets based on corn-soybean meal from 38 to 77 wk of age. HIGH vitamin premix decreased malondialdehyde (MDA) level (P < 0.001) of egg yolk, increased hatchability of fertile eggs (P = 0.029), increased hatchability of total eggs (P = 0.029), and decreased serum protein carbonyl level (P = 0.037) of breeder males. The mixture of CX and 25-OH-D3 increased serum calcium of breeder females (P = 0.010), decreased the cracked egg rate (P = 0.001), increased the pigmentation of egg yolk (P < 0.001) and male bill (P < 0.001), and decreased MDA level of egg yolk (P < 0.001) and male serum (P = 0.034). Interactive effects were observed in cracked egg rate (P = 0.038), shell thickness (P = 0.011) and serum phosphorus (P = 0.026) of breeder females. HIGH vitamin premix together with the mixture of CX and 25-OH-D3 decreased cracked egg rate and increased shell thickness of duck breeders. Serum phosphorus was decreased in duck breeder females fed REGULAR vitamin premix without the addition of the CX and 25-OH-D3 mixture. Dietary HIGH vitamin premix increased antioxidant status of eggs and breeder males, and increased hatchability. The mixture of CX and 25-OH-D3 enhanced egg shell quality, and promoted pigmentation and antioxidant status of eggs and breeder males.
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Cloud computing-based TagSNP selection algorithm for human genome data.
Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling
2015-01-05
Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used.
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New optimization model for routing and spectrum assignment with nodes insecurity
NASA Astrophysics Data System (ADS)
Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli
2017-04-01
By adopting the orthogonal frequency division multiplexing technology, elastic optical networks can provide the flexible and variable bandwidth allocation to each connection request and get higher spectrum utilization. The routing and spectrum assignment problem in elastic optical network is a well-known NP-hard problem. In addition, information security has received worldwide attention. We combine these two problems to investigate the routing and spectrum assignment problem with the guaranteed security in elastic optical network, and establish a new optimization model to minimize the maximum index of the used frequency slots, which is used to determine an optimal routing and spectrum assignment schemes. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. The heuristic algorithm is first used to sort the connection requests and then the genetic algorithm is designed to look for an optimal routing and spectrum assignment scheme. In the genetic algorithm, tailor-made crossover, mutation and local search operators are designed. Moreover, simulation experiments are conducted with three heuristic strategies, and the experimental results indicate that the effectiveness of the proposed model and algorithm framework.
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The Applications of Genetic Algorithms in Medicine.
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-11-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.].
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The Applications of Genetic Algorithms in Medicine
Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin
2015-01-01
A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060
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Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data
Hung, Che-Lun; Chen, Wen-Pei; Hua, Guan-Jie; Zheng, Huiru; Tsai, Suh-Jen Jane; Lin, Yaw-Ling
2015-01-01
Single nucleotide polymorphisms (SNPs) play a fundamental role in human genetic variation and are used in medical diagnostics, phylogeny construction, and drug design. They provide the highest-resolution genetic fingerprint for identifying disease associations and human features. Haplotypes are regions of linked genetic variants that are closely spaced on the genome and tend to be inherited together. Genetics research has revealed SNPs within certain haplotype blocks that introduce few distinct common haplotypes into most of the population. Haplotype block structures are used in association-based methods to map disease genes. In this paper, we propose an efficient algorithm for identifying haplotype blocks in the genome. In chromosomal haplotype data retrieved from the HapMap project website, the proposed algorithm identified longer haplotype blocks than an existing algorithm. To enhance its performance, we extended the proposed algorithm into a parallel algorithm that copies data in parallel via the Hadoop MapReduce framework. The proposed MapReduce-paralleled combinatorial algorithm performed well on real-world data obtained from the HapMap dataset; the improvement in computational efficiency was proportional to the number of processors used. PMID:25569088
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Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization
NASA Technical Reports Server (NTRS)
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.
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A genetic algorithm for replica server placement
NASA Astrophysics Data System (ADS)
Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl
2012-01-01
Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.
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A genetic algorithm for replica server placement
NASA Astrophysics Data System (ADS)
Eslami, Ghazaleh; Toroghi Haghighat, Abolfazl
2011-12-01
Modern distribution systems use replication to improve communication delay experienced by their clients. Some techniques have been developed for web server replica placement. One of the previous studies was Greedy algorithm proposed by Qiu et al, that needs knowledge about network topology. In This paper, first we introduce a genetic algorithm for web server replica placement. Second, we compare our algorithm with Greedy algorithm proposed by Qiu et al, and Optimum algorithm. We found that our approach can achieve better results than Greedy algorithm proposed by Qiu et al but it's computational time is more than Greedy algorithm.
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Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk
2014-01-01
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.
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Spies, Annika; Korzun, Viktor; Bayles, Rosemary; Rajaraman, Jeyaraman; Himmelbach, Axel; Hedley, Pete E.; Schweizer, Patrick
2012-01-01
Race-non-specific, or quantitative, pathogen resistance is of high importance to plant breeders due to its expected durability. However, it is usually controlled by multiple quantitative trait loci (QTL) and therefore difficult to handle in practice. Knowing the genes that underlie race-non-specific resistance (NR) would allow its exploitation in a more targeted manner. Here, we performed an association-genetic study in a customized worldwide collection of spring barley accessions for candidate genes of race-NR to the powdery mildew fungus Blumeria graminis f. sp. hordei (Bgh) and combined data with results from QTL mapping as well as functional-genomics approaches. This led to the identification of 11 associated genes with converging evidence for an important role in race-NR in the presence of the Mlo gene for basal susceptibility. Outstanding in this respect was the gene encoding the transcription factor WRKY2. The results suggest that unlocking plant genetic resources and integrating functional-genomic with genetic approaches can accelerate the discovery of genes underlying race-NR in barley and other crop plants. PMID:22629270
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Truss Optimization for a Manned Nuclear Electric Space Vehicle using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Benford, Andrew; Tinker, Michael L.
2004-01-01
The purpose of this paper is to utilize the genetic algorithm (GA) optimization method for structural design of a nuclear propulsion vehicle. Genetic algorithms provide a guided, random search technique that mirrors biological adaptation. To verify the GA capabilities, other traditional optimization methods were used to generate results for comparison to the GA results, first for simple two-dimensional structures, and then for full-scale three-dimensional truss designs.
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Superscattering of light optimized by a genetic algorithm
NASA Astrophysics Data System (ADS)
Mirzaei, Ali; Miroshnichenko, Andrey E.; Shadrivov, Ilya V.; Kivshar, Yuri S.
2014-07-01
We analyse scattering of light from multi-layer plasmonic nanowires and employ a genetic algorithm for optimizing the scattering cross section. We apply the mode-expansion method using experimental data for material parameters to demonstrate that our genetic algorithm allows designing realistic core-shell nanostructures with the superscattering effect achieved at any desired wavelength. This approach can be employed for optimizing both superscattering and cloaking at different wavelengths in the visible spectral range.
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A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm
Lee, Wah Ching; Tsang, Kim Fung; Chi, Hao Ran; Hung, Faan Hei; Wu, Chung Kit; Chui, Kwok Tai; Lau, Wing Hong; Leung, Yat Wah
2015-01-01
A high fuel efficiency management scheme for plug-in hybrid electric vehicles (PHEVs) has been developed. In order to achieve fuel consumption reduction, an adaptive genetic algorithm scheme has been designed to adaptively manage the energy resource usage. The objective function of the genetic algorithm is implemented by designing a fuzzy logic controller which closely monitors and resembles the driving conditions and environment of PHEVs, thus trading off between petrol versus electricity for optimal driving efficiency. Comparison between calculated results and publicized data shows that the achieved efficiency of the fuzzified genetic algorithm is better by 10% than existing schemes. The developed scheme, if fully adopted, would help reduce over 600 tons of CO2 emissions worldwide every day. PMID:25587974
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Neural-network-assisted genetic algorithm applied to silicon clusters
NASA Astrophysics Data System (ADS)
Marim, L. R.; Lemes, M. R.; dal Pino, A.
2003-03-01
Recently, a new optimization procedure that combines the power of artificial neural-networks with the versatility of the genetic algorithm (GA) was introduced. This method, called neural-network-assisted genetic algorithm (NAGA), uses a neural network to restrict the search space and it is expected to speed up the solution of global optimization problems if some previous information is available. In this paper, we have tested NAGA to determine the ground-state geometry of Sin (10⩽n⩽15) according to a tight-binding total-energy method. Our results indicate that NAGA was able to find the desired global minimum of the potential energy for all the test cases and it was at least ten times faster than pure genetic algorithm.
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Ahonen, Saija; Seath, Ian; Rusbridge, Clare; Holt, Susan; Key, Gill; Wang, Travis; Wang, Peixiang; Minassian, Berge A
2018-01-01
Canine DNA-testing has become an important tool in purebred dog breeding and many breeders use genetic testing results when planning their breeding strategies. In addition, information obtained from testing of hundreds dogs in one breed gives valuable information about the breed-wide genotype frequency of disease associated allele. Lafora disease is a late onset, recessively inherited genetic disease which is diagnosed in Miniature Wirehaired Dachshunds (MWHD). It is one of the most severe forms of canine epilepsy leading to neurodegeneration and, frequently euthanasia within a few years of diagnosis. Canine Lafora disease is caused by a dodecamer repeat expansion mutation in the NHLRC1 gene and a DNA test is available to identify homozygous dogs at risk, carriers and dogs free of the mutation. Blood samples were collected from 733 MWHDs worldwide, mostly of UK origin, for canine Lafora disease testing. Among the tested MWHD population 7.0% were homozygous for the mutation and at risk for Lafora disease. In addition, 234 dogs were heterozygous, indicating a carrier frequency of 31.9% in the tested population. Among the tested MWHDs, the mutant allele frequency was 0.2. In addition, data from the tested dogs over 6 years (2012-2017) indicated that the frequency of the homozygous and carrier dogs has decreased from 10.4% to 2.7% and 41.5% to 25.7%, respectively among MWHDs tested. As a consequence, the frequency of dogs free of the mutation has increased from 48.1% to 71.6%. This study provides valuable data for the MWHD community and shows that the DNA test is a useful tool for the breeders to prevent occurrence of Lafora disease in MWHDs. DNA testing has, over 6 years, helped to decrease the frequency of carriers and dogs at risk. Additionally, the DNA test can continue to be used to slowly eradicate the disease-causing mutation in the breed. However, this should be done carefully, over time, to avoid further compromising the genetic diversity of the breed. The DNA test also provides a diagnostic tool for veterinarians if they are presented with a dog that shows clinical signs associated with canine Lafora disease.
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Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.
ERIC Educational Resources Information Center
Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand
2003-01-01
Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…
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Characterization of uncertainty and sensitivity of model parameters is an essential and often overlooked facet of hydrological modeling. This paper introduces an algorithm called MOESHA that combines input parameter sensitivity analyses with a genetic algorithm calibration routin...
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Age, sex and social influences on adult survival in the cooperatively breeding Karoo Scrub-robin
Lloyd, Penn; Martin, Thomas E.; Taylor, Andrew; Braae, Anne; Altwegg, Res
2016-01-01
Among cooperatively breeding species, helpers are hypothesised to increase the survival of breeders by reducing breeder workload in offspring care and increased group vigilance against predators. Furthermore, parental nepotism or other benefits of group living may provide a survival benefit to young that delay dispersal to help. We tested these hypotheses in the Karoo Scrub-robin (Cercotrichas coryphaeus), a long-lived, and facultative cooperatively breeding species in which male helpers make substantial contributions to the care of young. We found that annual breeder survival in the presence of helpers did not differ detectably from breeders without helpers or breeders that lost helpers. Furthermore, helpers did not gain a survival benefit from deferred breeding; apparent survival did not differ detectably between male helpers and male breeders followed from one year old. These results are consistent with other studies suggesting a lack of adult survival benefits among species where breeders do not substantially reduce workloads when helpers are present. They are also consistent with the hypothesis that males that delay dispersal make the ‘best of a bad job’ by helping on their natal territory to gain indirect fitness benefits when they are unable to obtain a territory vacancy nearby.
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Zöttl, Markus; Vullioud, Philippe; Goddard, Katy; Torrents-Ticó, Miquel; Gaynor, David; Bennett, Nigel C; Clutton-Brock, Tim
2018-05-02
In Damaraland mole-rats (Fukomys damarensis), non-breeding subordinates contribute to the care of offspring born to the breeding pair in their group by carrying and retrieving young to the nest. In social mole-rats and some cooperative breeders, dominant females show unusually high testosterone levels and it has been suggested that high testosterone levels may increase reproductive and aggressive behavior and reduce investment in allo-parental and parental care, generating age and state-dependent variation in behavior. Here we show that, in Damaraland mole-rats, allo-parental care in males and females is unaffected by experimental increases in testosterone levels. Pup carrying decreases with age of the non-breeding helper while the change in social status from non-breeder to breeder has contrasting effects in the two sexes. Female breeders were more likely than female non-breeders to carry pups but male breeders were less likely to carry pups than male non-breeders, increasing the sex bias in parental care compared to allo-parental care. Our results indicate that testosterone is unlikely to be an important regulator of allo-parental care in mole-rats. Copyright © 2018. Published by Elsevier Inc.
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Amiri, Reza; Sasani, Shahryar; Jalali-Honarmand, Saeid; Rasaei, Ali; Seifolahpour, Behnaz; Bahraminejad, Sohbat
2018-02-01
Genetic variation among 78 irrigated bread wheat genotypes was studied for their nutritional value and baking quality traits as well as some agronomic traits. The experiment was conducted in a randomized complete block design with three replicates under normal and terminal drought stress conditions in Kermanshah, Iran during 2012-2013 cropping season. The results of combined ANOVA indicated highly significant genotypic differences for all traits. All studied traits except grain yield, hectoliter weight and grain fiber content were significantly affected by genotype × environment interaction. Drought stress reduced grain yield, thousand kernel weight, gluten index, grain starch content and hectoliter weight and slightly promoted grain protein and fiber contents, falling number, total gluten and ratio of wet gluten to grain protein content. Grain yield by 31.66% and falling number by 9.20% attained the highest decrease and increase due to drought stress. There were negative and significant correlations among grain yield with grain protein and fiber contents under both conditions. Results of cluster analysis showed that newer genotypes had more grain yield and gluten index than older ones, but instead, they had the lower grain protein and fiber contents. It is thought that wheat breeders have bred cultivars with high grain yield, low protein content, and improved bread-making attributes during last seven decades. While older genotypes indicated significantly higher protein contents, and some of them had higher gluten index. We concluded from this study that it is imperative for breeders to pay more attention to improve qualitative traits coordinated to grain yield.
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Mason, Annaliese S; Zhang, Jing; Tollenaere, Reece; Vasquez Teuber, Paula; Dalton-Morgan, Jessica; Hu, Liyong; Yan, Guijun; Edwards, David; Redden, Robert; Batley, Jacqueline
2015-09-01
Germplasm collections provide an extremely valuable resource for breeders and researchers. However, misclassification of accessions by species often hinders the effective use of these collections. We propose that use of high-throughput genotyping tools can provide a fast, efficient and cost-effective way of confirming species in germplasm collections, as well as providing valuable genetic diversity data. We genotyped 180 Brassicaceae samples sourced from the Australian Grains Genebank across the recently released Illumina Infinium Brassica 60K SNP array. Of these, 76 were provided on the basis of suspected misclassification and another 104 were sourced independently from the germplasm collection. Presence of the A- and C-genomes combined with principle components analysis clearly separated Brassica rapa, B. oleracea, B. napus, B. carinata and B. juncea samples into distinct species groups. Several lines were further validated using chromosome counts. Overall, 18% of samples (32/180) were misclassified on the basis of species. Within these 180 samples, 23/76 (30%) supplied on the basis of suspected misclassification were misclassified, and 9/105 (9%) of the samples randomly sourced from the Australian Grains Genebank were misclassified. Surprisingly, several individuals were also found to be the product of interspecific hybridization events. The SNP (single nucleotide polymorphism) array proved effective at confirming species, and provided useful information related to genetic diversity. As similar genomic resources become available for different crops, high-throughput molecular genotyping will offer an efficient and cost-effective method to screen germplasm collections worldwide, facilitating more effective use of these valuable resources by breeders and researchers. © 2015 John Wiley & Sons Ltd.
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IgM antiavian antibodies in sera from patients with pigeon breeder's disease.
Martínez-Cordero, E; Aguilar León, D E; Retana, V N
2000-01-01
The authors' objective was to study the presence of IgM antiavian antibodies in sera from patients with pigeon breeder's disease. We studied 93 patients with interstitial lung disease admitted for the assessment of pigeon breeder's disease. Eighty sera from healthy donors with no history of bird contact and 47 asymptomatic pigeon breeders were included as controls. The presence of IgM, IgG, and IgA antiavian antibodies was detected by ELISA and Western blot using avian-pooled serum antigen. Fifty-three patients were classified as having definite pigeon breeder's disease, whereas 40 did not fulfill these diagnostic criteria. The levels of IgM antiavian-antibodies in pigeon breeder's disease by ELISA exceeded both the values of healthy subjects with no history of avian contact (P = 2.5 x 10(-8)) and the results of asymptomatic breeders (P = 0. 03). Positive IgA antiavian antibodies were the most frequent abnormalities in pigeon breeder's disease showing values over the reference levels of control groups that reach significant statistical differences. Both precipitin-positive and -negative samples demonstrated IgM reactivity. IgM antiavian antibodies were confirmed by Western blot. A relationship of IgM positive tests with a recent history of avian antigen exposure and acute disease was found. Additionally, the positive IgM group included patients having subacute and chronic lung disease. Antiavian antibodies have previously been considered of minor significance in hypersensitivity pneumonitis; nevertheless, recent studies support their use in clinical diagnosis. Although no specific laboratory tests can confirm the diagnosis in pigeon breeder's disease, IgM antiavian antibodies may be useful for detecting recent antigen exposure and the acute stage of the disease. Copyright 2000 Wiley-Liss, Inc.
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A genetic algorithm for solving supply chain network design model
NASA Astrophysics Data System (ADS)
Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.
2013-09-01
Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.
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Im, Chak Han; Park, Young-Hoon; Hammel, Kenneth E; Park, Bokyung; Kwon, Soon Wook; Ryu, Hojin; Ryu, Jae-San
2016-07-01
Breeding new strains with improved traits is a long-standing goal of mushroom breeders that can be expedited by marker-assisted selection (MAS). We constructed a genetic linkage map of Pleurotus eryngii based on segregation analysis of markers in postmeiotic monokaryons from KNR2312. In total, 256 loci comprising 226 simple sequence-repeat (SSR) markers, 2 mating-type factors, and 28 insertion/deletion (InDel) markers were mapped. The map consisted of 12 linkage groups (LGs) spanning 1047.8cM, with an average interval length of 4.09cM. Four independent populations (Pd3, Pd8, Pd14, and Pd15) derived from crossing between four monokaryons from KNR2532 as a tester strain and 98 monokaryons from KNR2312 were used to characterize quantitative trait loci (QTL) for nine traits such as yield, quality, cap color, and earliness. Using composite interval mapping (CIM), 71 QTLs explaining between 5.82% and 33.17% of the phenotypic variations were identified. Clusters of more than five QTLs for various traits were identified in three genomic regions, on LGs 1, 7 and 9. Regardless of the population, 6 of the 9 traits studied and 18 of the 71 QTLs found in this study were identified in the largest cluster, LG1, in the range from 65.4 to 110.4cM. The candidate genes for yield encoding transcription factor, signal transduction, mycelial growth and hydrolase are suggested by using manual and computational analysis of genome sequence corresponding to QTL region with the highest likelihood odds (LOD) for yield. The genetic map and the QTLs established in this study will help breeders and geneticists to develop selection markers for agronomically important characteristics of mushrooms and to identify the corresponding genes. Copyright © 2016 Elsevier Inc. All rights reserved.
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Morphological and genetic characterization of an emerging Azorean horse breed: the Terceira Pony
Lopes, Maria S.; Mendonça, Duarte; Rojer, Horst; Cabral, Verónica; Bettencourt, Sílvia X.; da Câmara Machado, Artur
2015-01-01
The Terceira Pony is a horse indigenous to Terceira Island in the Azores. These horses were very important during the colonization of the island. Due to their very balanced proportions and correct gaits, and with an average withers height of 1.28 m, the Terceira Pony is often confused with a miniature pure-bred Lusitano. This population was officially recognized as the fourth Portuguese equine breed by the national authorities in January, 2014. The aim of this study was to analyze the morphology and the genetic diversity by means of microsatellite markers of this emerging horse breed. The biometric data consisted of 28 body measurements and nine angles from 30 animals (11 sires, 19 dams). The Terceira Pony is now a recognized horse breed and is gaining in popularity amongst breeders and the younger riding classes. The information obtained from this study will be very useful for conservation and management purposes, including maximizing the breed’s genetic diversity, and solidifying the desirable phenotypic traits. PMID:25774165
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NASA Astrophysics Data System (ADS)
Yusupov, L. R.; Klochkova, K. V.; Simonova, L. A.
2017-09-01
The paper presents a methodology of modeling the chemical composition of the composite material via genetic algorithm for optimization of the manufacturing process of products. The paper presents algorithms of methods based on intelligent system of vermicular graphite iron design
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A multiobjective optimization algorithm is applied to a groundwater quality management problem involving remediation by pump-and-treat (PAT). The multiobjective optimization framework uses the niched Pareto genetic algorithm (NPGA) and is applied to simultaneously minimize the...
