Evaluation of the Intel Xeon Phi 7120 and NVIDIA K80 as accelerators for two-dimensional panel codes

PubMed Central

2017-01-01

To optimize the geometry of airfoils for a specific application is an important engineering problem. In this context genetic algorithms have enjoyed some success as they are able to explore the search space without getting stuck in local optima. However, these algorithms require the computation of aerodynamic properties for a significant number of airfoil geometries. Consequently, for low-speed aerodynamics, panel methods are most often used as the inner solver. In this paper we evaluate the performance of such an optimization algorithm on modern accelerators (more specifically, the Intel Xeon Phi 7120 and the NVIDIA K80). For that purpose, we have implemented an optimized version of the algorithm on the CPU and Xeon Phi (based on OpenMP, vectorization, and the Intel MKL library) and on the GPU (based on CUDA and the MAGMA library). We present timing results for all codes and discuss the similarities and differences between the three implementations. Overall, we observe a speedup of approximately 2.5 for adding an Intel Xeon Phi 7120 to a dual socket workstation and a speedup between 3.4 and 3.8 for adding a NVIDIA K80 to a dual socket workstation. PMID:28582389

Evaluation of the Intel Xeon Phi 7120 and NVIDIA K80 as accelerators for two-dimensional panel codes.

PubMed

Einkemmer, Lukas

2017-01-01

To optimize the geometry of airfoils for a specific application is an important engineering problem. In this context genetic algorithms have enjoyed some success as they are able to explore the search space without getting stuck in local optima. However, these algorithms require the computation of aerodynamic properties for a significant number of airfoil geometries. Consequently, for low-speed aerodynamics, panel methods are most often used as the inner solver. In this paper we evaluate the performance of such an optimization algorithm on modern accelerators (more specifically, the Intel Xeon Phi 7120 and the NVIDIA K80). For that purpose, we have implemented an optimized version of the algorithm on the CPU and Xeon Phi (based on OpenMP, vectorization, and the Intel MKL library) and on the GPU (based on CUDA and the MAGMA library). We present timing results for all codes and discuss the similarities and differences between the three implementations. Overall, we observe a speedup of approximately 2.5 for adding an Intel Xeon Phi 7120 to a dual socket workstation and a speedup between 3.4 and 3.8 for adding a NVIDIA K80 to a dual socket workstation.

Library preparation and data analysis packages for rapid genome sequencing.

PubMed

Pomraning, Kyle R; Smith, Kristina M; Bredeweg, Erin L; Connolly, Lanelle R; Phatale, Pallavi A; Freitag, Michael

2012-01-01

High-throughput sequencing (HTS) has quickly become a valuable tool for comparative genetics and genomics and is now regularly carried out in laboratories that are not connected to large sequencing centers. Here we describe an updated version of our protocol for constructing single- and paired-end Illumina sequencing libraries, beginning with purified genomic DNA. The present protocol can also be used for "multiplexing," i.e. the analysis of several samples in a single flowcell lane by generating "barcoded" or "indexed" Illumina sequencing libraries in a way that is independent from Illumina-supported methods. To analyze sequencing results, we suggest several independent approaches but end users should be aware that this is a quickly evolving field and that currently many alignment (or "mapping") and counting algorithms are being developed and tested.

Design of 240,000 orthogonal 25mer DNA barcode probes.

PubMed

Xu, Qikai; Schlabach, Michael R; Hannon, Gregory J; Elledge, Stephen J

2009-02-17

DNA barcodes linked to genetic features greatly facilitate screening these features in pooled formats using microarray hybridization, and new tools are needed to design large sets of barcodes to allow construction of large barcoded mammalian libraries such as shRNA libraries. Here we report a framework for designing large sets of orthogonal barcode probes. We demonstrate the utility of this framework by designing 240,000 barcode probes and testing their performance by hybridization. From the test hybridizations, we also discovered new probe design rules that significantly reduce cross-hybridization after their introduction into the framework of the algorithm. These rules should improve the performance of DNA microarray probe designs for many applications.

Design of 240,000 orthogonal 25mer DNA barcode probes

PubMed Central

Xu, Qikai; Schlabach, Michael R.; Hannon, Gregory J.; Elledge, Stephen J.

2009-01-01

DNA barcodes linked to genetic features greatly facilitate screening these features in pooled formats using microarray hybridization, and new tools are needed to design large sets of barcodes to allow construction of large barcoded mammalian libraries such as shRNA libraries. Here we report a framework for designing large sets of orthogonal barcode probes. We demonstrate the utility of this framework by designing 240,000 barcode probes and testing their performance by hybridization. From the test hybridizations, we also discovered new probe design rules that significantly reduce cross-hybridization after their introduction into the framework of the algorithm. These rules should improve the performance of DNA microarray probe designs for many applications. PMID:19171886

Efficient engineering of chromosomal ribosome binding site libraries in mismatch repair proficient Escherichia coli.

PubMed

Oesterle, Sabine; Gerngross, Daniel; Schmitt, Steven; Roberts, Tania Michelle; Panke, Sven

2017-09-26

Multiplexed gene expression optimization via modulation of gene translation efficiency through ribosome binding site (RBS) engineering is a valuable approach for optimizing artificial properties in bacteria, ranging from genetic circuits to production pathways. Established algorithms design smart RBS-libraries based on a single partially-degenerate sequence that efficiently samples the entire space of translation initiation rates. However, the sequence space that is accessible when integrating the library by CRISPR/Cas9-based genome editing is severely restricted by DNA mismatch repair (MMR) systems. MMR efficiency depends on the type and length of the mismatch and thus effectively removes potential library members from the pool. Rather than working in MMR-deficient strains, which accumulate off-target mutations, or depending on temporary MMR inactivation, which requires additional steps, we eliminate this limitation by developing a pre-selection rule of genome-library-optimized-sequences (GLOS) that enables introducing large functional diversity into MMR-proficient strains with sequences that are no longer subject to MMR-processing. We implement several GLOS-libraries in Escherichia coli and show that GLOS-libraries indeed retain diversity during genome editing and that such libraries can be used in complex genome editing operations such as concomitant deletions. We argue that this approach allows for stable and efficient fine tuning of chromosomal functions with minimal effort.

Newborn Screening: MedlinePlus Health Topic

MedlinePlus

... deficiency (National Library of Medicine) Genetics Home Reference: glutaric acidemia type I (National Library of Medicine) Genetics Home Reference: glutaric acidemia type II (National Library of Medicine) Genetics ...

zipHMMlib: a highly optimised HMM library exploiting repetitions in the input to speed up the forward algorithm.

PubMed

Sand, Andreas; Kristiansen, Martin; Pedersen, Christian N S; Mailund, Thomas

2013-11-22

Hidden Markov models are widely used for genome analysis as they combine ease of modelling with efficient analysis algorithms. Calculating the likelihood of a model using the forward algorithm has worst case time complexity linear in the length of the sequence and quadratic in the number of states in the model. For genome analysis, however, the length runs to millions or billions of observations, and when maximising the likelihood hundreds of evaluations are often needed. A time efficient forward algorithm is therefore a key ingredient in an efficient hidden Markov model library. We have built a software library for efficiently computing the likelihood of a hidden Markov model. The library exploits commonly occurring substrings in the input to reuse computations in the forward algorithm. In a pre-processing step our library identifies common substrings and builds a structure over the computations in the forward algorithm which can be reused. This analysis can be saved between uses of the library and is independent of concrete hidden Markov models so one preprocessing can be used to run a number of different models.Using this library, we achieve up to 78 times shorter wall-clock time for realistic whole-genome analyses with a real and reasonably complex hidden Markov model. In one particular case the analysis was performed in less than 8 minutes compared to 9.6 hours for the previously fastest library. We have implemented the preprocessing procedure and forward algorithm as a C++ library, zipHMM, with Python bindings for use in scripts. The library is available at http://birc.au.dk/software/ziphmm/.

A Library of Optimization Algorithms for Organizational Design

DTIC Science & Technology

2005-01-01

N00014-98-1-0465 and #N00014-00-1-0101 A Library of Optimization Algorithms for Organizational Design Georgiy M. Levchuk Yuri N. Levchuk Jie Luo...E-mail: Krishna@engr.uconn.edu Abstract This paper presents a library of algorithms to solve a broad range of optimization problems arising in the...normative design of organizations to execute a specific mission. The use of specific optimization algorithms for different phases of the design process

Determination of the Spatial Distribution in Hydraulic Conductivity Using Genetic Algorithm Optimization

NASA Astrophysics Data System (ADS)

Aksoy, A.; Lee, J. H.; Kitanidis, P. K.