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Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense
2010-03-01
17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in...to alert the other agents and ensure trust in the system. This research presents an algorithm that tasks robots to meet the two specific goals of...problem is defined as a constraint satisfaction problem solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Both goals of
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Nuclear reactor composite fuel assembly
Burgess, Donn M.; Marr, Duane R.; Cappiello, Michael W.; Omberg, Ronald P.
1980-01-01
A core and composite fuel assembly for a liquid-cooled breeder nuclear reactor including a plurality of elongated coextending driver and breeder fuel elements arranged to form a generally polygonal bundle within a thin-walled duct. The breeder elements are larger in cross section than the driver elements, and each breeder element is laterally bounded by a number of the driver elements. Each driver element further includes structure for spacing the driver elements from adjacent fuel elements and, where adjacent, the thin-walled duct. A core made up of the fuel elements can advantageously include fissile fuel of only one enrichment, while varying the effective enrichment of any given assembly or core region, merely by varying the relative number and size of the driver and breeder elements.
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Application of genetic algorithm in modeling on-wafer inductors for up to 110 Ghz
NASA Astrophysics Data System (ADS)
Liu, Nianhong; Fu, Jun; Liu, Hui; Cui, Wenpu; Liu, Zhihong; Liu, Linlin; Zhou, Wei; Wang, Quan; Guo, Ao
2018-05-01
In this work, the genetic algorithm has been introducted into parameter extraction for on-wafer inductors for up to 110 GHz millimeter-wave operations, and nine independent parameters of the equivalent circuit model are optimized together. With the genetic algorithm, the model with the optimized parameters gives a better fitting accuracy than the preliminary parameters without optimization. Especially, the fitting accuracy of the Q value achieves a significant improvement after the optimization.
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Combinatorial Multiobjective Optimization Using Genetic Algorithms
NASA Technical Reports Server (NTRS)
Crossley, William A.; Martin. Eric T.
2002-01-01
The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.
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Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong
2017-11-20
A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.
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Robert, Alexandre; Paiva, Vitor H; Bolton, Mark; Jiguet, Frédéric; Bried, Joël
2012-08-01
Environmental variability, costs of reproduction, and heterogeneity in individual quality are three important sources of the temporal and interindividual variations in vital rates of wild populations. Based on an 18-year monitoring of an endangered, recently described, long-lived seabird, Monteiro's Storm-Petrel (Oceanodroma monteiroi), we designed multistate survival models to separate the effects of the reproductive cost (breeders vs. nonbreeders) and individual quality (successful vs. unsuccessful breeders) in relation to temporally variable demographic and oceanographic properties. The analysis revealed a gradient of individual quality from nonbreeders, to unsuccessful breeders, to successful breeders. The survival rates of unsuccessful breeders (0.90 +/- 0.023, mean +/- SE) tended to decrease in years of high average breeding success and were more sensitive to oceanographic variation than those of both (high-quality) successful breeders (0.97 +/- 0.015) and (low-quality) nonbreeders (0.83 +/- 0.028). Overall, our results indicate that reproductive costs act on individuals of intermediate quality and are mediated by environmental harshness.
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[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].
Yang, Ming; Zhou, Yinmin; Chen, Jialei; Yu, Minying; Shi, Xiufeng; Gu, Xijun
2009-10-01
To explore the feasibility of genetic algorithm (GA) on multiple objective blending technology for extractions of Cortex Fraxini. According to that the optimization objective was the combination of fingerprint similarity and the root-mean-square error of multiple key constituents, a new multiple objective optimization model of 10 batches extractions of Cortex Fraxini was built. The blending coefficient was obtained by genetic algorithm. The quality of 10 batches extractions of Cortex Fraxini that after blending was evaluated with the finger print similarity and root-mean-square error as indexes. The quality of 10 batches extractions of Cortex Fraxini that after blending was well improved. Comparing with the fingerprint of the control sample, the similarity was up, but the degree of variation is down. The relative deviation of the key constituents was less than 10%. It is proved that genetic algorithm works well on multiple objective blending technology for extractions of Cortex Fraxini. This method can be a reference to control the quality of extractions of Cortex Fraxini. Genetic algorithm in blending technology for extractions of Chinese medicines is advisable.
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A., Javadpour; A., Mohammadi
2016-01-01
Background Regarding the importance of right diagnosis in medical applications, various methods have been exploited for processing medical images solar. The method of segmentation is used to analyze anal to miscall structures in medical imaging. Objective This study describes a new method for brain Magnetic Resonance Image (MRI) segmentation via a novel algorithm based on genetic and regional growth. Methods Among medical imaging methods, brains MRI segmentation is important due to high contrast of non-intrusive soft tissue and high spatial resolution. Size variations of brain tissues are often accompanied by various diseases such as Alzheimer’s disease. As our knowledge about the relation between various brain diseases and deviation of brain anatomy increases, MRI segmentation is exploited as the first step in early diagnosis. In this paper, regional growth method and auto-mate selection of initial points by genetic algorithm is used to introduce a new method for MRI segmentation. Primary pixels and similarity criterion are automatically by genetic algorithms to maximize the accuracy and validity in image segmentation. Results By using genetic algorithms and defining the fixed function of image segmentation, the initial points for the algorithm were found. The proposed algorithms are applied to the images and results are manually selected by regional growth in which the initial points were compared. The results showed that the proposed algorithm could reduce segmentation error effectively. Conclusion The study concluded that the proposed algorithm could reduce segmentation error effectively and help us to diagnose brain diseases. PMID:27672629
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Ortuño, Francisco M; Valenzuela, Olga; Rojas, Fernando; Pomares, Hector; Florido, Javier P; Urquiza, Jose M; Rojas, Ignacio
2013-09-01
Multiple sequence alignments (MSAs) are widely used approaches in bioinformatics to carry out other tasks such as structure predictions, biological function analyses or phylogenetic modeling. However, current tools usually provide partially optimal alignments, as each one is focused on specific biological features. Thus, the same set of sequences can produce different alignments, above all when sequences are less similar. Consequently, researchers and biologists do not agree about which is the most suitable way to evaluate MSAs. Recent evaluations tend to use more complex scores including further biological features. Among them, 3D structures are increasingly being used to evaluate alignments. Because structures are more conserved in proteins than sequences, scores with structural information are better suited to evaluate more distant relationships between sequences. The proposed multiobjective algorithm, based on the non-dominated sorting genetic algorithm, aims to jointly optimize three objectives: STRIKE score, non-gaps percentage and totally conserved columns. It was significantly assessed on the BAliBASE benchmark according to the Kruskal-Wallis test (P < 0.01). This algorithm also outperforms other aligners, such as ClustalW, Multiple Sequence Alignment Genetic Algorithm (MSA-GA), PRRP, DIALIGN, Hidden Markov Model Training (HMMT), Pattern-Induced Multi-sequence Alignment (PIMA), MULTIALIGN, Sequence Alignment Genetic Algorithm (SAGA), PILEUP, Rubber Band Technique Genetic Algorithm (RBT-GA) and Vertical Decomposition Genetic Algorithm (VDGA), according to the Wilcoxon signed-rank test (P < 0.05), whereas it shows results not significantly different to 3D-COFFEE (P > 0.05) with the advantage of being able to use less structures. Structural information is included within the objective function to evaluate more accurately the obtained alignments. The source code is available at http://www.ugr.es/~fortuno/MOSAStrE/MO-SAStrE.zip.
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A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.
Lo, C C; Chang, W H
2000-01-01
The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.
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A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
NASA Technical Reports Server (NTRS)
Kobayashi, Takahisa; Simon, Donald L.
2001-01-01
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
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Genetic Algorithm Approaches for Actuator Placement
NASA Technical Reports Server (NTRS)
Crossley, William A.
2000-01-01
This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.
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A pipelined FPGA implementation of an encryption algorithm based on genetic algorithm
NASA Astrophysics Data System (ADS)
Thirer, Nonel
2013-05-01
With the evolution of digital data storage and exchange, it is essential to protect the confidential information from every unauthorized access. High performance encryption algorithms were developed and implemented by software and hardware. Also many methods to attack the cipher text were developed. In the last years, the genetic algorithm has gained much interest in cryptanalysis of cipher texts and also in encryption ciphers. This paper analyses the possibility to use the genetic algorithm as a multiple key sequence generator for an AES (Advanced Encryption Standard) cryptographic system, and also to use a three stages pipeline (with four main blocks: Input data, AES Core, Key generator, Output data) to provide a fast encryption and storage/transmission of a large amount of data.
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Gau, Rebecca D.; Merz, Ueli; Falloon, Richard E.; Brunner, Patrick C.
2013-01-01
Spongospora subterranea f. sp. subterranea (Sss) causes two diseases on potato (Solanum tuberosum), lesions on tubers and galls on roots, which are economically important worldwide. Knowledge of global genetic diversity and population structure of pathogens is essential for disease management including resistance breeding. A combination of microsatellite and DNA sequence data was used to investigate the structure and invasion history of Sss. South American populations (four countries, 132 samples) were consistently more diverse than those from all other regions (15 countries, 566 samples), in agreement with the hypothesis that Sss originated in South America where potato was domesticated. A substantial genetic differenciation was found between root and tuber-derived samples from South America. Estimates of past and recent gene flow suggested that Sss was probably introduced from South America into Europe. Subsequently, Europe is likely to have been the recent source of migrants of the pathogen, acting as a “bridgehead” for further global dissemination. Quarantine measures must continue to be focussed on maintaining low global genetic diversity and avoiding exchange of genetic material between the native and introduced regions. Nevertheless, the current low global genetic diversity of Sss allows potato breeders to select for resistance, which is likely to be durable. PMID:23840791
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New phenotypes for new breeding goals in pigs.
Merks, J W M; Mathur, P K; Knol, E F
2012-04-01
Pig breeders in the past have adopted their breeding goals according to the needs of the producers, processors and consumers and have made remarkable genetic improvements in the traits of interest. However, it is becoming more and more challenging to meet the market needs and expectations of consumers and in general of the citizens. In view of the current and future trends, the breeding goals have to include several additional traits and new phenotypes. These phenotypes include (a) vitality from birth to slaughter, (b) uniformity at different levels of production, (c) robustness, (d) welfare and health and (e) phenotypes to reduce carbon footprint. Advancements in management, genomics, statistical models and other technologies provide opportunities for recording these phenotypes. These new developments also provide opportunities for making effective use of the new phenotypes for faster genetic improvement to meet the newly adapted breeding goals.
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Image reconstruction through thin scattering media by simulated annealing algorithm
NASA Astrophysics Data System (ADS)
Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua
2018-07-01
An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.
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Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul
2005-01-01
An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.
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Genetic algorithm for neural networks optimization
NASA Astrophysics Data System (ADS)
Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta
2004-11-01
This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.
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Hybrid Architectures for Evolutionary Computing Algorithms
2008-01-01
other EC algorithms to FPGA Core Burns P1026/MAPLD 200532 Genetic Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based...on Parallel and Distributed Processing (IPPS/SPDP ), pp. 316-320, Proceedings. IEEE Computer Society 1998. [12] Scott, S. D. , Samal , A., and...Algorithm Hardware References S. Scott, A. Samal , and S. Seth, “HGA: A Hardware Based Genetic Algorithm”, Proceedings of the 1995 ACM Third
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Ozmutlu, H. Cenk
2014-01-01
We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204
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Bentzen, Rebecca L.; Powell, Abby N.
2015-01-01
Post-fledging dispersal and site fidelity are poorly understood, particularly for sea ducks that spend the majority of their annual cycle at sea. This is the first description of movements and their timing for first-year (juvenile) and second-year (subadult) King Eiders Somateria spectabilis in relation to their attendant females. We fitted satellite transmitters that operated for 2 years to 63 hatch-year birds and 17 attendant females at breeding areas in northern Alaska in 2006–2009. Our goals were to describe the spatio-temporal distribution of pre-breeding individuals and adult females that had been successful breeders. We also examined fidelity to wing moulting and wintering areas as well as natal philopatry. Juveniles did not appear to follow attendant adults, although they did winter in the same three general wintering areas, suggesting that genetic inheritance and social factors may have roles in the initial migration from the breeding area. Additionally, juveniles were more variable in the timing and duration of migration, moved longer distances during the winter, and were less faithful to moulting and wintering areas than adults, indicating that individual exploration and acquired navigational memory played a role in subsequent migrations. Most (75%) subadult females returned to natal areas, probably prospecting for future nesting sites, whereas subadult males were widely dispersed at sea. Timing and duration of moult migration and wing moult of adult females that were presumed to be successful breeders differed from those of unsuccessful breeders due to the extended time that the former spent on the breeding grounds. Temporal and spatial segregation of post-fledging King Eiders from adults has direct management implications in terms of resource development and population dynamics.
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Series Hybrid Electric Vehicle Power System Optimization Based on Genetic Algorithm
NASA Astrophysics Data System (ADS)
Zhu, Tianjun; Li, Bin; Zong, Changfu; Wu, Yang
2017-09-01
Hybrid electric vehicles (HEV), compared with conventional vehicles, have complex structures and more component parameters. If variables optimization designs are carried on all these parameters, it will increase the difficulty and the convergence of algorithm program, so this paper chooses the parameters which has a major influence on the vehicle fuel consumption to make it all work at maximum efficiency. First, HEV powertrain components modelling are built. Second, taking a tandem hybrid structure as an example, genetic algorithm is used in this paper to optimize fuel consumption and emissions. Simulation results in ADVISOR verify the feasibility of the proposed genetic optimization algorithm.
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Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.
Rani, R Ranjani; Ramyachitra, D
2016-12-01
Multiple sequence alignment (MSA) is a widespread approach in computational biology and bioinformatics. MSA deals with how the sequences of nucleotides and amino acids are sequenced with possible alignment and minimum number of gaps between them, which directs to the functional, evolutionary and structural relationships among the sequences. Still the computation of MSA is a challenging task to provide an efficient accuracy and statistically significant results of alignments. In this work, the Bacterial Foraging Optimization Algorithm was employed to align the biological sequences which resulted in a non-dominated optimal solution. It employs Multi-objective, such as: Maximization of Similarity, Non-gap percentage, Conserved blocks and Minimization of gap penalty. BAliBASE 3.0 benchmark database was utilized to examine the proposed algorithm against other methods In this paper, two algorithms have been proposed: Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC) and Bacterial Foraging Optimization Algorithm. It was found that Hybrid Genetic Algorithm with Artificial Bee Colony performed better than the existing optimization algorithms. But still the conserved blocks were not obtained using GA-ABC. Then BFO was used for the alignment and the conserved blocks were obtained. The proposed Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) was compared with widely used MSA methods Clustal Omega, Kalign, MUSCLE, MAFFT, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC). The final results show that the proposed MO-BFO algorithm yields better alignment than most widely used methods. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
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Automatic page layout using genetic algorithms for electronic albuming
NASA Astrophysics Data System (ADS)
Geigel, Joe; Loui, Alexander C. P.
2000-12-01
In this paper, we describe a flexible system for automatic page layout that makes use of genetic algorithms for albuming applications. The system is divided into two modules, a page creator module which is responsible for distributing images amongst various album pages, and an image placement module which positions images on individual pages. Final page layouts are specified in a textual form using XML for printing or viewing over the Internet. The system makes use of genetic algorithms, a class of search and optimization algorithms that are based on the concepts of biological evolution, for generating solutions with fitness based on graphic design preferences supplied by the user. The genetic page layout algorithm has been incorporated into a web-based prototype system for interactive page layout over the Internet. The prototype system is built using client-server architecture and is implemented in java. The system described in this paper has demonstrated the feasibility of using genetic algorithms for automated page layout in albuming and web-based imaging applications. We believe that the system adequately proves the validity of the concept, providing creative layouts in a reasonable number of iterations. By optimizing the layout parameters of the fitness function, we hope to further improve the quality of the final layout in terms of user preference and computation speed.
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NASA Astrophysics Data System (ADS)
Narwadi, Teguh; Subiyanto
2017-03-01
The Travelling Salesman Problem (TSP) is one of the best known NP-hard problems, which means that no exact algorithm to solve it in polynomial time. This paper present a new variant application genetic algorithm approach with a local search technique has been developed to solve the TSP. For the local search technique, an iterative hill climbing method has been used. The system is implemented on the Android OS because android is now widely used around the world and it is mobile system. It is also integrated with Google API that can to get the geographical location and the distance of the cities, and displays the route. Therefore, we do some experimentation to test the behavior of the application. To test the effectiveness of the application of hybrid genetic algorithm (HGA) is compare with the application of simple GA in 5 sample from the cities in Central Java, Indonesia with different numbers of cities. According to the experiment results obtained that in the average solution HGA shows in 5 tests out of 5 (100%) is better than simple GA. The results have shown that the hybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher complexity.
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NASA Astrophysics Data System (ADS)
Liu, Yan; Deng, Honggui; Ren, Shuang; Tang, Chengying; Qian, Xuewen
2018-01-01
We propose an efficient partial transmit sequence technique based on genetic algorithm and peak-value optimization algorithm (GAPOA) to reduce high peak-to-average power ratio (PAPR) in visible light communication systems based on orthogonal frequency division multiplexing (VLC-OFDM). By analysis of hill-climbing algorithm's pros and cons, we propose the POA with excellent local search ability to further process the signals whose PAPR is still over the threshold after processed by genetic algorithm (GA). To verify the effectiveness of the proposed technique and algorithm, we evaluate the PAPR performance and the bit error rate (BER) performance and compare them with partial transmit sequence (PTS) technique based on GA (GA-PTS), PTS technique based on genetic and hill-climbing algorithm (GH-PTS), and PTS based on shuffled frog leaping algorithm and hill-climbing algorithm (SFLAHC-PTS). The results show that our technique and algorithm have not only better PAPR performance but also lower computational complexity and BER than GA-PTS, GH-PTS, and SFLAHC-PTS technique.
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Goudie, Catherine; Coltin, Hallie; Witkowski, Leora; Mourad, Stephanie; Malkin, David; Foulkes, William D
2017-08-01
Identifying cancer predisposition syndromes in children with tumors is crucial, yet few clinical guidelines exist to identify children at high risk of having germline mutations. The McGill Interactive Pediatric OncoGenetic Guidelines project aims to create a validated pediatric guideline in the form of a smartphone/tablet application using algorithms to process clinical data and help determine whether to refer a child for genetic assessment. This paper discusses the initial stages of the project, focusing on its overall structure, the methodology underpinning the algorithms, and the upcoming algorithm validation process. © 2017 Wiley Periodicals, Inc.
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A genetic test of the natal homing versus social facilitation models for green turtle migration.
Meylan, A B; Bowen, B W; Avise, J C
1990-05-11
Female green turtles exhibit strong nest-site fidelity as adults, but whether the nesting beach is the natal site is not known. Under the natal homing hypothesis, females return to their natal beach to nest, whereas under the social facilitation model, virgin females follow experienced breeders to nesting beaches and after a "favorable" nesting experience, fix on that site for future nestings. Differences shown in mitochondrial DNA genotype frequency among green turtle colonies in the Caribbean Sea and Atlantic Ocean are consistent with natal homing expectations and indicate that social facilitation to nonnatal sites is rare.
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Optimization of genomic selection training populations with a genetic algorithm
USDA-ARS?s Scientific Manuscript database
In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...
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Wijnrocx, Katrien; François, Liesbeth; Goos, Peter; Buys, Nadine; Janssens, Steven
2018-01-01
The selection of a future breeding dog is a complicated task, in which disease characteristics and different traits have to be combined and weighed against one another. Truncation selection, that is the exclusion of affected animals, may be very inefficient when selecting on a large number of traits, and may result in a reduction of the genetic diversity in a population or breed. Selection could be facilitated by the use of a selection index that combines multiple traits or breeding values into one score. This however requires a consideration of their relative value according to their economic weight, which is difficult to express in monetary units for health traits. The use of a choice experiment to derive non-market values might be a solution to this problem. This is a pilot study to assess the potential use of choice experiments to ascertain the public preference and relative importance attached to health- and conformation traits in the selection of a Cavalier King Charles spaniel. The focus was on two prevalent disorders, mitral valve disease and syringomyelia, and on several important conformation traits such as muzzle length and eye shape. Based on available prior information, a Bayesian D-optimal design approach was used to develop a choice experiment and the resulting choice sets. Every participant (breeder or owner) in the choice experiment was presented with a total of 17 choice sets, in which at most four traits could vary to reduce the cognitive burden. A total of 114 respondents participated in the choice experiment and results showed that respondents (breeders/owners) current attitudes were directed towards health (syringomyelia and mitral valve disease), followed by eye shape and level of inbreeding. This approach identifies the value breeders and owners attach to certain traits in the breeding objective. The resulting relative weights, represented as the logworths obtained from the choice experiment, could be an alternative to economic weights. They could be implemented as a weight when breeding values are available, but more study on this topic will be necessary. A challenge in this approach is to scale up the experiment with additional traits. Moreover, for other traits, the genetic parameters and correlations should be known first, in order to include them in the health selection index as well.
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Wasson, Anton P.; Chiu, Grace S.; Zwart, Alexander B.; Binns, Timothy R.
2017-01-01
Ensuring future food security for a growing population while climate change and urban sprawl put pressure on agricultural land will require sustainable intensification of current farming practices. For the crop breeder this means producing higher crop yields with less resources due to greater environmental stresses. While easy gains in crop yield have been made mostly “above ground,” little progress has been made “below ground”; and yet it is these root system traits that can improve productivity and resistance to drought stress. Wheat pre-breeders use soil coring and core-break counts to phenotype root architecture traits, with data collected on rooting density for hundreds of genotypes in small increments of depth. The measured densities are both large datasets and highly variable even within the same genotype, hence, any rigorous, comprehensive statistical analysis of such complex field data would be technically challenging. Traditionally, most attributes of the field data are therefore discarded in favor of simple numerical summary descriptors which retain much of the high variability exhibited by the raw data. This poses practical challenges: although plant scientists have established that root traits do drive resource capture in crops, traits that are more randomly (rather than genetically) determined are difficult to breed for. In this paper we develop a hierarchical nonlinear mixed modeling approach that utilizes the complete field data for wheat genotypes to fit, under the Bayesian paradigm, an “idealized” relative intensity function for the root distribution over depth. Our approach was used to determine heritability: how much of the variation between field samples was purely random vs. being mechanistically driven by the plant genetics? Based on the genotypic intensity functions, the overall heritability estimate was 0.62 (95% Bayesian confidence interval was 0.52 to 0.71). Despite root count profiles that were statistically very noisy, our approach led to denoised profiles which exhibited rigorously discernible phenotypic traits. Profile-specific traits could be representative of a genotype, and thus, used as a quantitative tool to associate phenotypic traits with specific genotypes. This would allow breeders to select for whole root system distributions appropriate for sustainable intensification, and inform policy for mitigating crop yield risk and food insecurity. PMID:28303148
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Duan, Xiaoxue; Li, Feng; Mou, Shaoyang; Feng, Jiawei; Liu, Peifeng; Xu, Liangmei
2015-12-01
The effects of maternal L-arginine supplementation on laying performance and the antioxidant capacity of broiler breeder hens, egg yolk, and their one-day-old offspring were investigated. In a 9 wk experiment, 210 60-week-old Arbor Acres healthy female broiler breeders were randomly divided into 5 treatments with 6 replicates of 7 females and fed a corn and soybean meal diet with 5 arginine levels (0.96%, 1.16%, 1.36%, 1.56%, and 1.76% digestible arginine). Laying performance and anti-oxidant capacity of broiler breeder hens, eggs, and offspring were evaluated. Digestible arginine level in the broiler breeder diet had a significant effect on the laying rate (linear and quadratic effect, P<0.0001). The highest laying rate was obtained when the diet with 1.36% digestible arginine was fed. There was a significant effect of digestible arginine level in the broiler breeder diet on the total antioxidant capacity (T-AOC) levels and methane dicarboxylic aldehyde (MDA) concentration in the broiler breeder serum, egg yolk and serum, and liver and breast of one-day-old offspring (linear and quadratic effect, P<0.05). The T-AOC level was highest and the MDA concentration lowest in all tissues when a diet with 1.36% digestible arginine was fed. No difference in glutathione peroxidase (GSH-PX) activity in the broiler breeder serum was observed. There were significant effects of digestible arginine level in the broiler breeder diet on the GSH-PX activity of the egg yolk (linear effect, P<0.01; quadratic effect, P<0.05) and serum, liver, and breast of one-day-old offspring (linear and quadratic effect, P≤0.01). The GSH-PX activity in all tissues measured in this experiment was highest when the dietary digestible arginine was 1.36%. These results indicate that the diet with 1.36% digestible arginine (1,972 mg/d) is optimal to satisfy the nutritional needs of a female broiler breeder during the late laying period.
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The effect of low-density broiler breeder diets on performance and immune status of their offspring.
Enting, H; Boersma, W J A; Cornelissen, J B W J; van Winden, S C L; Verstegen, M W A; van der Aar, P J
2007-02-01
Effects of low-density broiler breeder diets on offspring performance and mortality were studied using 2,100 female and 210 male Cobb 500 breeders. Breeder treatments involved 4 experimental groups and a control group with normal density diets (ND, 2,600 kcal of AME/kg during rearing and 2,800 kcal of AME/kg during laying). In treatment 2, nutrient densities were decreased by 12% (LD12) and 11% (LD11) during the rearing and laying periods, respectively, whereas in treatment 3, nutrient densities were decreased by 23% (LD23) and 21% (LD21) during the rearing and laying periods, respectively. The nutrient density in these treatments was decreased through inclusion of palm kernel meal, wheat bran, wheat gluten feed, and sunflower seed meal in the diets. Treatment 4 included diets with the same nutrient densities as in treatment 2 but included oats and sugar beet pulp (LD12(OP) and LD11(OP)). In treatment 5, the same low-density diet was given to the breeders as in treatment 2 during the rearing period, but it was followed by a normal density diet during the laying period (LD12-ND). Treatments were applied from 4 to 60 wk of age. On low-density diets, offspring showed an increased 1-d-old weight. As compared with offspring of breeders that received ND, the d 38 live weight of chickens from 29-wk-old breeders fed LD11 was improved. Mortality was reduced in offspring from 60-wk-old parent stock given low-density diets. The IgM titers in 35-d-old offspring from eggs with a lower-than-average weight were reduced when 29-wk-old broiler breeders were fed low-density diets. In offspring from eggs with a higher-than-average weight from 60-wk-old parent stock given LD11 or LD21 diets, IgM titers were higher compared with ND. It was concluded that low-density broiler breeder diets can improve offspring growth rates, reduce mortality, and reduce or increase immune responses, depending on breeder age and egg weight.
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A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification
NASA Astrophysics Data System (ADS)
Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.
MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.
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Fireworks algorithm for mean-VaR/CVaR models
NASA Astrophysics Data System (ADS)
Zhang, Tingting; Liu, Zhifeng
2017-10-01
Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.
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Dynamic traffic assignment : genetic algorithms approach
DOT National Transportation Integrated Search
1997-01-01
Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...
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NASA Technical Reports Server (NTRS)
Peck, Charles C.; Dhawan, Atam P.; Meyer, Claudia M.
1991-01-01
A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
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NASA Astrophysics Data System (ADS)
Ebrahimi, Mehdi; Jahangirian, Alireza
2017-12-01
An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.
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Sun, J; Wang, T; Li, Z D; Shao, Y; Zhang, Z Y; Feng, H; Zou, D H; Chen, Y J
2017-12-01
To reconstruct a vehicle-bicycle-cyclist crash accident and analyse the injuries using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, and to provide biomechanical basis for the forensic identification of death cause. The vehicle was measured by 3D laser scanning technology. The multi-rigid-body models of cyclist, bicycle and vehicle were developed based on the measurements. The value range of optimal variables was set. A multi-objective genetic algorithm and the nondominated sorting genetic algorithm were used to find the optimal solutions, which were compared to the record of the surveillance video around the accident scene. The reconstruction result of laser scanning on vehicle was satisfactory. In the optimal solutions found by optimization method of genetic algorithm, the dynamical behaviours of dummy, bicycle and vehicle corresponded to that recorded by the surveillance video. The injury parameters of dummy were consistent with the situation and position of the real injuries on the cyclist in accident. The motion status before accident, damage process by crash and mechanical analysis on the injury of the victim can be reconstructed using 3D laser scanning technology, multi-rigid-body dynamics and optimized genetic algorithm, which have application value in the identification of injury manner and analysis of death cause in traffic accidents. Copyright© by the Editorial Department of Journal of Forensic Medicine
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NASA Astrophysics Data System (ADS)
Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.
2018-03-01
Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.
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Research on laser marking speed optimization by using genetic algorithm.
Wang, Dongyun; Yu, Qiwei; Zhang, Yu
2015-01-01
Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%.
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Tag SNP selection via a genetic algorithm.
Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh
2010-10-01
Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.
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Unexpected consequences of genetic selection in broilers and turkeys: problems and solutions.
Hocking, P M
2014-02-01
1. Genetic theory leads to the expectation that unexpected consequences of genetic selection for production traits will inevitably occur and that these changes are likely to be undesirable. 2. Both artificial selection for production efficiency and "natural" selection for adaptation to the production environment result in selection sweeps that increase the frequencies of rare recessive alleles that have a negative effect on fitness. 3. Fitness is broadly defined as any trait that affects the ability to survive, reproduce and contribute to the next generation, such as musculoskeletal disease in growing broiler chickens and multiple ovulation in adult broiler parents. 4. Welfare concerns about the negative effects of genetic selection on bird welfare are sometimes exaggerated but are nevertheless real. Breeders have paid increasing attention to these traits over several decades and have demonstrated improvement in pedigree flocks. There is an urgent need to monitor changes in commercial flocks to ensure that genetic change is accompanied by improvements in that target population. 5. New technologies for trait measurement, whole genome selection and targeted genetic modification hold out the promise of efficient and rapid improvement of welfare traits in future breeding of broiler chickens and turkeys. The potential of targeted genetic modification for enhancing welfare traits is considerable, but the goal of achieving public acceptability for the progeny of transgenic poultry will be politically challenging.
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Zhang, F; Ge, Y Y; Wang, W Y; Shen, X L; Yu, X Y
2012-12-03
Conventional hybridization and selection techniques have aided the development of new ornamental crop cultivars. However, little information is available on the genetic divergence of bromeliad hybrids. In the present study, we investigated the genetic variability in interspecific hybrids of Aechmea gomosepala and A. recurvata var. recurvata using inflorescence characteristics and sequence-related amplified polymorphism (SRAP) markers. The morphological analysis showed that the putative hybrids were intermediate between both parental species with respect to inflorescence characteristics. The 16 SRAP primer combinations yield 265 bands, among which 154 (57.72%) were polymorphic. The genetic similarity was an average of 0.59 and ranged from 0.21 to 0.87, indicating moderate genetic divergence among the hybrids. The unweighted pair group method with arithmetic average (UPGMA)-based cluster analysis distinguished the hybrids from their parents with a genetic distance coefficient of 0.54. The cophenetic correlation was 0.93, indicating a good fit between the dendrogram and the original distance matrix. The two-dimensional plot from the principal coordinate analysis showed that the hybrids were intermediately dispersed between both parents, corresponding to the results of the UPGMA cluster and the morphological analysis. These results suggest that SRAP markers could help to identify breeders, characterize F(1) hybrids of bromeliads at an early stage, and expedite genetic improvement of bromeliad cultivars.
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Wood, Jacquelyn L A; Tezel, Defne; Joyal, Destin; Fraser, Dylan J
2015-09-01
How population size influences quantitative genetic variation and differentiation among natural, fragmented populations remains unresolved. Small, isolated populations might occupy poor quality habitats and lose genetic variation more rapidly due to genetic drift than large populations. Genetic drift might furthermore overcome selection as population size decreases. Collectively, this might result in directional changes in additive genetic variation (VA ) and trait differentiation (QST ) from small to large population size. Alternatively, small populations might exhibit larger variation in VA and QST if habitat fragmentation increases variability in habitat types. We explored these alternatives by investigating VA and QST using nine fragmented populations of brook trout varying 50-fold in census size N (179-8416) and 10-fold in effective number of breeders, Nb (18-135). Across 15 traits, no evidence was found for consistent differences in VA and QST with population size and almost no evidence for increased variability of VA or QST estimates at small population size. This suggests that (i) small populations of some species may retain adaptive potential according to commonly adopted quantitative genetic measures and (ii) populations of varying sizes experience a variety of environmental conditions in nature, however extremely large studies are likely required before any firm conclusions can be made. © 2015 The Author(s). Evolution © 2015 The Society for the Study of Evolution.
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Research on rolling element bearing fault diagnosis based on genetic algorithm matching pursuit
NASA Astrophysics Data System (ADS)
Rong, R. W.; Ming, T. F.
2017-12-01
In order to solve the problem of slow computation speed, matching pursuit algorithm is applied to rolling bearing fault diagnosis, and the improvement are conducted from two aspects that are the construction of dictionary and the way to search for atoms. To be specific, Gabor function which can reflect time-frequency localization characteristic well is used to construct the dictionary, and the genetic algorithm to improve the searching speed. A time-frequency analysis method based on genetic algorithm matching pursuit (GAMP) algorithm is proposed. The way to set property parameters for the improvement of the decomposition results is studied. Simulation and experimental results illustrate that the weak fault feature of rolling bearing can be extracted effectively by this proposed method, at the same time, the computation speed increases obviously.
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Breeder Reactors, Understanding the Atom Series.
ERIC Educational Resources Information Center
Mitchell, Walter, III; Turner, Stanley E.
The theory of breeder reactors in relationship to a discussion of fission is presented. Different kinds of reactors are characterized by the cooling fluids used, such as liquid metal, gas, and molten salt. The historical development of breeder reactors over the past twenty-five years includes specific examples of reactors. The location and a brief…
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Food residue recycling by swine breeders in a developing economy: a case study in Da Nang, Viet Nam.
Kato, Takaaki; Pham, Dung Thi Xuan; Hoang, Hai; Xue, Yonghai; Tran, Quang Van
2012-12-01
This study provides a detailed description of food residue collection by swine breeders in Da Nang, Viet Nam. In January 2011, the study surveyed 30 swine breeders in two villages with respect to locations, methods, prices, quantities, and prospects for food residue collection. The sampled swine breeders regularly visited 55 locations in central Da Nang to collect raw food residue. They then transferred the food residue to their piggeries, boiled it, and fed it to their swine. A regression analysis revealed that the total amount of food residue collected by a farm depends on the number of swine in the farm and the number of collections made per day. Swine breeders in Da Nang were estimated to collect 26.3 metric tons of organic waste per day, which amounted to 4.1% of domestic waste collected by the local government. Among the sampled swine breeders, 93% answered that they would continue using food residue for the next five years. Copyright © 2012 Elsevier Ltd. All rights reserved.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Tumuluru, Jaya Shankar; McCulloch, Richard Chet James
In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the mostmore » improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.« less
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Automated Test Assembly for Cognitive Diagnosis Models Using a Genetic Algorithm
ERIC Educational Resources Information Center
Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A.
2009-01-01
Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…
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ERIC Educational Resources Information Center
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
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Rausch, Tobias; Thomas, Alun; Camp, Nicola J.; Cannon-Albright, Lisa A.; Facelli, Julio C.
2008-01-01
This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise. PMID:18547558
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A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.
Sun, Tao; Xu, Ming-Hai
2017-01-01
Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.
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Comparative evaluation of solar, fission, fusion, and fossil energy resources, part 3
NASA Technical Reports Server (NTRS)
Clement, J. D.; Reupke, W. A.
1974-01-01
The role of nuclear fission reactors in becoming an important power source in the world is discussed. The supply of fissile nuclear fuel will be severely depleted by the year 2000. With breeder reactors the world supply of uranium could last thousands of years. However, breeder reactors have problems of a large radioactive inventory and an accident potential which could present an unacceptable hazard. Although breeder reactors afford a possible solution to the energy shortage, their ultimate role will depend on demonstrated safety and acceptable risks and environmental effects. Fusion power would also be a long range, essentially permanent, solution to the world's energy problem. Fusion appears to compare favorably with breeders in safety and environmental effects. Research comparing a controlled fusion reactor with the breeder reactor in solving our long range energy needs is discussed.
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Liu, Chun; Kroll, Andreas
2016-01-01
Multi-robot task allocation determines the task sequence and distribution for a group of robots in multi-robot systems, which is one of constrained combinatorial optimization problems and more complex in case of cooperative tasks because they introduce additional spatial and temporal constraints. To solve multi-robot task allocation problems with cooperative tasks efficiently, a subpopulation-based genetic algorithm, a crossover-free genetic algorithm employing mutation operators and elitism selection in each subpopulation, is developed in this paper. Moreover, the impact of mutation operators (swap, insertion, inversion, displacement, and their various combinations) is analyzed when solving several industrial plant inspection problems. The experimental results show that: (1) the proposed genetic algorithm can obtain better solutions than the tested binary tournament genetic algorithm with partially mapped crossover; (2) inversion mutation performs better than other tested mutation operators when solving problems without cooperative tasks, and the swap-inversion combination performs better than other tested mutation operators/combinations when solving problems with cooperative tasks. As it is difficult to produce all desired effects with a single mutation operator, using multiple mutation operators (including both inversion and swap) is suggested when solving similar combinatorial optimization problems.
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Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. PMID:25097872
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Dong, Yu-Shuang; Xu, Gao-Chao; Fu, Xiao-Dong
2014-01-01
The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Not Available
Three solid-breeder water-cooled blanket concepts have been developed for ITER based on a multilayer configuration. The primary difference among the concepts is in the fabricated form of breeder and multiplier. All the concepts have beryllium for neutron multiplication and solid-breeder temperature control. The blanket design does not use helium gaps or insulator material to control the solid breeder temperature. Lithium oxide (Li{sub 2}O) and lithium zirconate (Li{sub 2}ZrO{sub 3}) are the primary and the backup breeder materials, respectively. The lithium-6 enrichment is 95%. The use of high lithium-6 enrichment reduces the solid breeder volume required in the blanket and consequentlymore » the total tritium inventory in the solid breeder material. Also, it increases the blanket capability to accommodate power variation. The multilayer blanket configuration can accommodate up to a factor of two change in the neutron wall loading without violating the different design guidelines. The blanket material forms are sintered products and packed bed of small pebbles. The first concept has a sintered product material (blocks) for both the beryllium multiplier and the solid breeder. The second concept, the common ITER blanket, uses a packed bed breeder and beryllium blocks. The last concept is similar to the first except for the first and the last beryllium zones. Two small layers of beryllium pebbles are located behind the first wall and the back of the last beryllium zone to reduce the total inventory of the beryllium material and to improve the blanket performance. The design philosophy adopted for the blanket is to produce the necessary tritium required for the ITER operation and to operate at power reactor conditions as much as possible. Also, the reliability and the safety aspects of the blanket are enhanced by using low-pressure water coolant and the separation of the tritium purge flow from the coolant system by several barriers.« less
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Garcia Montero, Angelica; Vole, Christiane; Burda, Hynek; Malkemper, Erich Pascal; Holtze, Susanne; Morhart, Michaela; Saragusty, Joseph; Hildebrandt, Thomas B.; Begall, Sabine
2016-01-01
Ansell’s mole-rats (Fukomys anselli) are subterranean rodents living in families composed of about 20 members with a single breeding pair and their non-breeding offspring. Most of them remain with their parents for their lifetime and help to maintain and defend the natal burrow system, forage, and care for younger siblings. Since incest avoidance is based on individual recognition (and not on social suppression) we expect that non-breeders produce viable sperm spontaneously. We compared the sperm of breeding and non-breeding males, obtained by electroejaculation and found no significant differences in sperm parameters between both groups. Here, we used electroejaculation to obtain semen for the first time in a subterranean mammal. Spermiogram analysis revealed no significant differences in sperm parameters between breeders and non-breeders. We found significantly larger testes (measured on autopsies and on living animals per ultrasonography) of breeders compared to non-breeders (with body mass having a significant effect). There were no marked histological differences between breeding and non-breeding males, and the relative area occupied by Leydig cells and seminiferous tubules on histological sections, respectively, was not significantly different between both groups. The seminiferous epithelium and to a lesser degree the interstitial testicular tissue are characterized by lesions (vacuolar degenerations), however, this feature does not hinder fertilization even in advanced stages of life. The continuous production of viable sperm also in sexually abstinent non-breeders might be best understood in light of the mating and social system of Fukomys anselli, and the potential to found a new family following an unpredictable and rare encounter with an unfamiliar female (“provoked or induced dispersal”). Apparently, the non-breeders do not reproduce because they do not copulate but not because they would be physiologically infertile. The significantly increased testes volume of breeding males (compared to non-breeders) is in agreement with previously found higher testosterone levels of breeders. PMID:26934488
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Genetic algorithm for nuclear data evaluation
DOE Office of Scientific and Technical Information (OSTI.GOV)
Arthur, Jennifer Ann
These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.
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Carter's breeder policy has failed, claims Westinghouse manager
DOE Office of Scientific and Technical Information (OSTI.GOV)
Not Available
1979-07-01
Nuclear nations developing liquid metal fast breeder reactor (LMFBR) technology have not been dissuaded by President Carter's efforts to stop the breeder program as a way to control the proliferation of nuclear weapons. There is no evidence that Carter's policy of moral persuasion has had any impact on their efforts. A review of the eight leading countries cites their extensive progress in the areas of breeder technology and fuel reprocessing, while the US has made only slight gains. The Fast Flux Test Facility at Hanford is near completion, but the Clinch River project has been slowed to a minimum.
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Genetic Characterization of Dog Personality Traits.
Ilska, Joanna; Haskell, Marie J; Blott, Sarah C; Sánchez-Molano, Enrique; Polgar, Zita; Lofgren, Sarah E; Clements, Dylan N; Wiener, Pamela
2017-06-01
The genetic architecture of behavioral traits in dogs is of great interest to owners, breeders, and professionals involved in animal welfare, as well as to scientists studying the genetics of animal (including human) behavior. The genetic component of dog behavior is supported by between-breed differences and some evidence of within-breed variation. However, it is a challenge to gather sufficiently large datasets to dissect the genetic basis of complex traits such as behavior, which are both time-consuming and logistically difficult to measure, and known to be influenced by nongenetic factors. In this study, we exploited the knowledge that owners have of their dogs to generate a large dataset of personality traits in Labrador Retrievers. While accounting for key environmental factors, we demonstrate that genetic variance can be detected for dog personality traits assessed using questionnaire data. We identified substantial genetic variance for several traits, including fetching tendency and fear of loud noises, while other traits revealed negligibly small heritabilities. Genetic correlations were also estimated between traits; however, due to fairly large SEs, only a handful of trait pairs yielded statistically significant estimates. Genomic analyses indicated that these traits are mainly polygenic, such that individual genomic regions have small effects, and suggested chromosomal associations for six of the traits. The polygenic nature of these traits is consistent with previous behavioral genetics studies in other species, for example in mouse, and confirms that large datasets are required to quantify the genetic variance and to identify the individual genes that influence behavioral traits. Copyright © 2017 by the Genetics Society of America.