2016-12-01

Heterogeneity in hydraulic conductivity (K) impacts the transport and fate of contaminants in subsurface as well as design and operation of managed aquifer recharge (MAR) systems. Recently, improvements in computational resources and availability of big data through electrical resistivity tomography (ERT) and remote sensing have provided opportunities to better characterize the subsurface. Yet, there is need to improve prediction and evaluation methods in order to obtain information from field measurements for better field characterization. In this study, genetic algorithm optimization, which has been widely used in optimal aquifer remediation designs, was used to determine the spatial distribution of K. A hypothetical 2 km by 2 km aquifer was considered. A genetic algorithm library, PGAPack, was linked with a fast Fourier transform based random field generator as well as a groundwater flow and contaminant transport simulation model (BIO2D-KE). The objective of the optimization model was to minimize the total squared error between measured and predicted field values. It was assumed measured K values were available through ERT. Performance of genetic algorithm in predicting the distribution of K was tested for different cases. In the first one, it was assumed that observed K values were evaluated using the random field generator only as the forward model. In the second case, as well as K-values obtained through ERT, measured head values were incorporated into evaluation in which BIO2D-KE and random field generator were used as the forward models. Lastly, tracer concentrations were used as additional information in the optimization model. Initial results indicated enhanced performance when random field generator and BIO2D-KE are used in combination in predicting the spatial distribution in K.

Compression and fast retrieval of SNP data.

PubMed

Sambo, Francesco; Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2014-11-01

The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Compression and fast retrieval of SNP data

PubMed Central

Sambo, Francesco; Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

2014-01-01

Motivation: The increasing interest in rare genetic variants and epistatic genetic effects on complex phenotypic traits is currently pushing genome-wide association study design towards datasets of increasing size, both in the number of studied subjects and in the number of genotyped single nucleotide polymorphisms (SNPs). This, in turn, is leading to a compelling need for new methods for compression and fast retrieval of SNP data. Results: We present a novel algorithm and file format for compressing and retrieving SNP data, specifically designed for large-scale association studies. Our algorithm is based on two main ideas: (i) compress linkage disequilibrium blocks in terms of differences with a reference SNP and (ii) compress reference SNPs exploiting information on their call rate and minor allele frequency. Tested on two SNP datasets and compared with several state-of-the-art software tools, our compression algorithm is shown to be competitive in terms of compression rate and to outperform all tools in terms of time to load compressed data. Availability and implementation: Our compression and decompression algorithms are implemented in a C++ library, are released under the GNU General Public License and are freely downloadable from http://www.dei.unipd.it/~sambofra/snpack.html. Contact: sambofra@dei.unipd.it or cobelli@dei.unipd.it. PMID:25064564

A novel process of viral vector barcoding and library preparation enables high-diversity library generation and recombination-free paired-end sequencing

PubMed Central

Davidsson, Marcus; Diaz-Fernandez, Paula; Schwich, Oliver D.; Torroba, Marcos; Wang, Gang; Björklund, Tomas

2016-01-01

Detailed characterization and mapping of oligonucleotide function in vivo is generally a very time consuming effort that only allows for hypothesis driven subsampling of the full sequence to be analysed. Recent advances in deep sequencing together with highly efficient parallel oligonucleotide synthesis and cloning techniques have, however, opened up for entirely new ways to map genetic function in vivo. Here we present a novel, optimized protocol for the generation of universally applicable, barcode labelled, plasmid libraries. The libraries are designed to enable the production of viral vector preparations assessing coding or non-coding RNA function in vivo. When generating high diversity libraries, it is a challenge to achieve efficient cloning, unambiguous barcoding and detailed characterization using low-cost sequencing technologies. With the presented protocol, diversity of above 3 million uniquely barcoded adeno-associated viral (AAV) plasmids can be achieved in a single reaction through a process achievable in any molecular biology laboratory. This approach opens up for a multitude of in vivo assessments from the evaluation of enhancer and promoter regions to the optimization of genome editing. The generated plasmid libraries are also useful for validation of sequencing clustering algorithms and we here validate the newly presented message passing clustering process named Starcode. PMID:27874090

Googling DNA sequences on the World Wide Web.

PubMed

Hajibabaei, Mehrdad; Singer, Gregory A C

2009-11-10

New web-based technologies provide an excellent opportunity for sharing and accessing information and using web as a platform for interaction and collaboration. Although several specialized tools are available for analyzing DNA sequence information, conventional web-based tools have not been utilized for bioinformatics applications. We have developed a novel algorithm and implemented it for searching species-specific genomic sequences, DNA barcodes, by using popular web-based methods such as Google. We developed an alignment independent character based algorithm based on dividing a sequence library (DNA barcodes) and query sequence to words. The actual search is conducted by conventional search tools such as freely available Google Desktop Search. We implemented our algorithm in two exemplar packages. We developed pre and post-processing software to provide customized input and output services, respectively. Our analysis of all publicly available DNA barcode sequences shows a high accuracy as well as rapid results. Our method makes use of conventional web-based technologies for specialized genetic data. It provides a robust and efficient solution for sequence search on the web. The integration of our search method for large-scale sequence libraries such as DNA barcodes provides an excellent web-based tool for accessing this information and linking it to other available categories of information on the web.

Handling the data management needs of high-throughput sequencing data: SpeedGene, a compression algorithm for the efficient storage of genetic data

PubMed Central

2012-01-01

Background As Next-Generation Sequencing data becomes available, existing hardware environments do not provide sufficient storage space and computational power to store and process the data due to their enormous size. This is and will be a frequent problem that is encountered everyday by researchers who are working on genetic data. There are some options available for compressing and storing such data, such as general-purpose compression software, PBAT/PLINK binary format, etc. However, these currently available methods either do not offer sufficient compression rates, or require a great amount of CPU time for decompression and loading every time the data is accessed. Results Here, we propose a novel and simple algorithm for storing such sequencing data. We show that, the compression factor of the algorithm ranges from 16 to several hundreds, which potentially allows SNP data of hundreds of Gigabytes to be stored in hundreds of Megabytes. We provide a C++ implementation of the algorithm, which supports direct loading and parallel loading of the compressed format without requiring extra time for decompression. By applying the algorithm to simulated and real datasets, we show that the algorithm gives greater compression rate than the commonly used compression methods, and the data-loading process takes less time. Also, The C++ library provides direct-data-retrieving functions, which allows the compressed information to be easily accessed by other C++ programs. Conclusions The SpeedGene algorithm enables the storage and the analysis of next generation sequencing data in current hardware environment, making system upgrades unnecessary. PMID:22591016

Novel search algorithms for a mid-infrared spectral library of cotton contaminants.

PubMed

Loudermilk, J Brian; Himmelsbach, David S; Barton, Franklin E; de Haseth, James A

2008-06-01

During harvest, a variety of plant based contaminants are collected along with cotton lint. The USDA previously created a mid-infrared, attenuated total reflection (ATR), Fourier transform infrared (FT-IR) spectral library of cotton contaminants for contaminant identification as the contaminants have negative impacts on yarn quality. This library has shown impressive identification rates for extremely similar cellulose based contaminants in cases where the library was representative of the samples searched. When spectra of contaminant samples from crops grown in different geographic locations, seasons, and conditions and measured with a different spectrometer and accessories were searched, identification rates for standard search algorithms decreased significantly. Six standard algorithms were examined: dot product, correlation, sum of absolute values of differences, sum of the square root of the absolute values of differences, sum of absolute values of differences of derivatives, and sum of squared differences of derivatives. Four categories of contaminants derived from cotton plants were considered: leaf, stem, seed coat, and hull. Experiments revealed that the performance of the standard search algorithms depended upon the category of sample being searched and that different algorithms provided complementary information about sample identity. These results indicated that choosing a single standard algorithm to search the library was not possible. Three voting scheme algorithms based on result frequency, result rank, category frequency, or a combination of these factors for the results returned by the standard algorithms were developed and tested for their capability to overcome the unpredictability of the standard algorithms' performances. The group voting scheme search was based on the number of spectra from each category of samples represented in the library returned in the top ten results of the standard algorithms. This group algorithm was able to identify correctly as many test spectra as the best standard algorithm without relying on human choice to select a standard algorithm to perform the searches.