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Fernandez-Lozano, C.; Canto, C.; Gestal, M.; Andrade-Garda, J. M.; Rabuñal, J. R.; Dorado, J.; Pazos, A.
2013-01-01
Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected. PMID:24453933
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Fuel management optimization using genetic algorithms and expert knowledge
DOE Office of Scientific and Technical Information (OSTI.GOV)
DeChaine, M.D.; Feltus, M.A.
1996-09-01
The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.
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Optimal placement of tuning masses on truss structures by genetic algorithms
NASA Technical Reports Server (NTRS)
Ponslet, Eric; Haftka, Raphael T.; Cudney, Harley H.
1993-01-01
Optimal placement of tuning masses, actuators and other peripherals on large space structures is a combinatorial optimization problem. This paper surveys several techniques for solving this problem. The genetic algorithm approach to the solution of the placement problem is described in detail. An example of minimizing the difference between the two lowest frequencies of a laboratory truss by adding tuning masses is used for demonstrating some of the advantages of genetic algorithms. The relative efficiencies of different codings are compared using the results of a large number of optimization runs.
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2008-06-01
postponed the fulfillment of her own Masters Degree by at least 18 months so that I would have the opportunity to earn mine. She is smart , lovely...GENETIC ALGORITHM AND MULTI AGENT SYSTEM TO EXPLORE EMERGENT PATTERNS OF SOCIAL RATIONALITY AND A DISTRESS-BASED MODEL FOR DECEIT IN THE WORKPLACE...of a Genetic Algorithm and Mutli Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the
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Multi-objective Optimization Design of Gear Reducer Based on Adaptive Genetic Algorithms
NASA Astrophysics Data System (ADS)
Li, Rui; Chang, Tian; Wang, Jianwei; Wei, Xiaopeng; Wang, Jinming
2008-11-01
An adaptive Genetic Algorithm (GA) is introduced to solve the multi-objective optimized design of the reducer. Firstly, according to the structure, strength, etc. in a reducer, a multi-objective optimized model of the helical gear reducer is established. And then an adaptive GA based on a fuzzy controller is introduced, aiming at the characteristics of multi-objective, multi-parameter, multi-constraint conditions. Finally, a numerical example is illustrated to show the advantages of this approach and the effectiveness of an adaptive genetic algorithm used in optimized design of a reducer.
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Risk of hypersensitivity pneumonitis and interstitial lung diseases among pigeon breeders.
Cramer, Christine; Schlünssen, Vivi; Bendstrup, Elisabeth; Stokholm, Zara Ann; Vestergaard, Jesper Medom; Frydenberg, Morten; Kolstad, Henrik Albert
2016-09-01
We studied the risk of hypersensitivity pneumonitis and other interstitial lung diseases (ILDs) among pigeon breeders.This is a retrospective follow-up study from 1980 to 2013 of 6920 pigeon breeders identified in the records of the Danish Racing Pigeon Association. They were compared with 276 800 individually matched referents randomly drawn from the Danish population. Hospital based diagnoses of hypersensitivity pneumonitis and other ILDs were identified in the National Patient Registry 1977-2013. Stratified Cox regression analyses estimated the hazard ratios (HR) of hypersensitivity pneumonitis and other ILDs adjusted for occupation, residence and redeemed prescription of medication with ILDs as a possible side-effect. Subjects were censored at death, emigration or a diagnosis of connective tissue disease.The overall incidence rate of ILD was 77.4 per 100 000 person-years among the pigeon breeders and 50.0 among the referents. This difference corresponded to an adjusted HR of 1.56 (95% CI 1.26-1.94). The adjusted HRs of hypersensitivity pneumonitis and other ILDs for pigeon breeders were 14.36 (95% CI 8.10-25.44) and 1.33 (95% CI 1.05-1.69), respectively.This study shows an increased risk of ILD among pigeon breeders compared with the referent population. Protective measures are recommended even though ILD leading to hospital contact remains rare among pigeon breeders. Copyright ©ERS 2016.
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NASA Astrophysics Data System (ADS)
Wu, Q. H.; Ma, J. T.
1993-09-01
A primary investigation into application of genetic algorithms in optimal reactive power dispatch and voltage control is presented. The application was achieved, based on (the United Kingdom) National Grid 48 bus network model, using a novel genetic search approach. Simulation results, compared with that obtained using nonlinear programming methods, are included to show the potential of applications of the genetic search methodology in power system economical and secure operations.
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Algorithme intelligent d'optimisation d'un design structurel de grande envergure
NASA Astrophysics Data System (ADS)
Dominique, Stephane
The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design. This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE). This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets. First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs. Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase. Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer. The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary. Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space. The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc. These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5. Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem. One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost. To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.
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NASA Astrophysics Data System (ADS)
Sun, Xiuqiao; Wang, Jian
2018-07-01
Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.
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Research on Laser Marking Speed Optimization by Using Genetic Algorithm
Wang, Dongyun; Yu, Qiwei; Zhang, Yu
2015-01-01
Laser Marking Machine is the most common coding equipment on product packaging lines. However, the speed of laser marking has become a bottleneck of production. In order to remove this bottleneck, a new method based on a genetic algorithm is designed. On the basis of this algorithm, a controller was designed and simulations and experiments were performed. The results show that using this algorithm could effectively improve laser marking efficiency by 25%. PMID:25955831
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NASA Astrophysics Data System (ADS)
An, M.; Assumpcao, M.
2003-12-01
The joint inversion of receiver function and surface wave is an effective way to diminish the influences of the strong tradeoff among parameters and the different sensitivity to the model parameters in their respective inversions, but the inversion problem becomes more complex. Multi-objective problems can be much more complicated than single-objective inversion in the model selection and optimization. If objectives are involved and conflicting, models can be ordered only partially. In this case, Pareto-optimal preference should be used to select solutions. On the other hand, the inversion to get only a few optimal solutions can not deal properly with the strong tradeoff between parameters, the uncertainties in the observation, the geophysical complexities and even the incompetency of the inversion technique. The effective way is to retrieve the geophysical information statistically from many acceptable solutions, which requires more competent global algorithms. Competent genetic algorithms recently proposed are far superior to the conventional genetic algorithm and can solve hard problems quickly, reliably and accurately. In this work we used one of competent genetic algorithms, Bayesian Optimization Algorithm as the main inverse procedure. This algorithm uses Bayesian networks to draw out inherited information and can use Pareto-optimal preference in the inversion. With this algorithm, the lithospheric structure of Paran"› basin is inverted to fit both the observations of inter-station surface wave dispersion and receiver function.
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How the Kennel Club is tackling inherited disorders in the United Kingdom.
Sampson, Jeff
2011-08-01
Health screening of potential canine breeding stock can provide invaluable information to allow breeders to select against inherited diseases in their breeding programmes. This review details the screening programmes that are currently available to UK dog breeders and evaluates their impact as selective tools for dog breeders. Copyright © 2011 Elsevier Ltd. All rights reserved.
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A Genetic-Based Scheduling Algorithm to Minimize the Makespan of the Grid Applications
NASA Astrophysics Data System (ADS)
Entezari-Maleki, Reza; Movaghar, Ali
Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in comparison with other well-known scheduling algorithms such as, Min-min, Max-min, RASA and Sufferage algorithms.
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Genetic Algorithms to Optimizatize Lecturer Assessment's Criteria
NASA Astrophysics Data System (ADS)
Jollyta, Deny; Johan; Hajjah, Alyauma
2017-12-01
The lecturer assessment criteria is used as a measurement of the lecturer's performance in a college environment. To determine the value for a criteriais complicated and often leads to doubt. The absence of a standard valuefor each assessment criteria will affect the final results of the assessment and become less presentational data for the leader of college in taking various policies relate to reward and punishment. The Genetic Algorithm comes as an algorithm capable of solving non-linear problems. Using chromosomes in the random initial population, one of the presentations is binary, evaluates the fitness function and uses crossover genetic operator and mutation to obtain the desired crossbreed. It aims to obtain the most optimum criteria values in terms of the fitness function of each chromosome. The training results show that Genetic Algorithm able to produce the optimal values of lecturer assessment criteria so that can be usedby the college as a standard value for lecturer assessment criteria.
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A theoretical comparison of evolutionary algorithms and simulated annealing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Hart, W.E.
1995-08-28
This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less
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Design of Genetic Algorithms for Topology Control of Unmanned Vehicles
2010-01-01
decentralised topology control mechanism distributed among active running software agents to achieve a uniform spread of terrestrial unmanned vehicles...14. ABSTRACT We present genetic algorithms (GAs) as a decentralised topology control mechanism distributed among active running software agents to...inspired topology control algorithm. The topology control of UVs using a decentralised solution over an unknown geographical terrain is a challenging
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Combinatorial optimization problem solution based on improved genetic algorithm
NASA Astrophysics Data System (ADS)
Zhang, Peng
2017-08-01
Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.
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AGPase: its role in crop productivity with emphasis on heat tolerance in cereals.
Saripalli, Gautam; Gupta, Pushpendra Kumar
2015-10-01
AGPase, a key enzyme of starch biosynthetic pathway, has a significant role in crop productivity. Thermotolerant variants of AGPase in cereals may be used for developing cultivars, which may enhance productivity under heat stress. Improvement of crop productivity has always been the major goal of plant breeders to meet the global demand for food. However, crop productivity itself is influenced in a large measure by a number of abiotic stresses including heat, which causes major losses in crop productivity. In cereals, crop productivity in terms of grain yield mainly depends upon the seed starch content so that starch biosynthesis and the enzymes involved in this process have been a major area of investigation for plant physiologists and plant breeders alike. Considerable work has been done on AGPase and its role in crop productivity, particularly under heat stress, because this enzyme is one of the major enzymes, which catalyses the rate-limiting first committed key enzymatic step of starch biosynthesis. Keeping the above in view, this review focuses on the basic features of AGPase including its structure, regulatory mechanisms involving allosteric regulators, its sub-cellular localization and its genetics. Major emphasis, however, has been laid on the genetics of AGPases and its manipulation for developing high yielding cultivars that will have comparable productivity under heat stress. Some important thermotolerant variants of AGPase, which mainly involve specific amino acid substitutions, have been highlighted, and the prospects of using these thermotolerant variants of AGPase in developing cultivars for heat prone areas have been discussed. The review also includes a brief account on transgenics for AGPase, which have been developed for basic studies and crop improvement.
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Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.
2010-01-01
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190
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NASA Astrophysics Data System (ADS)
Huang, Yin; Chen, Jianhua; Xiong, Shaojun
2009-07-01
Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.
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Rapid genetic diversification within dog breeds as evidenced by a case study on Schnauzers.
Streitberger, K; Schweizer, M; Kropatsch, R; Dekomien, G; Distl, O; Fischer, M S; Epplen, J T; Hertwig, S T
2012-10-01
As a result of strong artificial selection, the domesticated dog has arguably become one of the most morphologically diverse vertebrate species, which is mirrored in the classification of around 400 different breeds. To test the influence of breeding history on the genetic structure and variability of today's dog breeds, we investigated 12 dog breeds using a set of 19 microsatellite markers from a total of 597 individuals with about 50 individuals analysed per breed. High genetic diversity was noted over all breeds, with the ancient Asian breeds (Akita, Chow Chow, Shar Pei) exhibiting the highest variability, as was indicated chiefly by an extraordinarily high number of rare and private alleles. Using a Bayesian clustering method, we detected significant genetic stratification within the closely related Schnauzer breeds. The individuals of these three recently differentiated breeds (Miniature, Standard and Giant Schnauzer) could not be assigned to a single cluster each. This hidden genetic structure was probably caused by assortative mating owing to breeders' preferences regarding coat colour types and the underlying practice of breeding in separate lineages. Such processes of strong artificial disruptive selection for different morphological traits in isolated and relatively small lineages can result in the rapid creation of new dog types and potentially new breeds and represent a unique opportunity to study the evolution of genetic and morphological differences in recently diverged populations. © 2011 The Authors, Animal Genetics © 2011 Stichting International Foundation for Animal Genetics.
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The Genetic Architecture of Climatic Adaptation of Tropical Cattle
Porto-Neto, Laercio R.; Reverter, Antonio; Prayaga, Kishore C.; Chan, Eva K. F.; Johnston, David J.; Hawken, Rachel J.; Fordyce, Geoffry; Garcia, Jose Fernando; Sonstegard, Tad S.; Bolormaa, Sunduimijid; Goddard, Michael E.; Burrow, Heather M.; Henshall, John M.; Lehnert, Sigrid A.; Barendse, William
2014-01-01
Adaptation of global food systems to climate change is essential to feed the world. Tropical cattle production, a mainstay of profitability for farmers in the developing world, is dominated by heat, lack of water, poor quality feedstuffs, parasites, and tropical diseases. In these systems European cattle suffer significant stock loss, and the cross breeding of taurine x indicine cattle is unpredictable due to the dilution of adaptation to heat and tropical diseases. We explored the genetic architecture of ten traits of tropical cattle production using genome wide association studies of 4,662 animals varying from 0% to 100% indicine. We show that nine of the ten have genetic architectures that include genes of major effect, and in one case, a single location that accounted for more than 71% of the genetic variation. One genetic region in particular had effects on parasite resistance, yearling weight, body condition score, coat colour and penile sheath score. This region, extending 20 Mb on BTA5, appeared to be under genetic selection possibly through maintenance of haplotypes by breeders. We found that the amount of genetic variation and the genetic correlations between traits did not depend upon the degree of indicine content in the animals. Climate change is expected to expand some conditions of the tropics to more temperate environments, which may impact negatively on global livestock health and production. Our results point to several important genes that have large effects on adaptation that could be introduced into more temperate cattle without detrimental effects on productivity. PMID:25419663
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Ru, Sushan; Hardner, Craig; Carter, Patrick A; Evans, Kate; Main, Dorrie; Peace, Cameron
2016-01-01
Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest. Traditionally, genetic potential is determined by phenotypic evaluation. With the availability of DNA tests for some agronomically important traits, breeders have the opportunity to include DNA information in their seedling selection operations—known as marker-assisted seedling selection. A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies. Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops, as clonal propagation captures all genetic effects, not just additive. This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation, based on a generalized seedling selection scheme of clonally propagated crops. Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns. Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability. Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available. PMID:27148453
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Identification of Genetic Differentiation between Waxy and Common Maize by SNP Genotyping
Hao, Derong; Zhang, Zhenliang; Cheng, Yujing; Chen, Guoqing; Lu, Huhua; Mao, Yuxiang; Shi, Mingliang; Huang, Xiaolan; Zhou, Guangfei; Xue, Lin
2015-01-01
Waxy maize (Zea mays L. var. ceratina) is an important vegetable and economic crop that is thought to have originated from cultivated flint maize and most recently underwent divergence from common maize. In this study, a total of 110 waxy and 110 common maize inbred lines were genotyped with 3072 SNPs to evaluate the genetic diversity, population structure, and linkage disequilibrium decay as well as identify putative loci that are under positive selection. The results revealed abundant genetic diversity in the studied panel and that genetic diversity was much higher in common than in waxy maize germplasms. Principal coordinate analysis and neighbor-joining cluster analysis consistently classified the 220 accessions into two major groups and a mixed group with mixed ancestry. Subpopulation structure in both waxy and common maize sets were associated with the germplasm origin and corresponding heterotic groups. The LD decay distance (1500–2000 kb) in waxy maize was lower than that in common maize. Fourteen candidate loci were identified as under positive selection between waxy and common maize at the 99% confidence level. The information from this study can assist waxy maize breeders by enhancing parental line selection and breeding program design. PMID:26566240
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Evolutionary rates for multivariate traits: the role of selection and genetic variation
Pitchers, William; Wolf, Jason B.; Tregenza, Tom; Hunt, John; Dworkin, Ian
2014-01-01
A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders' equation (), which predicts evolutionary change for a suite of phenotypic traits () as a product of directional selection acting on them (β) and the genetic variance–covariance matrix for those traits (G). Despite being empirically challenging to estimate, there are enough published estimates of G and β to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates. PMID:25002697
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Radin, J W; Lu, Z; Percy, R G; Zeiger, E
1994-01-01
Responses of stomata to environment have been intensively studied, but little is known of genetic effects on stomatal conductance or their consequences. In Pima cotton (Gossypium barbadense L.), a crop that is bred for irrigated production in very hot environments, stomatal conductance varies genetically over a wide range and has increased with each release of new higher-yielding cultivars. A cross between heat-adapted (high-yielding) and unadapted genotypes produced F2 progeny cosegregating for stomatal conductance and leaf temperature. Within segregating populations in the field, conductance was negatively correlated with foliar temperature because of evaporative cooling. Plants were selected from the F2 generation specifically and solely for differing stomatal conductance. Among F3 and F4 populations derived from these selections, conductance and leaf cooling were significantly correlated with fruiting prolificacy during the hottest period of the year and with yield. Conductance was not associated with other factors that might have affected yield potential (single-leaf photosynthetic rate, leaf water potential). As breeders have increased the yield of this crop, genetic variability for conductance has allowed inadvertent selection for "heat avoidance" (evaporative cooling) in a hot environment. PMID:11607487
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Harper, John; Phillips, Dylan; Thomas, Ann; Gasior, Dagmara; Evans, Caron; Powell, Wayne; King, Julie; King, Ian; Jenkins, Glyn; Armstead, Ian
2018-04-09
Supernumerary 'B' chromosomes are non-essential components of the genome present in a range of plant and animal species-including many grasses. Within diploid and polyploid ryegrass and fescue species, including the forage grass perennial ryegrass (Lolium perenne L.), the presence of B chromosomes has been reported as influencing both chromosome pairing and chiasma frequencies. In this study, the effects of the presence/absence of B chromosomes on genetic recombination has been investigated through generating DArT (Diversity Arrays Technology) marker genetic maps for six perennial ryegrass diploid populations, the pollen parents of which contained either two B or zero B chromosomes. Through genetic and cytological analyses of these progeny and their parents, we have identified that, while overall cytological estimates of chiasma frequencies were significantly lower in pollen mother cells with two B chromosomes as compared with zero B chromosomes, the recombination frequencies within some marker intervals were actually increased, particularly for marker intervals in lower recombination regions of chromosomes, namely pericentromeric regions. Thus, in perennial ryegrass, the presence of two B chromosomes redistributed patterns of meiotic recombination in pollen mother cells in ways which could increase the range of allelic variation available to plant breeders.
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SOME USES OF MODELS OF QUANTITATIVE GENETIC SELECTION IN SOCIAL SCIENCE.
Weight, Michael D; Harpending, Henry
2017-01-01
The theory of selection of quantitative traits is widely used in evolutionary biology, agriculture and other related fields. The fundamental model known as the breeder's equation is simple, robust over short time scales, and it is often possible to estimate plausible parameters. In this paper it is suggested that the results of this model provide useful yardsticks for the description of social traits and the evaluation of transmission models. The differences on a standard personality test between samples of Old Order Amish and Indiana rural young men from the same county and the decline of homicide in Medieval Europe are used as illustrative examples of the overall approach. It is shown that the decline of homicide is unremarkable under a threshold model while the differences between rural Amish and non-Amish young men are too large to be a plausible outcome of simple genetic selection in which assortative mating by affiliation is equivalent to truncation selection.
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From breeder reactors to butterflies: risk, culture, and biotechnology.
Lomax, G P
2000-10-01
Social theories of risk suggest that a combination of scientific and cultural perspectives converge to influence risk perception. This article first surveys sociological perspectives suggesting that risks from modern technological development have become predominant concerns in the social consciousness. Particular attention is given to those theses describing how social elements work to create perception of risks in relation to new technologies. The themes that emerge from this survey are then related to contemporary debates concerning biotechnology. Specific attention is given to recent controversies regarding genetically modified crops, and parallels are drawn between debates over nuclear power and biotechnology. A procedural ethic for public discourse and decision making over the diffusion of genetically modified foods is offered. Ethical and social theories are linked with the hope that by recognizing the social dimensions of debates over new technologies a broader framework for conducting risk analysis may emerge.
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Other-regarding preferences in a non-human primate: Common marmosets provision food altruistically
Burkart, Judith M.; Fehr, Ernst; Efferson, Charles; van Schaik, Carel P.
2007-01-01
Human cooperation is unparalleled in the animal world and rests on an altruistic concern for the welfare of genetically unrelated strangers. The evolutionary roots of human altruism, however, remain poorly understood. Recent evidence suggests a discontinuity between humans and other primates because individual chimpanzees do not spontaneously provide food to other group members, indicating a lack of concern for their welfare. Here, we demonstrate that common marmoset monkeys (Callithrix jacchus) do spontaneously provide food to nonreciprocating and genetically unrelated individuals, indicating that other-regarding preferences are not unique to humans and that their evolution did not require advanced cognitive abilities such as theory of mind. Because humans and marmosets are cooperative breeders and the only two primate taxa in which such unsolicited prosociality has been found, we conclude that these prosocial predispositions may emanate from cooperative breeding. PMID:18077409
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Phenotyping for drought tolerance of crops in the genomics era
Tuberosa, Roberto
2012-01-01
Improving crops yield under water-limited conditions is the most daunting challenge faced by breeders. To this end, accurate, relevant phenotyping plays an increasingly pivotal role for the selection of drought-resilient genotypes and, more in general, for a meaningful dissection of the quantitative genetic landscape that underscores the adaptive response of crops to drought. A major and universally recognized obstacle to a more effective translation of the results produced by drought-related studies into improved cultivars is the difficulty in properly phenotyping in a high-throughput fashion in order to identify the quantitative trait loci that govern yield and related traits across different water regimes. This review provides basic principles and a broad set of references useful for the management of phenotyping practices for the study and genetic dissection of drought tolerance and, ultimately, for the release of drought-tolerant cultivars. PMID:23049510
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Wagner, Maggie R.; Lundberg, Derek S.; del Rio, Tijana G.
Bacteria living on and in leaves and roots influence many aspects of plant health, so the extent of a plant's genetic control over its microbiota is of great interest to crop breeders and evolutionary biologists. Laboratory-based studies, because they poorly simulate true environmental heterogeneity, may misestimate or totally miss the influence of certain host genes on the microbiome. Here we report a large-scale field experiment to disentangle the effects of genotype, environment, age and year of harvest on bacterial communities associated with leaves and roots of Boechera stricta (Brassicaceae), a perennial wild mustard. Host genetic control of the microbiome ismore » evident in leaves but not roots, and varies substantially among sites. Microbiome composition also shifts as plants age. Furthermore, a large proportion of leaf bacterial groups are shared with roots, suggesting inoculation from soil. Our results demonstrate how genotype-by-environment interactions contribute to the complexity of microbiome assembly in natural environments.« less
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A return to the genetic heritage of durum wheat to cope with drought heightened by climate change.
Slama, Amor; Mallek-Maalej, Elhem; Ben Mohamed, Hatem; Rhim, Thouraya; Radhouane, Leila
2018-01-01
The objective of this work was to perform a comparative analysis of the physiological, biochemical and agronomical parameters of recent and heritage durum wheat cultivars (Triticum durum Desf.) under water-deficit conditions. Five cultivars were grown under irrigated (control) and rainfall (stressed) conditions. Different agro-physiological and biochemical parameters were studied: electrolyte leakage, relative water content, chlorophyll fluorescence, proline, soluble sugars, specific peroxidase activity, yield and drought stress indices. It was revealed that a water deficit increased proline content, electrolyte leakage, soluble sugars and specific peroxidase activity and decreased relative water content, fluorescence and grain yield. According to these parameters and drought stress indices, our investigation indicated that old cultivars are the best-adapted to local conditions and showed characteristics of drought tolerance, while recent cultivars showed more drought susceptibility. Therefore, local cultivars of each country should be kept by farmers and plant breeders to preserve their genetic heritage.
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Pleiotropic effects of herbicide-resistance genes on crop yield: a review.
Darmency, Henri
2013-08-01
The rapid adoption of genetically engineered herbicide-resistant crop varieties (HRCVs)-encompassing 83% of all GM crops and nearly 8% of the worldwide arable area-is due to technical efficiency and higher returns. Other herbicide-resistant varieties obtained from genetic resources and mutagenesis have also been successfully released. Although the benefit for weed control is the main criteria for choosing HRCVs, the pleiotropic costs of genes endowing resistance have rarely been investigated in crops. Here the available data of comparisons between isogenic resistant and susceptible varieties are reviewed. Pleiotropic harmful effects on yield are reported in half of the cases, mostly with resistance mechanisms that originate from genetic resources and mutagenesis (atrazine in oilseed rape and millet, trifluralin in millet, imazamox in cotton) rather than genetic engineering (chlorsulfuron and glufosinate in some oilseed rape varieties, glyphosate in soybean). No effect was found for sethoxydim and bromoxynil resistance. Variable minor effects were found for imazamox, chlorsulfuron, glufosinate and glyphosate resistance. The importance of the breeding plan and the genetic background on the emergence of these effects is pointed out. Breeders' efforts to produce better varieties could compensate for the yield loss, which eliminates any possibility of formulating generic conclusions on pleiotropic effects that can be applied to all resistant crops. © 2013 Society of Chemical Industry.
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GM foods: is there a way forward?
Jones, Huw D
2015-08-01
There are many quality targets in cereals that could generate step-change improvements in nutritional or food-processing characteristics. For instance, levels of acrylamide, soluble and insoluble fibre, antioxidants, allergens and intolerance factors in food are, to a large extent, determined by the genetics of the raw materials used. However, improvements to these traits pose significant challenges to plant breeders. For some traits, this is because the underlying genetic and biochemical basis of the traits is not fully understood but for others, there is simply a lack of natural genetic variation in commercially useful germplasm. One strategy to overcome the latter hindrance is to use wide crosses with more exotic germplasm; however, this can bring other difficulties such as yield loss and linkage drag of deleterious alleles. As DNA sequencing becomes cheaper and faster, it drives the research fields of reverse genetics and functional genomics which in turn will enable the incorporation of desirable traits into crop varieties via molecular breeding and biotechnology. I will discuss the evolution of these techniques from conventional genetic modification to more recent developments in targeted gene editing and the potential of biotechnology to complement conventional breeding methods. I will also discuss the role of risk assessment and regulation in the commercialisation of GM crops.
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Genetic Diversity and Population Structure of Cowpea (Vigna unguiculata L. Walp).
Xiong, Haizheng; Shi, Ainong; Mou, Beiquan; Qin, Jun; Motes, Dennis; Lu, Weiguo; Ma, Jianbing; Weng, Yuejin; Yang, Wei; Wu, Dianxing
2016-01-01
The genetic diversity of cowpea was analyzed, and the population structure was estimated in a diverse set of 768 cultivated cowpea genotypes from the USDA GRIN cowpea collection, originally collected from 56 countries. Genotyping by sequencing was used to discover single nucleotide polymorphism (SNP) in cowpea and the identified SNP alleles were used to estimate the level of genetic diversity, population structure, and phylogenetic relationships. The aim of this study was to detect the gene pool structure of cowpea and to determine its relationship between different regions and countries. Based on the model-based ancestry analysis, the phylogenetic tree, and the principal component analysis, three well-differentiated genetic populations were postulated from 768 worldwide cowpea genotypes. According to the phylogenetic analyses between each individual, region, and country, we may trace the accession from off-original, back to the two candidate original areas (West and East of Africa) to predict the migration and domestication history during the cowpea dispersal and development. To our knowledge, this is the first report of the analysis of the genetic variation and relationship between globally cultivated cowpea genotypes. The results will help curators, researchers, and breeders to understand, utilize, conserve, and manage the collection for more efficient contribution to international cowpea research.
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Genetic Diversity and Population Structure of Cowpea (Vigna unguiculata L. Walp)
Xiong, Haizheng; Shi, Ainong; Mou, Beiquan; Qin, Jun; Motes, Dennis; Lu, Weiguo; Ma, Jianbing; Weng, Yuejin; Yang, Wei; Wu, Dianxing
2016-01-01
The genetic diversity of cowpea was analyzed, and the population structure was estimated in a diverse set of 768 cultivated cowpea genotypes from the USDA GRIN cowpea collection, originally collected from 56 countries. Genotyping by sequencing was used to discover single nucleotide polymorphism (SNP) in cowpea and the identified SNP alleles were used to estimate the level of genetic diversity, population structure, and phylogenetic relationships. The aim of this study was to detect the gene pool structure of cowpea and to determine its relationship between different regions and countries. Based on the model-based ancestry analysis, the phylogenetic tree, and the principal component analysis, three well-differentiated genetic populations were postulated from 768 worldwide cowpea genotypes. According to the phylogenetic analyses between each individual, region, and country, we may trace the accession from off-original, back to the two candidate original areas (West and East of Africa) to predict the migration and domestication history during the cowpea dispersal and development. To our knowledge, this is the first report of the analysis of the genetic variation and relationship between globally cultivated cowpea genotypes. The results will help curators, researchers, and breeders to understand, utilize, conserve, and manage the collection for more efficient contribution to international cowpea research. PMID:27509049
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NASA Astrophysics Data System (ADS)
Bay, Annick; Mayer, Alexandre
2014-09-01
The efficiency of light-emitting diodes (LED) has increased significantly over the past few years, but the overall efficiency is still limited by total internal reflections due to the high dielectric-constant contrast between the incident and emergent media. The bioluminescent organ of fireflies gave incentive for light-extraction enhance-ment studies. A specific factory-roof shaped structure was shown, by means of light-propagation simulations and measurements, to enhance light extraction significantly. In order to achieve a similar effect for light-emitting diodes, the structure needs to be adapted to the specific set-up of LEDs. In this context simulations were carried out to determine the best geometrical parameters. In the present work, the search for a geometry that maximizes the extraction of light has been conducted by using a genetic algorithm. The idealized structure considered previously was generalized to a broader variety of shapes. The genetic algorithm makes it possible to search simultaneously over a wider range of parameters. It is also significantly less time-consuming than the previous approach that was based on a systematic scan on parameters. The results of the genetic algorithm show that (1) the calculations can be performed in a smaller amount of time and (2) the light extraction can be enhanced even more significantly by using optimal parameters determined by the genetic algorithm for the generalized structure. The combination of the genetic algorithm with the Rigorous Coupled Waves Analysis method constitutes a strong simulation tool, which provides us with adapted designs for enhancing light extraction from light-emitting diodes.
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Bellucci, Michael A; Coker, David F
2011-07-28
We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics
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MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION
In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...
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Evaluation of a formula that categorizes female gray wolf breeding status by nipple size
Barber-Meyer, Shannon M.; Mech, L. David
2015-01-01
The proportion by age class of wild Canis lupus (Gray Wolf) females that reproduce in any given year remains unclear; thus, we evaluated the applicability to our long-term (1972–2013) data set of the Mech et al. (1993) formula that categorizes female Gray Wolf breeding status by nipple size and time of year. We used the formula to classify Gray Wolves from 68 capture events into 4 categories (yearling, adult non-breeder, former breeder, current breeder). To address issues with small sample size and variance, we created an ambiguity index to allow some Gray Wolves to be classed into 2 categories. We classified 20 nipple measurements ambiguously: 16 current or former breeder, 3 former or adult non-breeder, and 1 yearling or adult non-breeder. The formula unambiguously classified 48 (71%) of the nipple measurements; based on supplemental field evidence, at least 5 (10%) of these were incorrect. When used in conjunction with an ambiguity index we developed and with corrections made for classifications involving very large nipples, and supplemented with available field evidence, the Mech et al. (1993) formula provided reasonably reliable classification of breeding status in wild female Gray Wolves.
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Strain gage selection in loads equations using a genetic algorithm
NASA Technical Reports Server (NTRS)
1994-01-01
Traditionally, structural loads are measured using strain gages. A loads calibration test must be done before loads can be accurately measured. In one measurement method, a series of point loads is applied to the structure, and loads equations are derived via the least squares curve fitting algorithm using the strain gage responses to the applied point loads. However, many research structures are highly instrumented with strain gages, and the number and selection of gages used in a loads equation can be problematic. This paper presents an improved technique using a genetic algorithm to choose the strain gages used in the loads equations. Also presented are a comparison of the genetic algorithm performance with the current T-value technique and a variant known as the Best Step-down technique. Examples are shown using aerospace vehicle wings of high and low aspect ratio. In addition, a significant limitation in the current methods is revealed. The genetic algorithm arrived at a comparable or superior set of gages with significantly less human effort, and could be applied in instances when the current methods could not.
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A hybrid genetic algorithm for solving bi-objective traveling salesman problems
NASA Astrophysics Data System (ADS)
Ma, Mei; Li, Hecheng
2017-08-01
The traveling salesman problem (TSP) is a typical combinatorial optimization problem, in a traditional TSP only tour distance is taken as a unique objective to be minimized. When more than one optimization objective arises, the problem is known as a multi-objective TSP. In the present paper, a bi-objective traveling salesman problem (BOTSP) is taken into account, where both the distance and the cost are taken as optimization objectives. In order to efficiently solve the problem, a hybrid genetic algorithm is proposed. Firstly, two satisfaction degree indices are provided for each edge by considering the influences of the distance and the cost weight. The first satisfaction degree is used to select edges in a “rough” way, while the second satisfaction degree is executed for a more “refined” choice. Secondly, two satisfaction degrees are also applied to generate new individuals in the iteration process. Finally, based on genetic algorithm framework as well as 2-opt selection strategy, a hybrid genetic algorithm is proposed. The simulation illustrates the efficiency of the proposed algorithm.
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Environmental Enrichment for Broiler Breeders: An Undeveloped Field.
Riber, Anja B; de Jong, Ingrid C; van de Weerd, Heleen A; Steenfeldt, Sanna
2017-01-01
Welfare problems, such as hunger, frustration, aggression, and abnormal sexual behavior, are commonly found in broiler breeder production. To prevent or reduce these welfare problems, it has been suggested to provide stimulating enriched environments. We review the effect of the different types of environmental enrichment for broiler breeders, which have been described in the scientific literature, on behavior and welfare. Environmental enrichment is defined as an improvement of the environment of captive animals, which increases the behavioral opportunities of the animal and leads to improvements in biological function. This definition has been broadened to include practical and economic aspects as any enrichment strategy that adversely affects the health of animals (e.g., environmental hygiene), or that has too many economic or practical constraints will never be implemented on commercial farms and thus never benefit animals. Environmental enrichment for broiler breeders often has the purpose of satisfying the behavioral motivations for feeding and foraging, resting, and/or encouraging normal sexual behavior. Potentially successful enrichments for broiler breeders are elevated resting places, cover panels, and substrate (for broiler breeders housed in cage systems). However, most of the ideas for environmental enrichment for broiler breeders need to be further developed and studied with respect to the use, the effect on behavior and welfare, and the interaction with genotype and production system. In addition, information on practical use and the economics of the production system is often lacking although it is important for application in practice.
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Liu, Zhangxiong; Li, Huihui; Wen, Zixiang; Fan, Xuhong; Li, Yinghui; Guan, Rongxia; Guo, Yong; Wang, Shuming; Wang, Dechun; Qiu, Lijuan
2017-01-01
Soybean is one of the most important economic crops for both China and the United States (US). The exchange of germplasm between these two countries has long been active. In order to investigate genetic relationships between Chinese and US soybean germplasm, 277 Chinese soybean accessions and 300 US soybean accessions from geographically diverse regions were analyzed using 5,361 SNP markers. The genetic diversity and the polymorphism information content (PIC) of the Chinese accessions was higher than that of the US accessions. Population structure analysis, principal component analysis, and cluster analysis all showed that the genetic basis of Chinese soybeans is distinct from that of the USA. The groupings observed in clustering analysis reflected the geographical origins of the accessions; this conclusion was validated with both genetic distance analysis and relative kinship analysis. FST-based and EigenGWAS statistical analysis revealed high genetic variation between the two subpopulations. Analysis of the 10 loci with the strongest selection signals showed that many loci were located in chromosome regions that have previously been identified as quantitative trait loci (QTL) associated with environmental-adaptation-related and yield-related traits. The pattern of diversity among the American and Chinese accessions should help breeders to select appropriate parental accessions to enhance the performance of future soybean cultivars. PMID:29250088
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Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke
2012-01-01
Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.
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Rabow, A. A.; Scheraga, H. A.
1996-01-01
We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904
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The genetic algorithm: A robust method for stress inversion
NASA Astrophysics Data System (ADS)
Thakur, Prithvi; Srivastava, Deepak C.; Gupta, Pravin K.
2017-01-01
The stress inversion of geological or geophysical observations is a nonlinear problem. In most existing methods, it is solved by linearization, under certain assumptions. These linear algorithms not only oversimplify the problem but also are vulnerable to entrapment of the solution in a local optimum. We propose the use of a nonlinear heuristic technique, the genetic algorithm, which searches the global optimum without making any linearizing assumption or simplification. The algorithm mimics the natural evolutionary processes of selection, crossover and mutation and, minimizes a composite misfit function for searching the global optimum, the fittest stress tensor. The validity and efficacy of the algorithm are demonstrated by a series of tests on synthetic and natural fault-slip observations in different tectonic settings and also in situations where the observations are noisy. It is shown that the genetic algorithm is superior to other commonly practised methods, in particular, in those tectonic settings where none of the principal stresses is directed vertically and/or the given data set is noisy.
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USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES
Genetic algorithm calculations are applied to the design of chemical processes to achieve improvements in environmental and economic performance. By finding the set of Pareto (i.e., non-dominated) solutions one can see how different objectives, such as environmental and economic ...
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The application of immune genetic algorithm in main steam temperature of PID control of BP network
NASA Astrophysics Data System (ADS)
Li, Han; Zhen-yu, Zhang
In order to overcome the uncertainties, large delay, large inertia and nonlinear property of the main steam temperature controlled object in the power plant, a neural network intelligent PID control system based on immune genetic algorithm and BP neural network is designed. Using the immune genetic algorithm global search optimization ability and good convergence, optimize the weights of the neural network, meanwhile adjusting PID parameters using BP network. The simulation result shows that the system is superior to conventional PID control system in the control of quality and robustness.
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Optimization of multicast optical networks with genetic algorithm
NASA Astrophysics Data System (ADS)
Lv, Bo; Mao, Xiangqiao; Zhang, Feng; Qin, Xi; Lu, Dan; Chen, Ming; Chen, Yong; Cao, Jihong; Jian, Shuisheng
2007-11-01
In this letter, aiming to obtain the best multicast performance of optical network in which the video conference information is carried by specified wavelength, we extend the solutions of matrix games with the network coding theory and devise a new method to solve the complex problems of multicast network switching. In addition, an experimental optical network has been testified with best switching strategies by employing the novel numerical solution designed with an effective way of genetic algorithm. The result shows that optimal solutions with genetic algorithm are accordance with the ones with the traditional fictitious play method.
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Real coded genetic algorithm for fuzzy time series prediction
NASA Astrophysics Data System (ADS)
Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.
2017-10-01
Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.
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Air data system optimization using a genetic algorithm
NASA Technical Reports Server (NTRS)
Deshpande, Samir M.; Kumar, Renjith R.; Seywald, Hans; Siemers, Paul M., III
1992-01-01
An optimization method for flush-orifice air data system design has been developed using the Genetic Algorithm approach. The optimization of the orifice array minimizes the effect of normally distributed random noise in the pressure readings on the calculation of air data parameters, namely, angle of attack, sideslip angle and freestream dynamic pressure. The optimization method is applied to the design of Pressure Distribution/Air Data System experiment (PD/ADS) proposed for inclusion in the Aeroassist Flight Experiment (AFE). Results obtained by the Genetic Algorithm method are compared to the results obtained by conventional gradient search method.
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Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm
Terzić, Balša; Hofler, Alicia S.; Reeves, Cody J.; ...
2014-10-15
In this paper, a genetic algorithm-based optimization is used to simultaneously minimize two competing objectives guiding the operation of the Jefferson Lab's Continuous Electron Beam Accelerator Facility linacs: cavity heat load and radio frequency cavity trip rates. The results represent a significant improvement to the standard linac energy management tool and thereby could lead to a more efficient Continuous Electron Beam Accelerator Facility configuration. This study also serves as a proof of principle of how a genetic algorithm can be used for optimizing other linac-based machines.
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A novel hybrid genetic algorithm for optimal design of IPM machines for electric vehicle
NASA Astrophysics Data System (ADS)
Wang, Aimeng; Guo, Jiayu
2017-12-01
A novel hybrid genetic algorithm (HGA) is proposed to optimize the rotor structure of an IPM machine which is used in EV application. The finite element (FE) simulation results of the HGA design is compared with the genetic algorithm (GA) design and those before optimized. It is shown that the performance of the IPMSM is effectively improved by employing the GA and HGA, especially by HGA. Moreover, higher flux-weakening capability and less magnet usage are also obtained. Therefore, the validity of HGA method in IPMSM optimization design is verified.
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A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection
Thounaojam, Dalton Meitei; Khelchandra, Thongam; Singh, Kh. Manglem; Roy, Sudipta
2016-01-01
This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter. PMID:27127500
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Sethi, Gaurav; Saini, B S
2015-12-01
This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.
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Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A
2015-06-01
Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.
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Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs
NASA Astrophysics Data System (ADS)
Schalkoff, Robert J.; Shaaban, Khaled M.
1999-07-01
Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.
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North American Plan for Avian and Pandemic Influenza
2007-08-01
broiler and turkey flocks ( meat -type birds); • Commercial duck and goose meat -type production flocks; • Pullet production flocks; • Commercial...services, public health measures and communications. Notifiable Avian Influenza Hazard Specific Plan. This plan outlines the response to be undertaken by...terme_compartiment) • Broiler, turkey or layer breeder production flocks; • Duck breeder and upland game breeder flocks; • Commercial (grow out
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USDA-ARS?s Scientific Manuscript database
The effect of feeding programs on the time of clearance of Escherichia coli in broiler breeder pullets was investigated. Broiler breeder pullets from a single grandparent flock were in ovo-vaccinated at 19 d of incubation with a vector HVT (vHVT) vector HVT + Infectious bursal disease (IBD) vaccine....