Uni10: an open-source library for tensor network algorithms

NASA Astrophysics Data System (ADS)

Kao, Ying-Jer; Hsieh, Yun-Da; Chen, Pochung

2015-09-01

We present an object-oriented open-source library for developing tensor network algorithms written in C++ called Uni10. With Uni10, users can build a symmetric tensor from a collection of bonds, while the bonds are constructed from a list of quantum numbers associated with different quantum states. It is easy to label and permute the indices of the tensors and access a block associated with a particular quantum number. Furthermore a network class is used to describe arbitrary tensor network structure and to perform network contractions efficiently. We give an overview of the basic structure of the library and the hierarchy of the classes. We present examples of the construction of a spin-1 Heisenberg Hamiltonian and the implementation of the tensor renormalization group algorithm to illustrate the basic usage of the library. The library described here is particularly well suited to explore and fast prototype novel tensor network algorithms and to implement highly efficient codes for existing algorithms.

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM)

PubMed Central

Skinnider, Michael A.; Dejong, Chris A.; Rees, Philip N.; Johnston, Chad W.; Li, Haoxin; Webster, Andrew L. H.; Wyatt, Morgan A.; Magarvey, Nathan A.

2015-01-01

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/. PMID:26442528

A Web-Based Library and Algorithm System for Satellite and Airborne Image Products

DTIC Science & Technology

2011-06-28

Sequoia Scientific, Inc., and Dr. Paul Bissett at FERI, under other 6.1/6.2 program funding. 2 A Web-Based Library And Algorithm System For...of the spectrum matching approach to inverting hyperspectral imagery created by Drs. C. Mobley ( Sequoia Scientific) and P. Bissett (FERI...algorithms developed by Sequoia Scientific and FERI. Testing and Implementation of Library This project will result in the delivery of a WeoGeo

CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms.

PubMed

Kohlhoff, Kai J; Sosnick, Marc H; Hsu, William T; Pande, Vijay S; Altman, Russ B

2011-08-15

Data clustering techniques are an essential component of a good data analysis toolbox. Many current bioinformatics applications are inherently compute-intense and work with very large datasets. Sequential algorithms are inadequate for providing the necessary performance. For this reason, we have created Clustering Algorithms for Massively Parallel Architectures, Including GPU Nodes (CAMPAIGN), a central resource for data clustering algorithms and tools that are implemented specifically for execution on massively parallel processing architectures. CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs. The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource. New modules from the community will be accepted into the library and the layout of it is such that it can easily be extended to promising future platforms such as OpenCL. Releases of the CAMPAIGN library are freely available for download under the LGPL from https://simtk.org/home/campaign. Source code can also be obtained through anonymous subversion access as described on https://simtk.org/scm/?group_id=453. kjk33@cantab.net.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

NASA Astrophysics Data System (ADS)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

NASA Astrophysics Data System (ADS)

De Geyter, G.; Baes, M.; Fritz, J.; Camps, P.

2013-02-01

We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi- dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

G STL: the geostatistical template library in C++

NASA Astrophysics Data System (ADS)

Remy, Nicolas; Shtuka, Arben; Levy, Bruno; Caers, Jef

2002-10-01

The development of geostatistics has been mostly accomplished by application-oriented engineers in the past 20 years. The focus on concrete applications gave birth to many algorithms and computer programs designed to address different issues, such as estimating or simulating a variable while possibly accounting for secondary information such as seismic data, or integrating geological and geometrical data. At the core of any geostatistical data integration methodology is a well-designed algorithm. Yet, despite their obvious differences, all these algorithms share many commonalities on which to build a geostatistics programming library, lest the resulting library is poorly reusable and difficult to expand. Building on this observation, we design a comprehensive, yet flexible and easily reusable library of geostatistics algorithms in C++. The recent advent of the generic programming paradigm allows us elegantly to express the commonalities of the geostatistical algorithms into computer code. Generic programming, also referred to as "programming with concepts", provides a high level of abstraction without loss of efficiency. This last point is a major gain over object-oriented programming which often trades efficiency for abstraction. It is not enough for a numerical library to be reusable, it also has to be fast. Because generic programming is "programming with concepts", the essential step in the library design is the careful identification and thorough definition of these concepts shared by most geostatistical algorithms. Building on these definitions, a generic and expandable code can be developed. To show the advantages of such a generic library, we use G STL to build two sequential simulation programs working on two different types of grids—a surface with faults and an unstructured grid—without requiring any change to the G STL code.

Robot Vision Library

NASA Technical Reports Server (NTRS)

Howard, Andrew B.; Ansar, Adnan I.; Litwin, Todd E.; Goldberg, Steven B.

2009-01-01

The JPL Robot Vision Library (JPLV) provides real-time robot vision algorithms for developers who are not vision specialists. The package includes algorithms for stereo ranging, visual odometry and unsurveyed camera calibration, and has unique support for very wideangle lenses

Genomes to natural products PRediction Informatics for Secondary Metabolomes (PRISM).

PubMed

Skinnider, Michael A; Dejong, Chris A; Rees, Philip N; Johnston, Chad W; Li, Haoxin; Webster, Andrew L H; Wyatt, Morgan A; Magarvey, Nathan A

2015-11-16

Microbial natural products are an invaluable source of evolved bioactive small molecules and pharmaceutical agents. Next-generation and metagenomic sequencing indicates untapped genomic potential, yet high rediscovery rates of known metabolites increasingly frustrate conventional natural product screening programs. New methods to connect biosynthetic gene clusters to novel chemical scaffolds are therefore critical to enable the targeted discovery of genetically encoded natural products. Here, we present PRISM, a computational resource for the identification of biosynthetic gene clusters, prediction of genetically encoded nonribosomal peptides and type I and II polyketides, and bio- and cheminformatic dereplication of known natural products. PRISM implements novel algorithms which render it uniquely capable of predicting type II polyketides, deoxygenated sugars, and starter units, making it a comprehensive genome-guided chemical structure prediction engine. A library of 57 tailoring reactions is leveraged for combinatorial scaffold library generation when multiple potential substrates are consistent with biosynthetic logic. We compare the accuracy of PRISM to existing genomic analysis platforms. PRISM is an open-source, user-friendly web application available at http://magarveylab.ca/prism/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Job shop scheduling problem with late work criterion

NASA Astrophysics Data System (ADS)

Piroozfard, Hamed; Wong, Kuan Yew

2015-05-01

Scheduling is considered as a key task in many industries, such as project based scheduling, crew scheduling, flight scheduling, machine scheduling, etc. In the machine scheduling area, the job shop scheduling problems are considered to be important and highly complex, in which they are characterized as NP-hard. The job shop scheduling problems with late work criterion and non-preemptive jobs are addressed in this paper. Late work criterion is a fairly new objective function. It is a qualitative measure and concerns with late parts of the jobs, unlike classical objective functions that are quantitative measures. In this work, simulated annealing was presented to solve the scheduling problem. In addition, operation based representation was used to encode the solution, and a neighbourhood search structure was employed to search for the new solutions. The case studies are Lawrence instances that were taken from the Operations Research Library. Computational results of this probabilistic meta-heuristic algorithm were compared with a conventional genetic algorithm, and a conclusion was made based on the algorithm and problem.

GBS 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

2010-09-30

The Umbra gbs (Graph-Based Search) library provides implementations of graph-based search/planning algorithms that can be applied to legacy graph data structures. Unlike some other graph algorithm libraries, this one does not require your graph class to inherit from a specific base class. Implementations of Dijkstra's Algorithm and A-Star search are included and can be used with graphs that are lazily-constructed.

Matrix Multiplication Algorithm Selection with Support Vector Machines

DTIC Science & Technology

2015-05-01

libraries that could intelligently choose the optimal algorithm for a particular set of inputs. Users would be oblivious to the underlying algorithmic...SAT.” J. Artif . Intell. Res.(JAIR), vol. 32, pp. 565–606, 2008. [9] M. G. Lagoudakis and M. L. Littman, “Algorithm selection using reinforcement...Artificial Intelligence , vol. 21, no. 05, pp. 961–976, 2007. [15] C.-C. Chang and C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM

The role of medical libraries in undergraduate education: a case study in genetics*

PubMed Central

Tennant, Michele R.; Miyamoto, Michael M.