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U.S. Nuclear Cooperation with India: Issues for Congress
2008-10-17
safeguards-irrelevant.” The following facilities and activities were not on the separation list: ! 8 indigenous Indian power reactors ! Fast Breeder ...test Reactor (FTBR) and Prototype Fast Breeder Reactors (PFBR) under construction ! Enrichment facilities ! Spent fuel reprocessing facilities (except...potential use in a bomb. In addition, safeguards on enrichment, reprocessing plants, and breeder reactors would support the 2002 U.S. National Strategy to
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Toro, H; González, C; Cerda, L; Morales, M A; Dooner, P; Salamero, M
2002-01-01
The hypothesis that an effective protection of progeny chickens against inclusion body hepatitis/hydropericardium syndrome (IBH/HP) can be achieved by dual vaccination of breeders with fowl adenovirus (FAV) serotype 4 and chicken anemia virus (CAV) was tested. Thus, 17-wk-old brown leghorn pullet groups were vaccinated by different schemes including single FAV (inactivated), single CAV (attenuated), FAV and CAV dually, or were not vaccinated (controls). Subsequent progenies of these breeders were challenged with the virulent strains FAV-341 and CAV-10343 following three strategies: 1) FAV-341 intramuscularly (i.m.) at day 10 of age (only FAV-vaccinated and control progenies); 2) FAV + CAV i.m. simultaneously at day 10 of age (all progenies); 3) CAV i.m. at day 1 and FAV orally at day 10 of age (all progenies). The induction of IBH/HP in these progenies was evaluated throughout a 10-day period. Both breeder groups vaccinated against FAV and those vaccinated against CAV increased virus neutralizing specific antibodies. Challenge strategy 1 showed 26.6% mortality in control progeny chickens and 13.3% in the progeny of FAV-vaccinated breeders. Presence of lesions in the liver of these groups showed no significant differences (P > 0.05), suggesting a discreet protective effect of the vaccine. Challenge strategy 2 showed 29.4% mortality in controls and 94% of chickens showed hepatic inclusion bodies (HIB). Single CAV vaccination of breeders did not demonstrate a beneficial effect, with both mortality and liver lesions resembling the nonvaccinated controls. FAV vaccination of breeders significantly reduced both mortality (7.4%) and liver lesions (26% HIB) (P < 0.05), providing protection against this challenge strategy. Dual vaccination of breeders with FAV and CAV proved to be necessary to achieve maximum protection of the progeny (no mortality and 7% HIB). Challenge strategy 3 produced no mortality but consistent liver damage in controls (96% HIB). In this case, both CAV and FAV + CAV-vaccinated breeders showed best protection results in terms of liver histopathology (8% and 0% HIB, respectively). FAV vaccination alone produced 24% HIB, similar to challenge strategy 2, demonstrating a lower protective effect.
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Koppenol, A; Delezie, E; Aerts, J; Willems, E; Wang, Y; Franssens, L; Everaert, N; Buyse, J
2014-03-01
When added to the feed of broiler breeder hens, dietary polyunsaturated fatty acids (FA) can be incorporated into the yolk and therefore become available to the progeny during their early development. The mechanism involved in lipid metabolism and deposition in the egg may be influenced by breeder age. Before the effect of an elevated concentration of certain polyunsaturated FA on the embryo can be investigated, the effect at breeder level and egg quality must be further assessed. The aim of the present experiment was to evaluate the effects of dietary n-6/n-3 ratios and dietary eicosapentaenoic acid (EPA, 20:5 n-3) and docosahexaenoic acid (DHA, 22:6 n-3) ratios, provided to broiler breeder hens, in terms of their zoo technical performance, egg quality, and yolk FA composition. Starting at 6 wk of age, 640 Ross-308 broiler breeder hens were fed 1 of 4 different diets. The control diet was a basal diet, rich in n-6 FA. The 3 other diets were enriched in n-3 FA, formulated to obtain a different EPA/DHA ratio of 1/1 (EPA = DHA), 1/2 (DHA), or 2/1 (EPA). In fact, after analysis the EPA/DHA ratio was 0.8, 0.4, or 2.1, respectively. Dietary EPA and DHA addition did not affect the performance of the breeder hens, except for egg weight. Egg weight was lower (P < 0.001) for all n-3 treatments. Dietary EPA improved number of eggs laid in the first 2 wk of the production cycle (P = 0.029). The absolute and relative yolk weight of eggs laid by EPA = DHA fed hens was lowest (P = 0.004 and P = 0.025, respectively). The EPA and DHA concentrations in the yolk were highly dependent on dietary EPA and DHA concentrations with a regression coefficient equal to 0.89. It can be concluded that dietary EPA and DHA can be incorporated in the breeder egg yolk to become available for the developing embryo, without compromising the performance and egg quality of the flock.
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Fleskes, Joseph P.; Fowler, Ada C.; Casazza, Michael L.; Eadie, John M.
2010-01-01
Female Northern Pintails (Anas acuta) were sampled in California's three main Central Valley wintering regions (Sacramento Valley, Suisun Marsh, San Joaquin Valley) during September–October before most regional movements occur and microsatellite and mitochondrial DNA were analyzed to examine population structure and relatedness. Despite reportedly high rates of early-fall pairing and regional fidelity, both sets of markers indicated that there was little overall genetic structuring by region. Pintails from Suisun Marsh did exhibit higher relatedness among individuals and capture groups than in the Sacramento or San Joaquin Valleys, likely reflecting a sample comprised of a greater proportion of local breeders. The lack of genetic structuring among regions indicates that a high degree of movement and interchange occurs among pintails wintering in the Central Valley. Thus, although maintaining the existing distribution of pintails among Central Valley regions is important for other reasons, it does not appear to be critical to retain current patterns of population genetic variation. Because of potential lack of independence among highly related study individuals, researchers should consider regional differences in relatedness when designing sampling schemes and interpreting research findings.
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Eucalyptus applied genomics: from gene sequences to breeding tools.
Grattapaglia, Dario; Kirst, Matias
2008-01-01
Eucalyptus is the most widely planted hardwood crop in the tropical and subtropical world because of its superior growth, broad adaptability and multipurpose wood properties. Plantation forestry of Eucalyptus supplies high-quality woody biomass for several industrial applications while reducing the pressure on tropical forests and associated biodiversity. This review links current eucalypt breeding practices with existing and emerging genomic tools. A brief discussion provides a background to modern eucalypt breeding together with some current applications of molecular markers in support of operational breeding. Quantitative trait locus (QTL) mapping and genetical genomics are reviewed and an in-depth perspective is provided on the power of association genetics to dissect quantitative variation in this highly diverse organism. Finally, some challenges and opportunities to integrate genomic information into directional selective breeding are discussed in light of the upcoming draft of the Eucalyptus grandis genome. Given the extraordinary genetic variation that exists in the genus Eucalyptus, the ingenuity of most breeders, and the powerful genomic tools that have become available, the prospects of applied genomics in Eucalyptus forest production are encouraging.
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NASA Astrophysics Data System (ADS)
Sheng, Lizeng
The dissertation focuses on one of the major research needs in the area of adaptive/intelligent/smart structures, the development and application of finite element analysis and genetic algorithms for optimal design of large-scale adaptive structures. We first review some basic concepts in finite element method and genetic algorithms, along with the research on smart structures. Then we propose a solution methodology for solving a critical problem in the design of a next generation of large-scale adaptive structures---optimal placements of a large number of actuators to control thermal deformations. After briefly reviewing the three most frequently used general approaches to derive a finite element formulation, the dissertation presents techniques associated with general shell finite element analysis using flat triangular laminated composite elements. The element used here has three nodes and eighteen degrees of freedom and is obtained by combining a triangular membrane element and a triangular plate bending element. The element includes the coupling effect between membrane deformation and bending deformation. The membrane element is derived from the linear strain triangular element using Cook's transformation. The discrete Kirchhoff triangular (DKT) element is used as the plate bending element. For completeness, a complete derivation of the DKT is presented. Geometrically nonlinear finite element formulation is derived for the analysis of adaptive structures under the combined thermal and electrical loads. Next, we solve the optimization problems of placing a large number of piezoelectric actuators to control thermal distortions in a large mirror in the presence of four different thermal loads. We then extend this to a multi-objective optimization problem of determining only one set of piezoelectric actuator locations that can be used to control the deformation in the same mirror under the action of any one of the four thermal loads. A series of genetic algorithms, GA Version 1, 2 and 3, were developed to find the optimal locations of piezoelectric actuators from the order of 1021 ˜ 1056 candidate placements. Introducing a variable population approach, we improve the flexibility of selection operation in genetic algorithms. Incorporating mutation and hill climbing into micro-genetic algorithms, we are able to develop a more efficient genetic algorithm. Through extensive numerical experiments, we find that the design search space for the optimal placements of a large number of actuators is highly multi-modal and that the most distinct nature of genetic algorithms is their robustness. They give results that are random but with only a slight variability. The genetic algorithms can be used to get adequate solution using a limited number of evaluations. To get the highest quality solution, multiple runs including different random seed generators are necessary. The investigation time can be significantly reduced using a very coarse grain parallel computing. Overall, the methodology of using finite element analysis and genetic algorithm optimization provides a robust solution approach for the challenging problem of optimal placements of a large number of actuators in the design of next generation of adaptive structures.
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Selecting materialized views using random algorithm
NASA Astrophysics Data System (ADS)
Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi
2007-04-01
The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.
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Ortho Image and DTM Generation with Intelligent Methods
NASA Astrophysics Data System (ADS)
Bagheri, H.; Sadeghian, S.
2013-10-01
Nowadays the artificial intelligent algorithms has considered in GIS and remote sensing. Genetic algorithm and artificial neural network are two intelligent methods that are used for optimizing of image processing programs such as edge extraction and etc. these algorithms are very useful for solving of complex program. In this paper, the ability and application of genetic algorithm and artificial neural network in geospatial production process like geometric modelling of satellite images for ortho photo generation and height interpolation in raster Digital Terrain Model production process is discussed. In first, the geometric potential of Ikonos-2 and Worldview-2 with rational functions, 2D & 3D polynomials were tested. Also comprehensive experiments have been carried out to evaluate the viability of the genetic algorithm for optimization of rational function, 2D & 3D polynomials. Considering the quality of Ground Control Points, the accuracy (RMSE) with genetic algorithm and 3D polynomials method for Ikonos-2 Geo image was 0.508 pixel sizes and the accuracy (RMSE) with GA algorithm and rational function method for Worldview-2 image was 0.930 pixel sizes. For more another optimization artificial intelligent methods, neural networks were used. With the use of perceptron network in Worldview-2 image, a result of 0.84 pixel sizes with 4 neurons in middle layer was gained. The final conclusion was that with artificial intelligent algorithms it is possible to optimize the existing models and have better results than usual ones. Finally the artificial intelligence methods, like genetic algorithms as well as neural networks, were examined on sample data for optimizing interpolation and for generating Digital Terrain Models. The results then were compared with existing conventional methods and it appeared that these methods have a high capacity in heights interpolation and that using these networks for interpolating and optimizing the weighting methods based on inverse distance leads to a high accurate estimation of heights.
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DNA mutations of the cat: the good, the bad and the ugly.
Lyons, Leslie A
2015-03-01
The health of the cat is a complex interaction between its environment (nurture) and its genetics (nature). Over 70 genetic mutations (variants) have been defined in the cat, many involving diseases, structural abnormalities and clinically relevant health concerns. As more of the cat's genome is deciphered, less commonly will the term 'idiopathic' be used regarding the diagnosis of diseases and unique health conditions. State-of-the-art health care will include DNA profiling of the individual cat, and perhaps its tumor, to establish the best treatment approaches. Genetic testing and eventually whole genome sequencing should become routine diagnostics for feline health care. Cat breeds have disseminated around the world. Thus, practitioners should be aware of the breeds common to their region and the mutations found in those regional populations. Specific random-bred populations can also have defined genetic characteristics and mutations. This review of 'the good, the bad and the ugly' DNA variants provides the current state of knowledge for genetic testing and genetic health management for cats. It is aimed at feline and general practitioners wanting to update and review the basics of genetics, what tests are available for cats and sources for genetic testing. The tables are intended to be used as references in the clinic. Practitioners with a high proportion of cat breeder clientele will especially benefit from the review. The data presented is extracted from peer-reviewed publications pertaining to mutation identification, and relevant articles concerning the heritable trait and/or disease. The author also draws upon personal experience and expertise in feline genetics. © ISFM and AAFP 2015.
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NASA Astrophysics Data System (ADS)
Abdeh-Kolahchi, A.; Satish, M.; Datta, B.
2004-05-01
A state art groundwater monitoring network design is introduced. The method combines groundwater flow and transport results with optimization Genetic Algorithm (GA) to identify optimal monitoring well locations. Optimization theory uses different techniques to find a set of parameter values that minimize or maximize objective functions. The suggested groundwater optimal monitoring network design is based on the objective of maximizing the probability of tracking a transient contamination plume by determining sequential monitoring locations. The MODFLOW and MT3DMS models included as separate modules within the Groundwater Modeling System (GMS) are used to develop three dimensional groundwater flow and contamination transport simulation. The groundwater flow and contamination simulation results are introduced as input to the optimization model, using Genetic Algorithm (GA) to identify the groundwater optimal monitoring network design, based on several candidate monitoring locations. The groundwater monitoring network design model is used Genetic Algorithms with binary variables representing potential monitoring location. As the number of decision variables and constraints increase, the non-linearity of the objective function also increases which make difficulty to obtain optimal solutions. The genetic algorithm is an evolutionary global optimization technique, which is capable of finding the optimal solution for many complex problems. In this study, the GA approach capable of finding the global optimal solution to a groundwater monitoring network design problem involving 18.4X 1018 feasible solutions will be discussed. However, to ensure the efficiency of the solution process and global optimality of the solution obtained using GA, it is necessary that appropriate GA parameter values be specified. The sensitivity analysis of genetic algorithms parameters such as random number, crossover probability, mutation probability, and elitism are discussed for solution of monitoring network design.
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JavaGenes and Condor: Cycle-Scavenging Genetic Algorithms
NASA Technical Reports Server (NTRS)
Globus, Al; Langhirt, Eric; Livny, Miron; Ramamurthy, Ravishankar; Soloman, Marvin; Traugott, Steve
2000-01-01
A genetic algorithm code, JavaGenes, was written in Java and used to evolve pharmaceutical drug molecules and digital circuits. JavaGenes was run under the Condor cycle-scavenging batch system managing 100-170 desktop SGI workstations. Genetic algorithms mimic biological evolution by evolving solutions to problems using crossover and mutation. While most genetic algorithms evolve strings or trees, JavaGenes evolves graphs representing (currently) molecules and circuits. Java was chosen as the implementation language because the genetic algorithm requires random splitting and recombining of graphs, a complex data structure manipulation with ample opportunities for memory leaks, loose pointers, out-of-bound indices, and other hard to find bugs. Java garbage-collection memory management, lack of pointer arithmetic, and array-bounds index checking prevents these bugs from occurring, substantially reducing development time. While a run-time performance penalty must be paid, the only unacceptable performance we encountered was using standard Java serialization to checkpoint and restart the code. This was fixed by a two-day implementation of custom checkpointing. JavaGenes is minimally integrated with Condor; in other words, JavaGenes must do its own checkpointing and I/O redirection. A prototype Java-aware version of Condor was developed using standard Java serialization for checkpointing. For the prototype to be useful, standard Java serialization must be significantly optimized. JavaGenes is approximately 8700 lines of code and a few thousand JavaGenes jobs have been run. Most jobs ran for a few days. Results include proof that genetic algorithms can evolve directed and undirected graphs, development of a novel crossover operator for graphs, a paper in the journal Nanotechnology, and another paper in preparation.
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Nguyen, Hai Van; Finkelstein, Eric Andrew; Mital, Shweta; Gardner, Daphne Su-Lyn
2017-11-01
Offering genetic testing for Maturity Onset Diabetes of the Young (MODY) to all young patients with type 2 diabetes has been shown to be not cost-effective. This study tests whether a novel algorithm-driven genetic testing strategy for MODY is incrementally cost-effective relative to the setting of no testing. A decision tree was constructed to estimate the costs and effectiveness of the algorithm-driven MODY testing strategy and a strategy of no genetic testing over a 30-year time horizon from a payer's perspective. The algorithm uses glutamic acid decarboxylase (GAD) antibody testing (negative antibodies), age of onset of diabetes (<45 years) and body mass index (<25 kg/m 2 if diagnosed >30 years) to stratify the population of patients with diabetes into three subgroups, and testing for MODY only among the subgroup most likely to have the mutation. Singapore-specific costs and prevalence of MODY obtained from local studies and utility values sourced from the literature are used to populate the model. The algorithm-driven MODY testing strategy has an incremental cost-effectiveness ratio of US$93 663 per quality-adjusted life year relative to the no testing strategy. If the price of genetic testing falls from US$1050 to US$530 (a 50% decrease), it will become cost-effective. Our proposed algorithm-driven testing strategy for MODY is not yet cost-effective based on established benchmarks. However, as genetic testing prices continue to fall, this strategy is likely to become cost-effective in the near future. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
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Genetic Algorithms for Multiple-Choice Problems
NASA Astrophysics Data System (ADS)
Aickelin, Uwe
2010-04-01
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.
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A synthetic genetic edge detection program.
Tabor, Jeffrey J; Salis, Howard M; Simpson, Zachary Booth; Chevalier, Aaron A; Levskaya, Anselm; Marcotte, Edward M; Voigt, Christopher A; Ellington, Andrew D
2009-06-26
Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E. coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.
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A Synthetic Genetic Edge Detection Program
Tabor, Jeffrey J.; Salis, Howard; Simpson, Zachary B.; Chevalier, Aaron A.; Levskaya, Anselm; Marcotte, Edward M.; Voigt, Christopher A.; Ellington, Andrew D.
2009-01-01
Summary Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks. PMID:19563759
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Constrained minimization of smooth functions using a genetic algorithm
NASA Technical Reports Server (NTRS)
Moerder, Daniel D.; Pamadi, Bandu N.
1994-01-01
The use of genetic algorithms for minimization of differentiable functions that are subject to differentiable constraints is considered. A technique is demonstrated for converting the solution of the necessary conditions for a constrained minimum into an unconstrained function minimization. This technique is extended as a global constrained optimization algorithm. The theory is applied to calculating minimum-fuel ascent control settings for an energy state model of an aerospace plane.
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Experimental test of genetic rescue in isolated populations of brook trout
Robinson, Zachary L.; Coombs, Jason A.; Hudy, Mark; Nislow, Keith H.; Letcher, Benjamin H.; Whiteley, Andrew R.
2017-01-01
Genetic rescue is an increasingly considered conservation measure to address genetic erosion associated with habitat loss and fragmentation. The resulting gene flow from facilitating migration may improve fitness and adaptive potential, but is not without risks (e.g., outbreeding depression). Here, we conducted a test of genetic rescue by translocating ten (five of each sex) brook trout (Salvelinus fontinalis) from a single source to four nearby and isolated stream populations. To control for the demographic contribution of translocated individuals, ten resident individuals (five of each sex) were removed from each recipient population. Prior to the introduction of translocated individuals, the two smallest above-barrier populations had substantially lower genetic diversity, and all populations had reduced effective number of breeders relative to adjacent below-barrier populations. In the first reproductive bout following translocation, 31 of 40 (78%) translocated individuals reproduced successfully. Translocated individuals contributed to more families than expected under random mating and generally produced larger full-sibling families. We observed relatively high (>20%) introgression in three of the four recipient populations. The translocations increased genetic diversity of recipient populations by 45% in allelic richness and 25% in expected heterozygosity. Additionally, strong evidence of hybrid vigour was observed through significantly larger body sizes of hybrid offspring relative to resident offspring in all recipient populations. Continued monitoring of these populations will test for negative fitness effects beyond the first generation. However, these results provide much-needed experimental data to inform the potential effectiveness of genetic rescue-motivated translocations.
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Experimental test of genetic rescue in isolated populations of brook trout.
Robinson, Zachary L; Coombs, Jason A; Hudy, Mark; Nislow, Keith H; Letcher, Benjamin H; Whiteley, Andrew R
2017-09-01
Genetic rescue is an increasingly considered conservation measure to address genetic erosion associated with habitat loss and fragmentation. The resulting gene flow from facilitating migration may improve fitness and adaptive potential, but is not without risks (e.g., outbreeding depression). Here, we conducted a test of genetic rescue by translocating ten (five of each sex) brook trout (Salvelinus fontinalis) from a single source to four nearby and isolated stream populations. To control for the demographic contribution of translocated individuals, ten resident individuals (five of each sex) were removed from each recipient population. Prior to the introduction of translocated individuals, the two smallest above-barrier populations had substantially lower genetic diversity, and all populations had reduced effective number of breeders relative to adjacent below-barrier populations. In the first reproductive bout following translocation, 31 of 40 (78%) translocated individuals reproduced successfully. Translocated individuals contributed to more families than expected under random mating and generally produced larger full-sibling families. We observed relatively high (>20%) introgression in three of the four recipient populations. The translocations increased genetic diversity of recipient populations by 45% in allelic richness and 25% in expected heterozygosity. Additionally, strong evidence of hybrid vigour was observed through significantly larger body sizes of hybrid offspring relative to resident offspring in all recipient populations. Continued monitoring of these populations will test for negative fitness effects beyond the first generation. However, these results provide much-needed experimental data to inform the potential effectiveness of genetic rescue-motivated translocations. © 2017 John Wiley & Sons Ltd.
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Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm
NASA Technical Reports Server (NTRS)
Baskaran, Subbiah; Noever, D.
1999-01-01
Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.
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Study of genetic direct search algorithms for function optimization
NASA Technical Reports Server (NTRS)
Zeigler, B. P.
1974-01-01
The results are presented of a study to determine the performance of genetic direct search algorithms in solving function optimization problems arising in the optimal and adaptive control areas. The findings indicate that: (1) genetic algorithms can outperform standard algorithms in multimodal and/or noisy optimization situations, but suffer from lack of gradient exploitation facilities when gradient information can be utilized to guide the search. (2) For large populations, or low dimensional function spaces, mutation is a sufficient operator. However for small populations or high dimensional functions, crossover applied in about equal frequency with mutation is an optimum combination. (3) Complexity, in terms of storage space and running time, is significantly increased when population size is increased or the inversion operator, or the second level adaptation routine is added to the basic structure.
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An Adaptive Immune Genetic Algorithm for Edge Detection
NASA Astrophysics Data System (ADS)
Li, Ying; Bai, Bendu; Zhang, Yanning
An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.