2002-01-01

Between 1996 and 2001, the Health Science Center Libraries and Department of Zoology at the University of Florida partnered to provide a cohesive and comprehensive learning experience to undergraduate students in PCB3063, “Genetics.” During one semester each year, a librarian worked with up to 120 undergraduates, providing bibliographic and database instruction in the tools that practicing geneticists use (MEDLINE, GenBank, BLAST, etc.). Students learned to evaluate and synthesize the information that they retrieved, coupling it with information provided in classroom lectures, thus resulting in well-researched short papers on an assigned genetics topic. Exit surveys of students indicated that the majority found the library sessions and librarian's instruction to be useful. Responses also indicated that the project facilitated increased understanding of genetics concepts and appreciation for the scientific research process and the relevance of genetics to the real world. The library benefited from this partnership on a variety of fronts, including the development of skilled library users, pretrained future clientele, and increased visibility among campus research laboratories. The course and associated information instruction and assigned projects can be considered models for course-integrated instruction and the role of medical libraries in undergraduate education. PMID:11999176

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms.

PubMed

Sze, Sing-Hoi; Parrott, Jonathan J; Tarone, Aaron M

2017-12-06

While the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies. We develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies. Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

Spectral unmixing of urban land cover using a generic library approach

NASA Astrophysics Data System (ADS)

Degerickx, Jeroen; Lordache, Marian-Daniel; Okujeni, Akpona; Hermy, Martin; van der Linden, Sebastian; Somers, Ben

2016-10-01

Remote sensing based land cover classification in urban areas generally requires the use of subpixel classification algorithms to take into account the high spatial heterogeneity. These spectral unmixing techniques often rely on spectral libraries, i.e. collections of pure material spectra (endmembers, EM), which ideally cover the large EM variability typically present in urban scenes. Despite the advent of several (semi-) automated EM detection algorithms, the collection of such image-specific libraries remains a tedious and time-consuming task. As an alternative, we suggest the use of a generic urban EM library, containing material spectra under varying conditions, acquired from different locations and sensors. This approach requires an efficient EM selection technique, capable of only selecting those spectra relevant for a specific image. In this paper, we evaluate and compare the potential of different existing library pruning algorithms (Iterative Endmember Selection and MUSIC) using simulated hyperspectral (APEX) data of the Brussels metropolitan area. In addition, we develop a new hybrid EM selection method which is shown to be highly efficient in dealing with both imagespecific and generic libraries, subsequently yielding more robust land cover classification results compared to existing methods. Future research will include further optimization of the proposed algorithm and additional tests on both simulated and real hyperspectral data.

Genetic algorithms

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.

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.

ChemHTPS - A virtual high-throughput screening program suite for the chemical and materials sciences

NASA Astrophysics Data System (ADS)

Afzal, Mohammad Atif Faiz; Evangelista, William; Hachmann, Johannes

The discovery of new compounds, materials, and chemical reactions with exceptional properties is the key for the grand challenges in innovation, energy and sustainability. This process can be dramatically accelerated by means of the virtual high-throughput screening (HTPS) of large-scale candidate libraries. The resulting data can further be used to study the underlying structure-property relationships and thus facilitate rational design capability. This approach has been extensively used for many years in the drug discovery community. However, the lack of openly available virtual HTPS tools is limiting the use of these techniques in various other applications such as photovoltaics, optoelectronics, and catalysis. Thus, we developed ChemHTPS, a general-purpose, comprehensive and user-friendly suite, that will allow users to efficiently perform large in silico modeling studies and high-throughput analyses in these applications. ChemHTPS also includes a massively parallel molecular library generator which offers a multitude of options to customize and restrict the scope of the enumerated chemical space and thus tailor it for the demands of specific applications. To streamline the non-combinatorial exploration of chemical space, we incorporate genetic algorithms into the framework. In addition to implementing smarter algorithms, we also focus on the ease of use, workflow, and code integration to make this technology more accessible to the community.

A portable MPI-based parallel vector template library

NASA Technical Reports Server (NTRS)

Sheffler, Thomas J.

1995-01-01

This paper discusses the design and implementation of a polymorphic collection library for distributed address-space parallel computers. The library provides a data-parallel programming model for C++ by providing three main components: a single generic collection class, generic algorithms over collections, and generic algebraic combining functions. Collection elements are the fourth component of a program written using the library and may be either of the built-in types of C or of user-defined types. Many ideas are borrowed from the Standard Template Library (STL) of C++, although a restricted programming model is proposed because of the distributed address-space memory model assumed. Whereas the STL provides standard collections and implementations of algorithms for uniprocessors, this paper advocates standardizing interfaces that may be customized for different parallel computers. Just as the STL attempts to increase programmer productivity through code reuse, a similar standard for parallel computers could provide programmers with a standard set of algorithms portable across many different architectures. The efficacy of this approach is verified by examining performance data collected from an initial implementation of the library running on an IBM SP-2 and an Intel Paragon.

A Portable MPI-Based Parallel Vector Template Library

NASA Technical Reports Server (NTRS)

Sheffler, Thomas J.

1995-01-01

This paper discusses the design and implementation of a polymorphic collection library for distributed address-space parallel computers. The library provides a data-parallel programming model for C + + by providing three main components: a single generic collection class, generic algorithms over collections, and generic algebraic combining functions. Collection elements are the fourth component of a program written using the library and may be either of the built-in types of c or of user-defined types. Many ideas are borrowed from the Standard Template Library (STL) of C++, although a restricted programming model is proposed because of the distributed address-space memory model assumed. Whereas the STL provides standard collections and implementations of algorithms for uniprocessors, this paper advocates standardizing interfaces that may be customized for different parallel computers. Just as the STL attempts to increase programmer productivity through code reuse, a similar standard for parallel computers could provide programmers with a standard set of algorithms portable across many different architectures. The efficacy of this approach is verified by examining performance data collected from an initial implementation of the library running on an IBM SP-2 and an Intel Paragon.

Benchmarking for Bayesian Reinforcement Learning

PubMed Central

Ernst, Damien; Couëtoux, Adrien

2016-01-01

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the collected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but the benchmarks used to compare them are only relevant for specific cases. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test problems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed. PMID:27304891

Benchmarking for Bayesian Reinforcement Learning.

PubMed

Castronovo, Michael; Ernst, Damien; Couëtoux, Adrien; Fonteneau, Raphael

2016-01-01

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the collected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but the benchmarks used to compare them are only relevant for specific cases. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test problems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed.

Evaluation of microsatellite loci from libraries derived from the wild diploid 'Calcutta 4' and 'Ouro' banana cultivars.

PubMed

Silva, P R O; Jesus, O N J; Creste, S; Figueira, A; Amorim, E P; Ferreira, C F

2015-09-25

Microsatellite markers have been widely used in the quantification of genetic variability and for genetic breeding in Musa spp. The objective of the present study was to evaluate the discriminatory power of microsatellite markers derived from 'Calcutta 4' and 'Ouro' genomic libraries, and to analyze the genetic variability among 30 banana accessions. Thirty-eight markers were used: 15 from the 'Ouro' library and 23 from the 'Calcutta 4' library. Genetic diversity was evaluated by considering SSR markers as both dominant markers because of the presence of triploid accessions, and co-dominant markers. For the dominant analysis, polymorphism information content (PIC) values for 44 polymorphic markers ranged from 0.063 to 0.533, with a mean value of 0.24. A dendrogram analysis separated the BGB-Banana accessions into 4 groups: the 'Ouro' and 'Muísa Tia' accessions were the most dissimilar (93% dissimilarity), while the most similar accessions were 'Pacovan' and 'Walha'. The mean genetic distance between samples was 0.74. For the analysis considering SSR markers as co-dominants, using only diploid accessions, two groups were separated based on their genome contents (A and B). The PIC values for the markers from the 'Calcutta 4' library varied from 0.4836 to 0.7886, whereas those from the 'Ouro' library ranged from 0.3800 to 0.7521. Given the high PIC values, the markers from both the libraries showed high discriminatory power, and can therefore be widely applied for analysis of genetic diversity, population structures, and linkage mapping in Musa spp.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stevens, K; Huang, T; Buttler, D

We present the C-Cat Wordnet package, an open source library for using and modifying Wordnet. The package includes four key features: an API for modifying Synsets; implementations of standard similarity metrics, implementations of well known Word Sense Disambiguation algorithms, and an implementation of the Castanet algorithm. The library is easily extendible and usable in many runtime environments. We demonstrate it's use on two standard Word Sense Disambiguation tasks and apply the Castanet algorithm to a corpus.

Recombination walking: genetic selection of clones from pooled libraries of yeast artificial chromosomes by homologous recombination.

PubMed Central

Miller, A M; Savinelli, E A; Couture, S M; Hannigan, G M; Han, Z; Selden, R F; Treco, D A

1993-01-01

Recombination walking is based on the genetic selection of specific human clones from a yeast artificial chromosome (YAC) library by homologous recombination. The desired clone is selected from a pooled (unordered) YAC library, eliminating labor-intensive steps typically used in organizing and maintaining ordered YAC libraries. Recombination walking represents an efficient approach to library screening and is well suited for chromosome-walking approaches to the isolation of genes associated with common diseases. Images Fig. 1 Fig. 2 Fig. 3 Fig. 4 PMID:8367472

A basic analysis toolkit for biological sequences

PubMed Central

Giancarlo, Raffaele; Siragusa, Alessandro; Siragusa, Enrico; Utro, Filippo

2007-01-01

This paper presents a software library, nicknamed BATS, for some basic sequence analysis tasks. Namely, local alignments, via approximate string matching, and global alignments, via longest common subsequence and alignments with affine and concave gap cost functions. Moreover, it also supports filtering operations to select strings from a set and establish their statistical significance, via z-score computation. None of the algorithms is new, but although they are generally regarded as fundamental for sequence analysis, they have not been implemented in a single and consistent software package, as we do here. Therefore, our main contribution is to fill this gap between algorithmic theory and practice by providing an extensible and easy to use software library that includes algorithms for the mentioned string matching and alignment problems. The library consists of C/C++ library functions as well as Perl library functions. It can be interfaced with Bioperl and can also be used as a stand-alone system with a GUI. The software is available at under the GNU GPL. PMID:17877802

Web-Based Library and Algorithm System for Satellite and Airborne Image Products

DTIC Science & Technology

2011-01-01

the spectrum matching approach to inverting hyperspectral imagery created by Drs. C. Mobley ( Sequoia Scientific) and P. Bissett (FERI). 5...matching algorithms developed by Sequoia Scientific and FERI. Testing and Implementation of Library This project will result in the delivery of a...transitioning VSW algorithms developed by Dr. Curtis D. Mobley at Sequoia Scientific, Inc., and Dr. Paul Bissett at FERI, under other 6.1/6.2 program funding.

A phasing and imputation method for pedigreed populations that results in a single-stage genomic evaluation

PubMed Central

2012-01-01

Background Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective. An algorithm that imputes alleles or allele probabilities for all animals in the pedigree and for all genotyped single nucleotide polymorphisms (SNP) provides a framework to combine all pedigree, genomic, and phenotypic information into a single-stage genomic evaluation. Methods An algorithm was developed for imputation of genotypes in pedigreed populations that allows imputation for completely ungenotyped animals and for low-density genotyped animals, accommodates a wide variety of pedigree structures for genotyped animals, imputes unmapped SNP, and works for large datasets. The method involves simple phasing rules, long-range phasing and haplotype library imputation and segregation analysis. Results Imputation accuracy was high and computational cost was feasible for datasets with pedigrees of up to 25 000 animals. The resulting single-stage genomic evaluation increased the accuracy of estimated genomic breeding values compared to a scenario in which phenotypes on relatives that were not genotyped were ignored. Conclusions The developed imputation algorithm and software and the resulting single-stage genomic evaluation method provide powerful new ways to exploit imputation and to obtain more accurate genetic evaluations. PMID:22462519

Functional annotation of chemical libraries across diverse biological processes.

PubMed

Piotrowski, Jeff S; Li, Sheena C; Deshpande, Raamesh; Simpkins, Scott W; Nelson, Justin; Yashiroda, Yoko; Barber, Jacqueline M; Safizadeh, Hamid; Wilson, Erin; Okada, Hiroki; Gebre, Abraham A; Kubo, Karen; Torres, Nikko P; LeBlanc, Marissa A; Andrusiak, Kerry; Okamoto, Reika; Yoshimura, Mami; DeRango-Adem, Eva; van Leeuwen, Jolanda; Shirahige, Katsuhiko; Baryshnikova, Anastasia; Brown, Grant W; Hirano, Hiroyuki; Costanzo, Michael; Andrews, Brenda; Ohya, Yoshikazu; Osada, Hiroyuki; Yoshida, Minoru; Myers, Chad L; Boone, Charles

2017-09-01

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Integrating digital topology in image-processing libraries.

PubMed

Lamy, Julien

2007-01-01

This paper describes a method to integrate digital topology informations in image-processing libraries. This additional information allows a library user to write algorithms respecting topological constraints, for example, a seed fill or a skeletonization algorithm. As digital topology is absent from most image-processing libraries, such constraints cannot be fulfilled. We describe and give code samples for all the structures necessary for this integration, and show a use case in the form of a homotopic thinning filter inside ITK. The obtained filter can be up to a hundred times as fast as ITK's thinning filter and works for any image dimension. This paper mainly deals of integration within ITK, but can be adapted with only minor modifications to other image-processing libraries.

Amber Plug-In for Protein Shop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliva, Ricardo

2004-05-10

The Amber Plug-in for ProteinShop has two main components: an AmberEngine library to compute the protein energy models, and a module to solve the energy minimization problem using an optimization algorithm in the OPTI-+ library. Together, these components allow the visualization of the protein folding process in ProteinShop. AmberEngine is a object-oriented library to compute molecular energies based on the Amber model. The main class is called ProteinEnergy. Its main interface methods are (1) "init" to initialize internal variables needed to compute the energy. (2) "eval" to evaluate the total energy given a vector of coordinates. Additional methods allow themore » user to evaluate the individual components of the energy model (bond, angle, dihedral, non-bonded-1-4, and non-bonded energies) and to obtain the energy of each individual atom. The Amber Engine library source code includes examples and test routines that illustrate the use of the library in stand alone programs. The energy minimization module uses the AmberEngine library and the nonlinear optimization library OPT++. OPT++ is open source software available under the GNU Lesser General Public License. The minimization module currently makes use of the LBFGS optimization algorithm in OPT++ to perform the energy minimization. Future releases may give the user a choice of other algorithms available in OPT++.« less

Extending a Tandem Mass Spectral Library to Include MS2 Spectra of Fragment Ions Produced In-Source and MSn Spectra.

PubMed

Yang, Xiaoyu; Neta, Pedatsur; Stein, Stephen E

2017-11-01

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS 2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS 3 and MS 4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS 1 scans for an analyte acquired during an infusion experiment. The MS 2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS 2 spectra of the original precursors and of the in-source fragments as well as the MS n spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ.

Developing Information Power Grid Based Algorithms and Software

NASA Technical Reports Server (NTRS)

Dongarra, Jack

1998-01-01

This exploratory study initiated our effort to understand performance modeling on parallel systems. The basic goal of performance modeling is to understand and predict the performance of a computer program or set of programs on a computer system. Performance modeling has numerous applications, including evaluation of algorithms, optimization of code implementations, parallel library development, comparison of system architectures, parallel system design, and procurement of new systems. Our work lays the basis for the construction of parallel libraries that allow for the reconstruction of application codes on several distinct architectures so as to assure performance portability. Following our strategy, once the requirements of applications are well understood, one can then construct a library in a layered fashion. The top level of this library will consist of architecture-independent geometric, numerical, and symbolic algorithms that are needed by the sample of applications. These routines should be written in a language that is portable across the targeted architectures.

Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism.