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Kin discrimination via odour in the cooperatively breeding banded mongoose.
Mitchell, J; Kyabulima, S; Businge, R; Cant, M A; Nichols, H J
2018-03-01
Kin discrimination is often beneficial for group-living animals as it aids in inbreeding avoidance and providing nepotistic help. In mammals, the use of olfactory cues in kin discrimination is widespread and may occur through learning the scents of individuals that are likely to be relatives, or by assessing genetic relatedness directly through assessing odour similarity (phenotype matching). We use scent presentations to investigate these possibilities in a wild population of the banded mongoose Mungos mungo , a cooperative breeder in which inbreeding risk is high and females breed communally, disrupting behavioural cues to kinship. We find that adults show heightened behavioural responses to unfamiliar (extra-group) scents than to familiar (within-group) scents. Interestingly, we found that responses to familiar odours, but not unfamiliar odours, varied with relatedness. This suggests that banded mongooses are either able to use an effective behavioural rule to identify likely relatives from within their group, or that phenotype matching is used in the context of within-group kin recognition but not extra-group kin recognition. In other cooperative breeders, familiarity is used within the group and phenotype matching may be used to identify unfamiliar kin. However, for the banded mongoose this pattern may be reversed, most likely due to their unusual breeding system which disrupts within-group behavioural cues to kinship.
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Adult survival of Black-legged Kittiwakes Rissa tridactyla in a Pacific colony
Hatch, Scott A.; Roberts, Bay D.; Fadely, Brian S.
1993-01-01
Breeding Black-legged Kittiwakes Rissa tridactyla survived at a mean annual rate of 0.926 in four years at a colony in Alaska. Survival rates observed in sexed males (0.930) and females (0.937) did not differ significantly. The rate of return among nonbreeding Kittiwakes (0.839) was lower than that of known breeders, presumably because more nonbreeders moved away from the study plots where they were marked. Individual nonbreeders frequented sites up to 5 km apart on the same island, while a few established breeders moved up to 2.5 km between years. Mate retention in breeding Kittiwakes averaged 69% in three years. Among pairs that split, the cause of changing mates was about equally divided between death (46%) and divorce (54%). Average adult life expectancy was estimated at 13.0 years. Combined with annual productivity averaging 0.17 chick per nest, the observed survival was insufficient for maintaining population size. Rather, an irregular decline observed in the study colony since 1981 is consistent with the model of a closed population with little or no recruitment. Compared to their Atlantic counterparts, Pacific Kittiwakes have low productivity and high survival. The question arises whether differences reflect phenotypic plasticity or genetically determined variation in population parameters.
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Staphylococcus agnetis, a potential pathogen in broiler breeders.
Poulsen, Louise Ladefoged; Thøfner, Ida; Bisgaard, Magne; Olsen, Rikke Heidemann; Christensen, Jens Peter; Christensen, Henrik
2017-12-01
In this study, four broiler parent flocks have been followed from the onset of the production period (week 20) until slaughter (week 60). Every week, approximately ten dead broiler breeders, randomly selected among birds dead on their own, were collected and subjected to a full post mortem analysis including bacteriological examination. In total 997 breeders were investigated and for the first time Staphylococcus agnetis was isolated in pure culture from cases of endocarditis and septicemia from 16 broiler breeders. In addition, the cloacal flora from newly hatched chickens originating from the same four flocks were characterized and S. agnetis was found in pure culture of several newly hatched chickens (n=12) and only in one case in combination with another species. Clonality of the isolates was examined by pulsed-field-gel-electrophoresis which showed indistinguishable patterns in isolates from both broiler breeders and broilers. Three isolates were whole genome sequenced to obtain knowledge on virulence genes. The isolates harbored a number of genes encoding different fibrinogen binding proteins and toxins which might be important for virulence. The present findings demonstrate that S. agnetis may be associated with mortality in broiler breeders. No disease was associated with the broilers which were found positive for S. agnetis in the cloaca. Copyright © 2017 Elsevier B.V. All rights reserved.
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Convergence properties of simple genetic algorithms
NASA Technical Reports Server (NTRS)
Bethke, A. D.; Zeigler, B. P.; Strauss, D. M.
1974-01-01
The essential parameters determining the behaviour of genetic algorithms were investigated. Computer runs were made while systematically varying the parameter values. Results based on the progress curves obtained from these runs are presented along with results based on the variability of the population as the run progresses.
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A genetic algorithm approach in interface and surface structure optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Zhang, Jian
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less
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NASA Astrophysics Data System (ADS)
Braiek, A.; Adili, A.; Albouchi, F.; Karkri, M.; Ben Nasrallah, S.
2016-06-01
The aim of this work is to simultaneously identify the conductive and radiative parameters of a semitransparent sample using a photothermal method associated with an inverse problem. The identification of the conductive and radiative proprieties is performed by the minimization of an objective function that represents the errors between calculated temperature and measured signal. The calculated temperature is obtained from a theoretical model built with the thermal quadrupole formalism. Measurement is obtained in the rear face of the sample whose front face is excited by a crenel of heat flux. For identification procedure, a genetic algorithm is developed and used. The genetic algorithm is a useful tool in the simultaneous estimation of correlated or nearly correlated parameters, which can be a limiting factor for the gradient-based methods. The results of the identification procedure show the efficiency and the stability of the genetic algorithm to simultaneously estimate the conductive and radiative properties of clear glass.
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An application of CART algorithm in genetics: IGFs and cGH polymorphisms in Japanese quail
NASA Astrophysics Data System (ADS)
Kaplan, Selçuk
2017-04-01
The avian insulin-like growth factor-1 (IGFs) and avian growth hormone (cGH) genes are the most important genes that can affect bird performance traits because of its important function in growth and metabolism. Understanding the molecular genetic basis of variation in growth-related traits is of importance for continued improvement and increased rates of genetic gain. The objective of the present study was to identify polymorphisms of cGH and IGFs genes in Japanese quail using conventional least square method (LSM) and CART algorithm. Therefore, this study was aimed to demonstrate at determining the polymorphisms of two genes related growth characteristics via CART algorithm. A simulated data set was generated to analyze by adhering the results of some poultry genetic studies which it includes live weights at 5 weeks of age, 3 alleles and 6 genotypes of cGH and 2 alleles and 3 genotypes of IGFs. As a result, it has been determined that the CART algorithm has some advantages as for that LSM.
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Application of artificial intelligence to search ground-state geometry of clusters
NASA Astrophysics Data System (ADS)
Lemes, Maurício Ruv; Marim, L. R.; dal Pino, A.
2002-08-01
We introduce a global optimization procedure, the neural-assisted genetic algorithm (NAGA). It combines the power of an artificial neural network (ANN) with the versatility of the genetic algorithm. This method is suitable to solve optimization problems that depend on some kind of heuristics to limit the search space. If a reasonable amount of data is available, the ANN can ``understand'' the problem and provide the genetic algorithm with a selected population of elements that will speed up the search for the optimum solution. We tested the method in a search for the ground-state geometry of silicon clusters. We trained the ANN with information about the geometry and energetics of small silicon clusters. Next, the ANN learned how to restrict the configurational space for larger silicon clusters. For Si10 and Si20, we noticed that the NAGA is at least three times faster than the ``pure'' genetic algorithm. As the size of the cluster increases, it is expected that the gain in terms of time will increase as well.
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Application of genetic algorithms to focal mechanism determination
NASA Astrophysics Data System (ADS)
Kobayashi, Reiji; Nakanishi, Ichiro
1994-04-01
Genetic algorithms are a new class of methods for global optimization. They resemble Monte Carlo techniques, but search for solutions more efficiently than uniform Monte Carlo sampling. In the field of geophysics, genetic algorithms have recently been used to solve some non-linear inverse problems (e.g., earthquake location, waveform inversion, migration velocity estimation). We present an application of genetic algorithms to focal mechanism determination from first-motion polarities of P-waves and apply our method to two recent large events, the Kushiro-oki earthquake of January 15, 1993 and the SW Hokkaido (Japan Sea) earthquake of July 12, 1993. Initial solution and curvature information of the objective function that gradient methods need are not required in our approach. Moreover globally optimal solutions can be efficiently obtained. Calculation of polarities based on double-couple models is the most time-consuming part of the source mechanism determination. The amount of calculations required by the method designed in this study is much less than that of previous grid search methods.
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NASA Astrophysics Data System (ADS)
Lu, Lin; Chang, Yunlong; Li, Yingmin; Lu, Ming
2013-05-01
An orthogonal experiment was conducted by the means of multivariate nonlinear regression equation to adjust the influence of external transverse magnetic field and Ar flow rate on welding quality in the process of welding condenser pipe by high-speed argon tungsten-arc welding (TIG for short). The magnetic induction and flow rate of Ar gas were used as optimum variables, and tensile strength of weld was set to objective function on the base of genetic algorithm theory, and then an optimal design was conducted. According to the request of physical production, the optimum variables were restrained. The genetic algorithm in the MATLAB was used for computing. A comparison between optimum results and experiment parameters was made. The results showed that the optimum technologic parameters could be chosen by the means of genetic algorithm with the conditions of excessive optimum variables in the process of high-speed welding. And optimum technologic parameters of welding coincided with experiment results.
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Optimal sensor placement for spatial lattice structure based on genetic algorithms
NASA Astrophysics Data System (ADS)
Liu, Wei; Gao, Wei-cheng; Sun, Yi; Xu, Min-jian
2008-10-01
Optimal sensor placement technique plays a key role in structural health monitoring of spatial lattice structures. This paper considers the problem of locating sensors on a spatial lattice structure with the aim of maximizing the data information so that structural dynamic behavior can be fully characterized. Based on the criterion of optimal sensor placement for modal test, an improved genetic algorithm is introduced to find the optimal placement of sensors. The modal strain energy (MSE) and the modal assurance criterion (MAC) have been taken as the fitness function, respectively, so that three placement designs were produced. The decimal two-dimension array coding method instead of binary coding method is proposed to code the solution. Forced mutation operator is introduced when the identical genes appear via the crossover procedure. A computational simulation of a 12-bay plain truss model has been implemented to demonstrate the feasibility of the three optimal algorithms above. The obtained optimal sensor placements using the improved genetic algorithm are compared with those gained by exiting genetic algorithm using the binary coding method. Further the comparison criterion based on the mean square error between the finite element method (FEM) mode shapes and the Guyan expansion mode shapes identified by data-driven stochastic subspace identification (SSI-DATA) method are employed to demonstrate the advantage of the different fitness function. The results showed that some innovations in genetic algorithm proposed in this paper can enlarge the genes storage and improve the convergence of the algorithm. More importantly, the three optimal sensor placement methods can all provide the reliable results and identify the vibration characteristics of the 12-bay plain truss model accurately.
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Cui, Peng; Hou, Yuansheng; Tang, Mingjie; Zhang, Haiting; Zuohua, Yuanchun; Yin, Zuohua; Li, Tianxian; Guo, Shan; Xing, Zhi; He, Yubang; Prosser, Diann J.; Newman, Scott H.; Takekawa, John Y.; Yan, Baoping; Lei, Fumin
2011-01-01
The highly pathogenic avian influenza (HPAI) H5N1 outbreak at Qinghai Lake, China, in 2005 caused the death of over 6,000 migratory birds, half of which were Bar-headed Geese Anser indicus. Understanding the movements of this species may inform monitoring of outbreak risks for HPAI viruses; thus, we investigated the movement patterns of 29 Bar-headed Geese at Qinghai Lake, China during 2007 and 2008 by using high resolution GPS satellite telemetry. We described the movements and distribution of marked Bar-headed Geese during the pre-nesting, nesting, and moulting periods. Of 21 Bar-headed Geese with complete transmission records, 3 moved to other areas during the nesting period: 2 to Jianghe wetland (50 km northwest of Qinghai Lake) and 1 to Cuolongka Lake (220 km northwest of Qinghai Lake) during the nesting period. We identified nesting attempts of 7 of the marked geese at Qinghai Lake. Four completed successful nesting attempts according to our rules of judgment for the breeding status, and 2 geese lost broods soon after hatching (hereafter referred to as unsuccessful breeders). Of 18 geese present at Qinghai Lake during the nesting period, 9 (6 non-breeders, 2 successful breeders and 1 unsuccessful breeder) remained at Qinghai Lake during the moulting period; and 9 (5 non-breeders, 4 unsuccessful breeders) left Qinghai Lake for moulting. Kuhai Lake, Donggeicuona Lake, Alake Lake, Zhaling-Eling Lake area and Huangheyuan wetland area were used as moulting sites. Geese that moulted at Qinghai Lake, Cuolongka Lake, Kuhai Lake, Donggeicuona Lake and Alake Lake also moved to Zhaling-Eling Lake area or Huangheyuan wetland area and stayed there for several days prior to autumn migration. Mean home range and core area estimates did not differ significantly by sex, year and between breeders and non-breeders.
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Cui, P.; Hou, Y.; Tang, M.; Zhang, H.; Zhou, Y.; Yin, Z.; Li, T.; Guo, S.; Xing, Z.; He, Y.; Prosser, D.J.; Newman, S.H.; Takekawa, John Y.; Yan, B.; Lei, F.
2011-01-01
The highly pathogenic avian influenza (HPAI) H5N1 outbreak at Qinghai Lake, China, in 2005 caused the death of over 6,000 migratory birds, half of which were Bar-headed Geese Anser indicus. Understanding the movements of this species may inform monitoring of outbreak risks for HPAI viruses; thus, we investigated the movement patterns of 29 Bar-headed Geese at Qinghai Lake, China during 2007 and 2008 by using high resolution GPS satellite telemetry. We described the movements and distribution of marked Bar-headed Geese during the pre-nesting, nesting, and moulting periods. Of 21 Bar-headed Geese with complete transmission records, 3 moved to other areas during the nesting period: 2 to Jianghe wetland (50 km northwest of Qinghai Lake) and 1 to Cuolongka Lake (220 km northwest of Qinghai Lake) during the nesting period. We identified nesting attempts of 7 of the marked geese at Qinghai Lake. Four completed successful nesting attempts according to our rules of judgment for the breeding status, and 2 geese lost broods soon after hatching (hereafter referred to as unsuccessful breeders). Of 18 geese present at Qinghai Lake during the nesting period, 9 (6 non-breeders, 2 successful breeders and 1 unsuccessful breeder) remained at Qinghai Lake during the moulting period; and 9 (5 non-breeders, 4 unsuccessful breeders) left Qinghai Lake for moulting. Kuhai Lake, Donggeicuona Lake, Alake Lake, Zhaling-Eling Lake area and Huangheyuan wetland area were used as moulting sites. Geese that moulted at Qinghai Lake, Cuolongka Lake, Kuhai Lake, Donggeicuona Lake and Alake Lake also moved to Zhaling-Eling Lake area or Huangheyuan wetland area and stayed there for several days prior to autumn migration. Mean home range and core area estimates did not differ significantly by sex, year and between breeders and non-breeders. ?? 2010 Dt. Ornithologen-Gesellschaft e.V.
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Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.
Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert
2011-03-01
This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.
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Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters
NASA Astrophysics Data System (ADS)
Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.
2014-01-01
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.
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Prioritizing the Components of Vulnerability: A Genetic Algorithm Minimization of Flood Risk
NASA Astrophysics Data System (ADS)
Bongolan, Vena Pearl; Ballesteros, Florencio; Baritua, Karessa Alexandra; Junne Santos, Marie
2013-04-01
We define a flood resistant city as an optimal arrangement of communities according to their traits, with the goal of minimizing the flooding vulnerability via a genetic algorithm. We prioritize the different components of flooding vulnerability, giving each component a weight, thus expressing vulnerability as a weighted sum. This serves as the fitness function for the genetic algorithm. We also allowed non-linear interactions among related but independent components, viz, poverty and mortality rate, and literacy and radio/ tv penetration. The designs produced reflect the relative importance of the components, and we observed a synchronicity between the interacting components, giving us a more consistent design.
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Algorithmic Trading with Developmental and Linear Genetic Programming
NASA Astrophysics Data System (ADS)
Wilson, Garnett; Banzhaf, Wolfgang
A developmental co-evolutionary genetic programming approach (PAM DGP) and a standard linear genetic programming (LGP) stock trading systemare applied to a number of stocks across market sectors. Both GP techniques were found to be robust to market fluctuations and reactive to opportunities associated with stock price rise and fall, with PAMDGP generating notably greater profit in some stock trend scenarios. Both algorithms were very accurate at buying to achieve profit and selling to protect assets, while exhibiting bothmoderate trading activity and the ability to maximize or minimize investment as appropriate. The content of the trading rules produced by both algorithms are also examined in relation to stock price trend scenarios.
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NASA Astrophysics Data System (ADS)
Shen, Yanqing
2018-04-01
LiFePO4 battery is developed rapidly in electric vehicle, whose safety and functional capabilities are influenced greatly by the evaluation of available cell capacity. Added with adaptive switch mechanism, this paper advances a supervised chaos genetic algorithm based state of charge determination method, where a combined state space model is employed to simulate battery dynamics. The method is validated by the experiment data collected from battery test system. Results indicate that the supervised chaos genetic algorithm based state of charge determination method shows great performance with less computation complexity and is little influenced by the unknown initial cell state.
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Moore, J H
1995-06-01
A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.
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An Efficient Functional Test Generation Method For Processors Using Genetic Algorithms
NASA Astrophysics Data System (ADS)
Hudec, Ján; Gramatová, Elena
2015-07-01
The paper presents a new functional test generation method for processors testing based on genetic algorithms and evolutionary strategies. The tests are generated over an instruction set architecture and a processor description. Such functional tests belong to the software-oriented testing. Quality of the tests is evaluated by code coverage of the processor description using simulation. The presented test generation method uses VHDL models of processors and the professional simulator ModelSim. The rules, parameters and fitness functions were defined for various genetic algorithms used in automatic test generation. Functionality and effectiveness were evaluated using the RISC type processor DP32.
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Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem
NASA Astrophysics Data System (ADS)
Rahmalia, Dinita
2017-08-01
Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.
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Fast optimization of glide vehicle reentry trajectory based on genetic algorithm
NASA Astrophysics Data System (ADS)
Jia, Jun; Dong, Ruixing; Yuan, Xuejun; Wang, Chuangwei
2018-02-01
An optimization method of reentry trajectory based on genetic algorithm is presented to meet the need of reentry trajectory optimization for glide vehicle. The dynamic model for the glide vehicle during reentry period is established. Considering the constraints of heat flux, dynamic pressure, overload etc., the optimization of reentry trajectory is investigated by utilizing genetic algorithm. The simulation shows that the method presented by this paper is effective for the optimization of reentry trajectory of glide vehicle. The efficiency and speed of this method is comparative with the references. Optimization results meet all constraints, and the on-line fast optimization is potential by pre-processing the offline samples.
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On Directly Solving SCHRÖDINGER Equation for H+2 Ion by Genetic Algorithm
NASA Astrophysics Data System (ADS)
Saha, Rajendra; Bhattacharyya, S. P.
Schrödinger equation (SE) is sought to be solved directly for the ground state of H+2 ion by invoking genetic algorithm (GA). In one approach the internuclear distance (R) is kept fixed, the corresponding electronic SE for H+2 is solved by GA at each R and the full potential energy curve (PEC) is constructed. The minimum of the PEC is then located giving Ve and Re. Alternatively, Ve and Re are located in a single run by allowing R to vary simultaneously while solving the electronic SE by genetic algorithm. The performance patterns of the two strategies are compared.
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Applying a Genetic Algorithm to Reconfigurable Hardware
NASA Technical Reports Server (NTRS)
Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim
2004-01-01
This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.
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Mobile transporter path planning
NASA Technical Reports Server (NTRS)
Baffes, Paul; Wang, Lui
1990-01-01
The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.
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Miah, Gous; Rafii, Mohd Y.; Ismail, Mohd R.; Puteh, Adam B.; Rahim, Harun A.; Islam, Kh. Nurul; Latif, Mohammad Abdul
2013-01-01
Over the last few decades, the use of molecular markers has played an increasing role in rice breeding and genetics. Of the different types of molecular markers, microsatellites have been utilized most extensively, because they can be readily amplified by PCR and the large amount of allelic variation at each locus. Microsatellites are also known as simple sequence repeats (SSR), and they are typically composed of 1–6 nucleotide repeats. These markers are abundant, distributed throughout the genome and are highly polymorphic compared with other genetic markers, as well as being species-specific and co-dominant. For these reasons, they have become increasingly important genetic markers in rice breeding programs. The evolution of new biotypes of pests and diseases as well as the pressures of climate change pose serious challenges to rice breeders, who would like to increase rice production by introducing resistance to multiple biotic and abiotic stresses. Recent advances in rice genomics have now made it possible to identify and map a number of genes through linkage to existing DNA markers. Among the more noteworthy examples of genes that have been tightly linked to molecular markers in rice are those that confer resistance or tolerance to blast. Therefore, in combination with conventional breeding approaches, marker-assisted selection (MAS) can be used to monitor the presence or lack of these genes in breeding populations. For example, marker-assisted backcross breeding has been used to integrate important genes with significant biological effects into a number of commonly grown rice varieties. The use of cost-effective, finely mapped microsatellite markers and MAS strategies should provide opportunities for breeders to develop high-yield, blast resistance rice cultivars. The aim of this review is to summarize the current knowledge concerning the linkage of microsatellite markers to rice blast resistance genes, as well as to explore the use of MAS in rice breeding programs aimed at improving blast resistance in this species. We also discuss the various advantages, disadvantages and uses of microsatellite markers relative to other molecular marker types. PMID:24240810
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Genetic algorithms and their use in Geophysical Problems
DOE Office of Scientific and Technical Information (OSTI.GOV)
Parker, Paul B.