PubMed

Bhandarkar, S M; Chirravuri, S; Arnold, J

1996-01-01

Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is usually isomorphic to the NP-complete Optimal Linear Arrangement problem. Parallel SIMD and MIMD algorithms for simulated annealing based on Markov chain distribution are proposed and applied to the problem of chromosome reconstruction via clone ordering. Perturbation methods and problem-specific annealing heuristics are proposed and described. The SIMD algorithms are implemented on a 2048 processor MasPar MP-2 system which is an SIMD 2-D toroidal mesh architecture whereas the MIMD algorithms are implemented on an 8 processor Intel iPSC/860 which is an MIMD hypercube architecture. A comparative analysis of the various SIMD and MIMD algorithms is presented in which the convergence, speedup, and scalability characteristics of the various algorithms are analyzed and discussed. On a fine-grained, massively parallel SIMD architecture with a low synchronization overhead such as the MasPar MP-2, a parallel simulated annealing algorithm based on multiple periodically interacting searches performs the best. For a coarse-grained MIMD architecture with high synchronization overhead such as the Intel iPSC/860, a parallel simulated annealing algorithm based on multiple independent searches yields the best results. In either case, distribution of clonal data across multiple processors is shown to exacerbate the tendency of the parallel simulated annealing algorithm to get trapped in a local optimum.

Research on fast Fourier transforms algorithm of huge remote sensing image technology with GPU and partitioning technology.

PubMed

Yang, Xue; Li, Xue-You; Li, Jia-Guo; Ma, Jun; Zhang, Li; Yang, Jan; Du, Quan-Ye

2014-02-01

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

Site-directed protein recombination as a shortest-path problem.

PubMed

Endelman, Jeffrey B; Silberg, Jonathan J; Wang, Zhen-Gang; Arnold, Frances H

2004-07-01

Protein function can be tuned using laboratory evolution, in which one rapidly searches through a library of proteins for the properties of interest. In site-directed recombination, n crossovers are chosen in an alignment of p parents to define a set of p(n + 1) peptide fragments. These fragments are then assembled combinatorially to create a library of p(n+1) proteins. We have developed a computational algorithm to enrich these libraries in folded proteins while maintaining an appropriate level of diversity for evolution. For a given set of parents, our algorithm selects crossovers that minimize the average energy of the library, subject to constraints on the length of each fragment. This problem is equivalent to finding the shortest path between nodes in a network, for which the global minimum can be found efficiently. Our algorithm has a running time of O(N(3)p(2) + N(2)n) for a protein of length N. Adjusting the constraints on fragment length generates a set of optimized libraries with varying degrees of diversity. By comparing these optima for different sets of parents, we rapidly determine which parents yield the lowest energy libraries.

Postprocessing of Voxel-Based Topologies for Additive Manufacturing Using the Computational Geometry Algorithms Library (CGAL)

DTIC Science & Technology

2015-06-01

10-2014 to 00-11-2014 4. TITLE AND SUBTITLE Postprocessing of Voxel-Based Topologies for Additive Manufacturing Using the Computational Geometry...ABSTRACT Postprocessing of 3-dimensional (3-D) topologies that are defined as a set of voxels using the Computational Geometry Algorithms Library (CGAL... computational geometry algorithms, several of which are suited to the task. The work flow described in this report involves first defining a set of

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.

Quantitative synthesis of genetically encoded glycopeptide libraries displayed on M13 phage.

PubMed

Ng, Simon; Jafari, Mohammad R; Matochko, Wadim L; Derda, Ratmir

2012-09-21

Phage display is a powerful technology that enables the discovery of peptide ligands for many targets. Chemical modification of phage libraries have allowed the identification of ligands with properties not encountered in natural polypeptides. In this report, we demonstrated the synthesis of 2 × 10(8) genetically encoded glycopeptides from a commercially available phage-displayed peptide library (Ph.D.-7) in a two-step, one-pot reaction in <1.5 h. Unlike previous reports, we bypassed genetic engineering of phage. The glycan moiety was introduced via an oxime ligation following oxidation of an N-terminal Ser/Thr; these residues are present in the peptide libraries at 20-30% abundance. The construction of libraries was facilitated by simple characterization, which directly assessed the yield and regioselectivity of chemical reactions performed on phage. This quantification method also allowed facile yield determination of reactions in 10(9) distinct molecules. We envision that the methodology described herein will find broad application in the synthesis of custom chemically modified phage libraries.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

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.

HPC Programming on Intel Many-Integrated-Core Hardware with MAGMA Port to Xeon Phi

DOE PAGES

Dongarra, Jack; Gates, Mark; Haidar, Azzam; ...

2015-01-01

This paper presents the design and implementation of several fundamental dense linear algebra (DLA) algorithms for multicore with Intel Xeon Phi coprocessors. In particular, we consider algorithms for solving linear systems. Further, we give an overview of the MAGMA MIC library, an open source, high performance library, that incorporates the developments presented here and, more broadly, provides the DLA functionality equivalent to that of the popular LAPACK library while targeting heterogeneous architectures that feature a mix of multicore CPUs and coprocessors. The LAPACK-compliance simplifies the use of the MAGMA MIC library in applications, while providing them with portably performant DLA.more » High performance is obtained through the use of the high-performance BLAS, hardware-specific tuning, and a hybridization methodology whereby we split the algorithm into computational tasks of various granularities. Execution of those tasks is properly scheduled over the heterogeneous hardware by minimizing data movements and mapping algorithmic requirements to the architectural strengths of the various heterogeneous hardware components. Our methodology and programming techniques are incorporated into the MAGMA MIC API, which abstracts the application developer from the specifics of the Xeon Phi architecture and is therefore applicable to algorithms beyond the scope of DLA.« less

General purpose graphic processing unit implementation of adaptive pulse compression algorithms

NASA Astrophysics Data System (ADS)

Cai, Jingxiao; Zhang, Yan

2017-07-01

This study introduces a practical approach to implement real-time signal processing algorithms for general surveillance radar based on NVIDIA graphical processing units (GPUs). The pulse compression algorithms are implemented using compute unified device architecture (CUDA) libraries such as CUDA basic linear algebra subroutines and CUDA fast Fourier transform library, which are adopted from open source libraries and optimized for the NVIDIA GPUs. For more advanced, adaptive processing algorithms such as adaptive pulse compression, customized kernel optimization is needed and investigated. A statistical optimization approach is developed for this purpose without needing much knowledge of the physical configurations of the kernels. It was found that the kernel optimization approach can significantly improve the performance. Benchmark performance is compared with the CPU performance in terms of processing accelerations. The proposed implementation framework can be used in various radar systems including ground-based phased array radar, airborne sense and avoid radar, and aerospace surveillance radar.

Redesigning a library-based genetics class research project through instructional theory and authentic experience.

PubMed

Tennant, Michele R; Edwards, Mary; Miyamoto, Michael M

2012-04-01

How can the library-based research project of a genetics course be reinvigorated and made sustainable without sacrificing educational integrity? The University of Florida's Health Science Center Library provides the case study. Since 1996, the librarian has codeveloped, supported, and graded all components of the project. In 2009, the project evolved from a single-authored paper to a group-work poster, with graded presentations hosted by the library. In 2010, students were surveyed regarding class enhancements. Responses indicated a preference for collaborative work and the poster format and suggested the changes facilitated learning. Instructors reported that the poster format more clearly documented students' understanding of genetics. Results suggest project enhancements contributed to greater appreciation, understanding, and application of classroom material and offered a unique and authentic learning experience, without compromising educational integrity. The library benefitted through increased visibility as a partner in the educational mission and development of a sustainable instructional collaboration.

Efficiency Study of Implicit and Explicit Time Integration Operators for Finite Element Applications

DTIC Science & Technology

1977-07-01

cffiAciency, wherein Beta =0 provides anl exp~licit algorithm, wvhile Beta &0 provides anl implicit algorithm. Both algorithmns arc used in the same...Hlueneme CA: CO, Code C44A Port j IHuenemne, CA NAVSEC Cod,. 6034 (Library), Washington DC NAVSI*CGRUAC’I’ PWO, ’rorri Sta, OkinawaI NAVSIIIPRBFTAC Library

A methodology to annotate systems biology markup language models with the synthetic biology open language.

PubMed

Roehner, Nicholas; Myers, Chris J

2014-02-21

Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.

Clustering Educational Digital Library Usage Data: A Comparison of Latent Class Analysis and K-Means Algorithms

ERIC Educational Resources Information Center

Xu, Beijie; Recker, Mimi; Qi, Xiaojun; Flann, Nicholas; Ye, Lei

2013-01-01

This article examines clustering as an educational data mining method. In particular, two clustering algorithms, the widely used K-means and the model-based Latent Class Analysis, are compared, using usage data from an educational digital library service, the Instructional Architect (IA.usu.edu). Using a multi-faceted approach and multiple data…

The Automation of Stochastization Algorithm with Use of SymPy Computer Algebra Library

NASA Astrophysics Data System (ADS)

Demidova, Anastasya; Gevorkyan, Migran; Kulyabov, Dmitry; Korolkova, Anna; Sevastianov, Leonid

2018-02-01

SymPy computer algebra library is used for automatic generation of ordinary and stochastic systems of differential equations from the schemes of kinetic interaction. Schemes of this type are used not only in chemical kinetics but also in biological, ecological and technical models. This paper describes the automatic generation algorithm with an emphasis on application details.

SymDex: increasing the efficiency of chemical fingerprint similarity searches for comparing large chemical libraries by using query set indexing.

PubMed

Tai, David; Fang, Jianwen

2012-08-27

The large sizes of today's chemical databases require efficient algorithms to perform similarity searches. It can be very time consuming to compare two large chemical databases. This paper seeks to build upon existing research efforts by describing a novel strategy for accelerating existing search algorithms for comparing large chemical collections. The quest for efficiency has focused on developing better indexing algorithms by creating heuristics for searching individual chemical against a chemical library by detecting and eliminating needless similarity calculations. For comparing two chemical collections, these algorithms simply execute searches for each chemical in the query set sequentially. The strategy presented in this paper achieves a speedup upon these algorithms by indexing the set of all query chemicals so redundant calculations that arise in the case of sequential searches are eliminated. We implement this novel algorithm by developing a similarity search program called Symmetric inDexing or SymDex. SymDex shows over a 232% maximum speedup compared to the state-of-the-art single query search algorithm over real data for various fingerprint lengths. Considerable speedup is even seen for batch searches where query set sizes are relatively small compared to typical database sizes. To the best of our knowledge, SymDex is the first search algorithm designed specifically for comparing chemical libraries. It can be adapted to most, if not all, existing indexing algorithms and shows potential for accelerating future similarity search algorithms for comparing chemical databases.

Introducing difference recurrence relations for faster semi-global alignment of long sequences.

PubMed

Suzuki, Hajime; Kasahara, Masahiro

2018-02-19

The read length of single-molecule DNA sequencers is reaching 1 Mb. Popular alignment software tools widely used for analyzing such long reads often take advantage of single-instruction multiple-data (SIMD) operations to accelerate calculation of dynamic programming (DP) matrices in the Smith-Waterman-Gotoh (SWG) algorithm with a fixed alignment start position at the origin. Nonetheless, 16-bit or 32-bit integers are necessary for storing the values in a DP matrix when sequences to be aligned are long; this situation hampers the use of the full SIMD width of modern processors. We proposed a faster semi-global alignment algorithm, "difference recurrence relations," that runs more rapidly than the state-of-the-art algorithm by a factor of 2.1. Instead of calculating and storing all the values in a DP matrix directly, our algorithm computes and stores mainly the differences between the values of adjacent cells in the matrix. Although the SWG algorithm and our algorithm can output exactly the same result, our algorithm mainly involves 8-bit integer operations, enabling us to exploit the full width of SIMD operations (e.g., 32) on modern processors. We also developed a library, libgaba, so that developers can easily integrate our algorithm into alignment programs. Our novel algorithm and optimized library implementation will facilitate accelerating nucleotide long-read analysis algorithms that use pairwise alignment stages. The library is implemented in the C programming language and available at https://github.com/ocxtal/libgaba .

Assembling short reads from jumping libraries with large insert sizes.

PubMed

Vasilinetc, Irina; Prjibelski, Andrey D; Gurevich, Alexey; Korobeynikov, Anton; Pevzner, Pavel A

2015-10-15

Advances in Next-Generation Sequencing technologies and sample preparation recently enabled generation of high-quality jumping libraries that have a potential to significantly improve short read assemblies. However, assembly algorithms have to catch up with experimental innovations to benefit from them and to produce high-quality assemblies. We present a new algorithm that extends recently described exSPAnder universal repeat resolution approach to enable its applications to several challenging data types, including jumping libraries generated by the recently developed Illumina Nextera Mate Pair protocol. We demonstrate that, with these improvements, bacterial genomes often can be assembled in a few contigs using only a single Nextera Mate Pair library of short reads. Described algorithms are implemented in C++ as a part of SPAdes genome assembler, which is freely available at bioinf.spbau.ru/en/spades. ap@bioinf.spbau.ru Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Quantum supercharger library: hyper-parallelism of the Hartree-Fock method.

PubMed

Fernandes, Kyle D; Renison, C Alicia; Naidoo, Kevin J

2015-07-05

We present here a set of algorithms that completely rewrites the Hartree-Fock (HF) computations common to many legacy electronic structure packages (such as GAMESS-US, GAMESS-UK, and NWChem) into a massively parallel compute scheme that takes advantage of hardware accelerators such as Graphical Processing Units (GPUs). The HF compute algorithm is core to a library of routines that we name the Quantum Supercharger Library (QSL). We briefly evaluate the QSL's performance and report that it accelerates a HF 6-31G Self-Consistent Field (SCF) computation by up to 20 times for medium sized molecules (such as a buckyball) when compared with mature Central Processing Unit algorithms available in the legacy codes in regular use by researchers. It achieves this acceleration by massive parallelization of the one- and two-electron integrals and optimization of the SCF and Direct Inversion in the Iterative Subspace routines through the use of GPU linear algebra libraries. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

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.

AgBioData | National Agricultural Library

Science.gov Websites

Skip to main content Home National Agricultural Library United States Department of Agriculture Ag National Invertebrate Genetic Resources Insects impact American agriculture both as destructive and , biotypes, and other genetic entities and to document their different interactions with agriculture and the

Customizing FP-growth algorithm to parallel mining with Charm++ library

NASA Astrophysics Data System (ADS)

Puścian, Marek

2017-08-01

This paper presents a frequent item mining algorithm that was customized to handle growing data repositories. The proposed solution applies Master Slave scheme to frequent pattern growth technique. Efficient utilization of available computation units is achieved by dynamic reallocation of tasks. Conditional frequent trees are assigned to parallel workers basing on their workload. Proposed enhancements have been successfully implemented using Charm++ library. This paper discusses results of the performance of parallelized FP-growth algorithm against different datasets. The approach has been illustrated with many experiments and measurements performed using multiprocessor and multithreaded computer.

Redesigning a library-based genetics class research project through instructional theory and authentic experience*

PubMed Central

Tennant, Michele R; Edwards, Mary; Miyamoto, Michael M

2012-01-01

Question: How can the library-based research project of a genetics course be reinvigorated and made sustainable without sacrificing educational integrity? Setting: The University of Florida's Health Science Center Library provides the case study. Methods: Since 1996, the librarian has codeveloped, supported, and graded all components of the project. In 2009, the project evolved from a single-authored paper to a group-work poster, with graded presentations hosted by the library. In 2010, students were surveyed regarding class enhancements. Results: Responses indicated a preference for collaborative work and the poster format and suggested the changes facilitated learning. Instructors reported that the poster format more clearly documented students' understanding of genetics. Conclusion: Results suggest project enhancements contributed to greater appreciation, understanding, and application of classroom material and offered a unique and authentic learning experience, without compromising educational integrity. The library benefitted through increased visibility as a partner in the educational mission and development of a sustainable instructional collaboration. PMID:22514504

Scheduling language and algorithm development study. Appendix: Study approach and activity summary

NASA Technical Reports Server (NTRS)

1974-01-01

The approach and organization of the study to develop a high level computer programming language and a program library are presented. The algorithm and problem modeling analyses are summarized. The approach used to identify and specify the capabilities required in the basic language is described. Results of the analyses used to define specifications for the scheduling module library are presented.

Library Search Prefilters for Vehicle Manufacturers to Assist in the Forensic Examination of Automotive Paints.