1999-04-01
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or ''fittest'' models from a ''population'' and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show thatmore » certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Optimal efficiency is usually achieved with smaller (< 50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (> 2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.« less
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Genetic algorithms and their use in geophysical problems
NASA Astrophysics Data System (ADS)
Parker, Paul Bradley
Genetic algorithms (GAs), global optimization methods that mimic Darwinian evolution are well suited to the nonlinear inverse problems of geophysics. A standard genetic algorithm selects the best or "fittest" models from a "population" and then applies operators such as crossover and mutation in order to combine the most successful characteristics of each model and produce fitter models. More sophisticated operators have been developed, but the standard GA usually provides a robust and efficient search. Although the choice of parameter settings such as crossover and mutation rate may depend largely on the type of problem being solved, numerous results show that certain parameter settings produce optimal performance for a wide range of problems and difficulties. In particular, a low (about half of the inverse of the population size) mutation rate is crucial for optimal results, but the choice of crossover method and rate do not seem to affect performance appreciably. Also, optimal efficiency is usually achieved with smaller (<50) populations. Lastly, tournament selection appears to be the best choice of selection methods due to its simplicity and its autoscaling properties. However, if a proportional selection method is used such as roulette wheel selection, fitness scaling is a necessity, and a high scaling factor (>2.0) should be used for the best performance. Three case studies are presented in which genetic algorithms are used to invert for crustal parameters. The first is an inversion for basement depth at Yucca mountain using gravity data, the second an inversion for velocity structure in the crust of the south island of New Zealand using receiver functions derived from teleseismic events, and the third is a similar receiver function inversion for crustal velocities beneath the Mendocino Triple Junction region of Northern California. The inversions demonstrate that genetic algorithms are effective in solving problems with reasonably large numbers of free parameters and with computationally expensive objective function calculations. More sophisticated techniques are presented for special problems. Niching and island model algorithms are introduced as methods to find multiple, distinct solutions to the nonunique problems that are typically seen in geophysics. Finally, hybrid algorithms are investigated as a way to improve the efficiency of the standard genetic algorithm.
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Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida
By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...
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Longest jobs first algorithm in solving job shop scheduling using adaptive genetic algorithm (GA)
NASA Astrophysics Data System (ADS)
Alizadeh Sahzabi, Vahid; Karimi, Iman; Alizadeh Sahzabi, Navid; Mamaani Barnaghi, Peiman
2012-01-01
In this paper, genetic algorithm was used to solve job shop scheduling problems. One example discussed in JSSP (Job Shop Scheduling Problem) and I described how we can solve such these problems by genetic algorithm. The goal in JSSP is to gain the shortest process time. Furthermore I proposed a method to obtain best performance on performing all jobs in shortest time. The method mainly, is according to Genetic algorithm (GA) and crossing over between parents always follows the rule which the longest process is at the first in the job queue. In the other word chromosomes is suggested to sorts based on the longest processes to shortest i.e. "longest job first" says firstly look which machine contains most processing time during its performing all its jobs and that is the bottleneck. Secondly, start sort those jobs which are belonging to that specific machine descending. Based on the achieved results," longest jobs first" is the optimized status in job shop scheduling problems. In our results the accuracy would grow up to 94.7% for total processing time and the method improved 4% the accuracy of performing all jobs in the presented example.
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Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo
2018-06-25
The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.
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Optimization of beam orientation in radiotherapy using planar geometry
NASA Astrophysics Data System (ADS)
Haas, O. C. L.; Burnham, K. J.; Mills, J. A.
1998-08-01
This paper proposes a new geometrical formulation of the coplanar beam orientation problem combined with a hybrid multiobjective genetic algorithm. The approach is demonstrated by optimizing the beam orientation in two dimensions, with the objectives being formulated using planar geometry. The traditional formulation of the objectives associated with the organs at risk has been modified to account for the use of complex dose delivery techniques such as beam intensity modulation. The new algorithm attempts to replicate the approach of a treatment planner whilst reducing the amount of computation required. Hybrid genetic search operators have been developed to improve the performance of the genetic algorithm by exploiting problem-specific features. The multiobjective genetic algorithm is formulated around the concept of Pareto optimality which enables the algorithm to search in parallel for different objectives. When the approach is applied without constraining the number of beams, the solution produces an indication of the minimum number of beams required. It is also possible to obtain non-dominated solutions for various numbers of beams, thereby giving the clinicians a choice in terms of the number of beams as well as in the orientation of these beams.
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Distributed query plan generation using multiobjective genetic algorithm.
Panicker, Shina; Kumar, T V Vijay
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.
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Acoustic Impedance Inversion of Seismic Data Using Genetic Algorithm
NASA Astrophysics Data System (ADS)
Eladj, Said; Djarfour, Noureddine; Ferahtia, Djalal; Ouadfeul, Sid-Ali
2013-04-01
The inversion of seismic data can be used to constrain estimates of the Earth's acoustic impedance structure. This kind of problem is usually known to be non-linear, high-dimensional, with a complex search space which may be riddled with many local minima, and results in irregular objective functions. We investigate here the performance and the application of a genetic algorithm, in the inversion of seismic data. The proposed algorithm has the advantage of being easily implemented without getting stuck in local minima. The effects of population size, Elitism strategy, uniform cross-over and lower mutation are examined. The optimum solution parameters and performance were decided as a function of the testing error convergence with respect to the generation number. To calculate the fitness function, we used L2 norm of the sample-to-sample difference between the reference and the inverted trace. The cross-over probability is of 0.9-0.95 and mutation has been tested at 0.01 probability. The application of such a genetic algorithm to synthetic data shows that the inverted acoustic impedance section was efficient. Keywords: Seismic, Inversion, acoustic impedance, genetic algorithm, fitness functions, cross-over, mutation.
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Distributed Query Plan Generation Using Multiobjective Genetic Algorithm
Panicker, Shina; Vijay Kumar, T. V.
2014-01-01
A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513
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Genetic algorithm enhanced by machine learning in dynamic aperture optimization
DOE Office of Scientific and Technical Information (OSTI.GOV)
Li, Yongjun; Cheng, Weixing; Yu, Li Hua
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less
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Genetic algorithm enhanced by machine learning in dynamic aperture optimization
NASA Astrophysics Data System (ADS)
Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert
2018-05-01
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
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Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...
2018-05-29
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less
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Hybrid algorithms for fuzzy reverse supply chain network design.
Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.
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Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design
Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.
2014-01-01
In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057
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An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.
Yoon, Yourim; Kim, Yong-Hyuk
2013-10-01
Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.
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Comparison of Genetic Algorithm and Hill Climbing for Shortest Path Optimization Mapping
NASA Astrophysics Data System (ADS)
Fronita, Mona; Gernowo, Rahmat; Gunawan, Vincencius
2018-02-01
Traveling Salesman Problem (TSP) is an optimization to find the shortest path to reach several destinations in one trip without passing through the same city and back again to the early departure city, the process is applied to the delivery systems. This comparison is done using two methods, namely optimization genetic algorithm and hill climbing. Hill Climbing works by directly selecting a new path that is exchanged with the neighbour's to get the track distance smaller than the previous track, without testing. Genetic algorithms depend on the input parameters, they are the number of population, the probability of crossover, mutation probability and the number of generations. To simplify the process of determining the shortest path supported by the development of software that uses the google map API. Tests carried out as much as 20 times with the number of city 8, 16, 24 and 32 to see which method is optimal in terms of distance and time computation. Based on experiments conducted with a number of cities 3, 4, 5 and 6 producing the same value and optimal distance for the genetic algorithm and hill climbing, the value of this distance begins to differ with the number of city 7. The overall results shows that these tests, hill climbing are more optimal to number of small cities and the number of cities over 30 optimized using genetic algorithms.
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Weather prediction using a genetic memory
NASA Technical Reports Server (NTRS)
Rogers, David
1990-01-01
Kanaerva's sparse distributed memory (SDM) is an associative memory model based on the mathematical properties of high dimensional binary address spaces. Holland's genetic algorithms are a search technique for high dimensional spaces inspired by evolutional processes of DNA. Genetic Memory is a hybrid of the above two systems, in which the memory uses a genetic algorithm to dynamically reconfigure its physical storage locations to reflect correlations between the stored addresses and data. This architecture is designed to maximize the ability of the system to scale-up to handle real world problems.
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Exploding the myths about the fast breeder reactor
DOE Office of Scientific and Technical Information (OSTI.GOV)
Burns, S.
1979-01-01
This paper discusses the facts and figures about the effects of conservation policies, the benefits of the Clinch River Breeder Reactor demonstration plant, the feasibility of nuclear weapons manufacture from reactor-grade plutonium, diversion of plutonium from nuclear plants, radioactive waste disposal, and the toxicity of plutonium. The paper concludes that the U.S. is not proceeding with a high confidence strategy for breeder development because of a variety of false assumptions.
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Development of Inspection and Repair Technology for Heat Exchanger Tubes in Fast Breeder Reactors
2009-06-01
Technology for Heat Exchanger Tubes in Fast Breeder Reactors Akihiko NISHIMURA *1 , Takahisa SHOBU, Kiyoshi OKA, Toshihiko YAMAGUCHI, Yukihiro SHIMADA...fast breeder reactors (FBRs). It comprises a laser processing head combined with an eddy current testing unit. Ultrashort laser pulse ablation is used...be applied in the main- tenance of large structures such as nuclear reactors and chemical factories [1]. Internal access to a blanket cooling pipe
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Moir, R.W.
1982-02-22
The fusion breeder is a fusion reactor designed with special blankets to maximize the transmutation by 14 MeV neutrons of uranium-238 to plutonium or thorium to uranium-233 for use as a fuel for fission reactors. Breeding fissile fuels has not been a goal of the US fusion energy program. This paper suggests it is time for a policy change to make the fusion breeder a goal of the US fusion program and the US nuclear energy program. The purpose of this paper is to suggest this policy change be made and tell why it should be made, and to outlinemore » specific research and development goals so that the fusion breeder will be developed in time to meet fissile fuel needs.« less
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Moir, R.W.
1982-04-20
The fusion breeder is a fusion reactor designed with special blankets to maximize the transmutation by 14 MeV neutrons of uranium-238 to plutonium or thorium to uranium-233 for use as a fuel for fission reactors. Breeding fissile fuels has not been a goal of the US fusion energy program. This paper suggests it is time for a policy change to make the fusion breeder a goal of the US fusion program and the US nuclear energy program. The purpose of this paper is to suggest this policy change be made and tell why it should be made, and to outlinemore » specific research and development goals so that the fusion breeder will be developed in time to meet fissile fuel needs.« less
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iNJclust: Iterative Neighbor-Joining Tree Clustering Framework for Inferring Population Structure.
Limpiti, Tulaya; Amornbunchornvej, Chainarong; Intarapanich, Apichart; Assawamakin, Anunchai; Tongsima, Sissades
2014-01-01
Understanding genetic differences among populations is one of the most important issues in population genetics. Genetic variations, e.g., single nucleotide polymorphisms, are used to characterize commonality and difference of individuals from various populations. This paper presents an efficient graph-based clustering framework which operates iteratively on the Neighbor-Joining (NJ) tree called the iNJclust algorithm. The framework uses well-known genetic measurements, namely the allele-sharing distance, the neighbor-joining tree, and the fixation index. The behavior of the fixation index is utilized in the algorithm's stopping criterion. The algorithm provides an estimated number of populations, individual assignments, and relationships between populations as outputs. The clustering result is reported in the form of a binary tree, whose terminal nodes represent the final inferred populations and the tree structure preserves the genetic relationships among them. The clustering performance and the robustness of the proposed algorithm are tested extensively using simulated and real data sets from bovine, sheep, and human populations. The result indicates that the number of populations within each data set is reasonably estimated, the individual assignment is robust, and the structure of the inferred population tree corresponds to the intrinsic relationships among populations within the data.
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Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming
NASA Astrophysics Data System (ADS)
Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita
2018-03-01
We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.
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Jamie Marie Marranca,; Amy Welsh,; Roseman, Edward F.
2015-01-01
Lake sturgeon (Acipenser fulvescens) have experienced significant habitat loss, resulting in reduced population sizes. Three artificial reefs were built in the Huron-Erie corridor in the Great Lakes to replace lost spawning habitat. Genetic data were collected to determine the source and numbers of adult lake sturgeon spawning on the reefs and to determine if the founder effect resulted in reduced genetic diversity. DNA was extracted from larval tail clips and 12 microsatellite loci were amplified. Larval genotypes were then compared to 22 previously studied spawning lake sturgeon populations in the Great Lakes to determine the source of the parental population. The effective number of breeders (Nb) was calculated for each reef cohort. The larval genotypes were then compared to the source population to determine if there were any losses in genetic diversity that are indicative of the founder effect. The St. Clair and Detroit River adult populations were found to be the source parental population for the larvae collected on all three artificial reefs. There were large numbers of contributing adults relative to the number of sampled larvae. There was no significant difference between levels of genetic diversity in the source population and larval samples from the artificial reefs; however, there is some evidence for a genetic bottleneck in the reef populations likely due to the founder effect. Habitat restoration in the Huron-Erie corridor is likely resulting in increased habitat for the large lake sturgeon population in the system and in maintenance of the population's genetic diversity.
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Rutledge, L Y; Garroway, C J; Loveless, K M; Patterson, B R
2010-12-01
Distinguishing genetically differentiated populations within hybrid zones and determining the mechanisms by which introgression occurs are crucial for setting effective conservation policy. Extensive hybridization among grey wolves (Canis lupus), eastern wolves (C. lycaon) and coyotes (C. latrans) in eastern North America has blurred species distinctions, creating a Canis hybrid swarm. Using complementary genetic markers, we tested the hypotheses that eastern wolves have acted as a conduit of sex-biased gene flow between grey wolves and coyotes, and that eastern wolves in Algonquin Provincial Park (APP) have differentiated following a history of introgression. Mitochondrial, Y chromosome and autosomal microsatellite genetic data provided genotypes for 217 canids from three geographic regions in Ontario, Canada: northeastern Ontario, APP and southern Ontario. Coyote mitochondrial DNA (mtDNA) haplotypes were common across regions but coyote-specific Y chromosome haplotypes were absent; grey wolf mtDNA was absent from southern regions, whereas grey wolf Y chromosome haplotypes were present in all three regions. Genetic structuring analyses revealed three distinct clusters within a genetic cline, suggesting some gene flow among species. In APP, however, 78.4% of all breeders and 11 of 15 known breeding pairs had assignment probability of Q0.8 to the Algonquin cluster, and the proportion of eastern wolf Y chromosome haplotypes in APP breeding males was higher than expected from random mating within the park (P<0.02). The data indicate that Algonquin wolves remain genetically distinct despite providing a sex-biased genetic bridge between coyotes and grey wolves. We speculate that ongoing hybridization within the park is limited by pre-mating reproductive barriers.
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Eltaher, Shamseldeen; Sallam, Ahmed; Belamkar, Vikas; Emara, Hamdy A; Nower, Ahmed A; Salem, Khaled F M; Poland, Jesse; Baenziger, Peter S
2018-01-01
The availability of information on the genetic diversity and population structure in wheat ( Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F 3:6 ) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon's information index ( I ) = 0.494, diversity index ( h ) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity ( I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars.
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Eltaher, Shamseldeen; Sallam, Ahmed; Belamkar, Vikas; Emara, Hamdy A.; Nower, Ahmed A.; Salem, Khaled F. M.; Poland, Jesse; Baenziger, Peter S.
2018-01-01
The availability of information on the genetic diversity and population structure in wheat (Triticum aestivum L.) breeding lines will help wheat breeders to better use their genetic resources and manage genetic variation in their breeding program. The recent advances in sequencing technology provide the opportunity to identify tens or hundreds of thousands of single nucleotide polymorphism (SNPs) in large genome species (e.g., wheat). These SNPs can be utilized for understanding genetic diversity and performing genome wide association studies (GWAS) for complex traits. In this study, the genetic diversity and population structure were investigated in a set of 230 genotypes (F3:6) derived from various crosses as a prerequisite for GWAS and genomic selection. Genotyping-by-sequencing provided 25,566 high-quality SNPs. The polymorphism information content (PIC) across chromosomes ranged from 0.09 to 0.37 with an average of 0.23. The distribution of SNPs markers on the 21 chromosomes ranged from 319 on chromosome 3D to 2,370 on chromosome 3B. The analysis of population structure revealed three subpopulations (G1, G2, and G3). Analysis of molecular variance identified 8% variance among and 92% within subpopulations. Of the three subpopulations, G2 had the highest level of genetic diversity based on three genetic diversity indices: Shannon’s information index (I) = 0.494, diversity index (h) = 0.328 and unbiased diversity index (uh) = 0.331, while G3 had lowest level of genetic diversity (I = 0.348, h = 0.226 and uh = 0.236). This high genetic diversity identified among the subpopulations can be used to develop new wheat cultivars. PMID:29593779
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Gobin, Oliver C; Schüth, Ferdi
2008-01-01
Genetic algorithms are widely used to solve and optimize combinatorial problems and are more often applied for library design in combinatorial chemistry. Because of their flexibility, however, their implementation can be challenging. In this study, the influence of the representation of solid catalysts on the performance of genetic algorithms was systematically investigated on the basis of a new, constrained, multiobjective, combinatorial test problem with properties common to problems in combinatorial materials science. Constraints were satisfied by penalty functions, repair algorithms, or special representations. The tests were performed using three state-of-the-art evolutionary multiobjective algorithms by performing 100 optimization runs for each algorithm and test case. Experimental data obtained during the optimization of a noble metal-free solid catalyst system active in the selective catalytic reduction of nitric oxide with propene was used to build up a predictive model to validate the results of the theoretical test problem. A significant influence of the representation on the optimization performance was observed. Binary encodings were found to be the preferred encoding in most of the cases, and depending on the experimental test unit, repair algorithms or penalty functions performed best.
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NASA Astrophysics Data System (ADS)
Yan, Mingfei; Hu, Huasi; Otake, Yoshie; Taketani, Atsushi; Wakabayashi, Yasuo; Yanagimachi, Shinzo; Wang, Sheng; Pan, Ziheng; Hu, Guang
2018-05-01
Thermal neutron computer tomography (CT) is a useful tool for visualizing two-phase flow due to its high imaging contrast and strong penetrability of neutrons for tube walls constructed with metallic material. A novel approach for two-phase flow CT reconstruction based on an improved adaptive genetic algorithm with sparsity constraint (IAGA-SC) is proposed in this paper. In the algorithm, the neighborhood mutation operator is used to ensure the continuity of the reconstructed object. The adaptive crossover probability P c and mutation probability P m are improved to help the adaptive genetic algorithm (AGA) achieve the global optimum. The reconstructed results for projection data, obtained from Monte Carlo simulation, indicate that the comprehensive performance of the IAGA-SC algorithm exceeds the adaptive steepest descent-projection onto convex sets (ASD-POCS) algorithm in restoring typical and complex flow regimes. It especially shows great advantages in restoring the simply connected flow regimes and the shape of object. In addition, the CT experiment for two-phase flow phantoms was conducted on the accelerator-driven neutron source to verify the performance of the developed IAGA-SC algorithm.
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Creating IRT-Based Parallel Test Forms Using the Genetic Algorithm Method
ERIC Educational Resources Information Center
Sun, Koun-Tem; Chen, Yu-Jen; Tsai, Shu-Yen; Cheng, Chien-Fen
2008-01-01
In educational measurement, the construction of parallel test forms is often a combinatorial optimization problem that involves the time-consuming selection of items to construct tests having approximately the same test information functions (TIFs) and constraints. This article proposes a novel method, genetic algorithm (GA), to construct parallel…
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Genetic Algorithm Phase Retrieval for the Systematic Image-Based Optical Alignment Testbed
NASA Technical Reports Server (NTRS)
Rakoczy, John; Steincamp, James; Taylor, Jaime
2003-01-01
A reduced surrogate, one point crossover genetic algorithm with random rank-based selection was used successfully to estimate the multiple phases of a segmented optical system modeled on the seven-mirror Systematic Image-Based Optical Alignment testbed located at NASA's Marshall Space Flight Center.
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By integrating Genetic Algorithm and MODFLOW2005, an optimizing tool is developed to characterize the aquifer system of Region II, Northwest Florida. The history and the newest available observation data of the aquifer system is fitted automatically by using the numerical model c...
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Genetic algorithm to solve the problems of lectures and practicums scheduling
NASA Astrophysics Data System (ADS)
Syahputra, M. F.; Apriani, R.; Sawaluddin; Abdullah, D.; Albra, W.; Heikal, M.; Abdurrahman, A.; Khaddafi, M.
2018-02-01
Generally, the scheduling process is done manually. However, this method has a low accuracy level, along with possibilities that a scheduled process collides with another scheduled process. When doing theory class and practicum timetable scheduling process, there are numerous problems, such as lecturer teaching schedule collision, schedule collision with another schedule, practicum lesson schedules that collides with theory class, and the number of classrooms available. In this research, genetic algorithm is implemented to perform theory class and practicum timetable scheduling process. The algorithm will be used to process the data containing lists of lecturers, courses, and class rooms, obtained from information technology department at University of Sumatera Utara. The result of scheduling process using genetic algorithm is the most optimal timetable that conforms to available time slots, class rooms, courses, and lecturer schedules.
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Multiple feature fusion via covariance matrix for visual tracking
NASA Astrophysics Data System (ADS)
Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui
2018-04-01
Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.
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A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem
Liu, Dong-sheng; Fan, Shu-jiang
2014-01-01
In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. PMID:24688389