PubMed

Lavine, Barry K; White, Collin G; Ding, Tao

2018-03-01

Pattern recognition techniques have been applied to the infrared (IR) spectral libraries of the Paint Data Query (PDQ) database to differentiate between nonidentical but similar IR spectra of automotive paints. To tackle the problem of library searching, search prefilters were developed to identify the vehicle make from IR spectra of the clear coat, surfacer-primer, and e-coat layers. To develop these search prefilters with the appropriate degree of accuracy, IR spectra from the PDQ database were preprocessed using the discrete wavelet transform to enhance subtle but significant features in the IR spectral data. Wavelet coefficients characteristic of vehicle make were identified using a genetic algorithm for pattern recognition and feature selection. Search prefilters to identify automotive manufacturer through IR spectra obtained from a paint chip recovered at a crime scene were developed using 1596 original manufacturer's paint systems spanning six makes (General Motors, Chrysler, Ford, Honda, Nissan, and Toyota) within a limited production year range (2000-2006). Search prefilters for vehicle manufacturer that were developed as part of this study were successfully validated using IR spectra obtained directly from the PDQ database. Information obtained from these search prefilters can serve to quantify the discrimination power of original automotive paint encountered in casework and further efforts to succinctly communicate trace evidential significance to the courts.

Development of a Trypanosoma cruzi strain typing assay using MS2 peptide spectral libraries (Tc-STAMS2)

PubMed Central

de Oliveira, Gilberto Santos; Kawahara, Rebeca; Rosa-Fernandes, Livia; Avila, Carla Cristi; Teixeira, Marta M. G.; Larsen, Martin R.

2018-01-01

Background Chagas disease also known as American trypanosomiasis is caused by the protozoan Trypanosoma cruzi. Over the last 30 years, Chagas disease has expanded from a neglected parasitic infection of the rural population to an urbanized chronic disease, becoming a potentially emergent global health problem. T. cruzi strains were assigned to seven genetic groups (TcI-TcVI and TcBat), named discrete typing units (DTUs), which represent a set of isolates that differ in virulence, pathogenicity and immunological features. Indeed, diverse clinical manifestations (from asymptomatic to highly severe disease) have been attempted to be related to T.cruzi genetic variability. Due to that, several DTU typing methods have been introduced. Each method has its own advantages and drawbacks such as high complexity and analysis time and all of them are based on genetic signatures. Recently, a novel method discriminated bacterial strains using a peptide identification-free, genome sequence-independent shotgun proteomics workflow. Here, we aimed to develop a Trypanosoma cruzi Strain Typing Assay using MS/MS peptide spectral libraries, named Tc-STAMS2. Methods/Principal findings The Tc-STAMS2 method uses shotgun proteomics combined with spectral library search to assign and discriminate T. cruzi strains independently on the genome knowledge. The method is based on the construction of a library of MS/MS peptide spectra built using genotyped T. cruzi reference strains. For identification, the MS/MS peptide spectra of unknown T. cruzi cells are identified using the spectral matching algorithm SpectraST. The Tc-STAMS2 method allowed correct identification of all DTUs with high confidence. The method was robust towards different sample preparations, length of chromatographic gradients and fragmentation techniques. Moreover, a pilot inter-laboratory study showed the applicability to different MS platforms. Conclusions and significance This is the first study that develops a MS-based platform for T. cruzi strain typing. Indeed, the Tc-STAMS2 method allows T. cruzi strain typing using MS/MS spectra as discriminatory features and allows the differentiation of TcI-TcVI DTUs. Similar to genomic-based strategies, the Tc-STAMS2 method allows identification of strains within DTUs. Its robustness towards different experimental and biological variables makes it a valuable complementary strategy to the current T. cruzi genotyping assays. Moreover, this method can be used to identify DTU-specific features correlated with the strain phenotype. PMID:29608573

Development of a Trypanosoma cruzi strain typing assay using MS2 peptide spectral libraries (Tc-STAMS2).

PubMed

de Oliveira, Gilberto Santos; Kawahara, Rebeca; Rosa-Fernandes, Livia; Mule, Simon Ngao; Avila, Carla Cristi; Teixeira, Marta M G; Larsen, Martin R; Palmisano, Giuseppe

2018-04-01

Chagas disease also known as American trypanosomiasis is caused by the protozoan Trypanosoma cruzi. Over the last 30 years, Chagas disease has expanded from a neglected parasitic infection of the rural population to an urbanized chronic disease, becoming a potentially emergent global health problem. T. cruzi strains were assigned to seven genetic groups (TcI-TcVI and TcBat), named discrete typing units (DTUs), which represent a set of isolates that differ in virulence, pathogenicity and immunological features. Indeed, diverse clinical manifestations (from asymptomatic to highly severe disease) have been attempted to be related to T.cruzi genetic variability. Due to that, several DTU typing methods have been introduced. Each method has its own advantages and drawbacks such as high complexity and analysis time and all of them are based on genetic signatures. Recently, a novel method discriminated bacterial strains using a peptide identification-free, genome sequence-independent shotgun proteomics workflow. Here, we aimed to develop a Trypanosoma cruzi Strain Typing Assay using MS/MS peptide spectral libraries, named Tc-STAMS2. The Tc-STAMS2 method uses shotgun proteomics combined with spectral library search to assign and discriminate T. cruzi strains independently on the genome knowledge. The method is based on the construction of a library of MS/MS peptide spectra built using genotyped T. cruzi reference strains. For identification, the MS/MS peptide spectra of unknown T. cruzi cells are identified using the spectral matching algorithm SpectraST. The Tc-STAMS2 method allowed correct identification of all DTUs with high confidence. The method was robust towards different sample preparations, length of chromatographic gradients and fragmentation techniques. Moreover, a pilot inter-laboratory study showed the applicability to different MS platforms. This is the first study that develops a MS-based platform for T. cruzi strain typing. Indeed, the Tc-STAMS2 method allows T. cruzi strain typing using MS/MS spectra as discriminatory features and allows the differentiation of TcI-TcVI DTUs. Similar to genomic-based strategies, the Tc-STAMS2 method allows identification of strains within DTUs. Its robustness towards different experimental and biological variables makes it a valuable complementary strategy to the current T. cruzi genotyping assays. Moreover, this method can be used to identify DTU-specific features correlated with the strain phenotype.

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

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.

Pricing Structures for Automated Library Consortia.

ERIC Educational Resources Information Center

Machovec, George S.

1993-01-01

Discusses the development of successful pricing algorithms for cooperative library automation projects. Highlights include desirable characteristics of pricing measures, including simplicity and the ability to allow for system growth; problems with transaction-based systems; and a review of the pricing strategies of seven library consortia.…

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.

MorphoGraphX: A platform for quantifying morphogenesis in 4D.

PubMed

Barbier de Reuille, Pierre; Routier-Kierzkowska, Anne-Lise; Kierzkowski, Daniel; Bassel, George W; Schüpbach, Thierry; Tauriello, Gerardo; Bajpai, Namrata; Strauss, Sören; Weber, Alain; Kiss, Annamaria; Burian, Agata; Hofhuis, Hugo; Sapala, Aleksandra; Lipowczan, Marcin; Heimlicher, Maria B; Robinson, Sarah; Bayer, Emmanuelle M; Basler, Konrad; Koumoutsakos, Petros; Roeder, Adrienne H K; Aegerter-Wilmsen, Tinri; Nakayama, Naomi; Tsiantis, Miltos; Hay, Angela; Kwiatkowska, Dorota; Xenarios, Ioannis; Kuhlemeier, Cris; Smith, Richard S

2015-05-06

Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX ( www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.

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.

MpTheory Java library: a multi-platform Java library for systems biology based on the Metabolic P theory.

PubMed

Marchetti, Luca; Manca, Vincenzo

2015-04-15

MpTheory Java library is an open-source project collecting a set of objects and algorithms for modeling observed dynamics by means of the Metabolic P (MP) theory, that is, a mathematical theory introduced in 2004 for modeling biological dynamics. By means of the library, it is possible to model biological systems both at continuous and at discrete time. Moreover, the library comprises a set of regression algorithms for inferring MP models starting from time series of observations. To enhance the modeling experience, beside a pure Java usage, the library can be directly used within the most popular computing environments, such as MATLAB, GNU Octave, Mathematica and R. The library is open-source and licensed under the GNU Lesser General Public License (LGPL) Version 3.0. Source code, binaries and complete documentation are available at http://mptheory.scienze.univr.it. luca.marchetti@univr.it, marchetti@cosbi.eu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort.

PubMed

Jeschek, Markus; Gerngross, Daniel; Panke, Sven

2016-03-31

Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.

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.

CARS Spectral Fitting with Multiple Resonant Species using Sparse Libraries

NASA Technical Reports Server (NTRS)

Cutler, Andrew D.; Magnotti, Gaetano

2010-01-01

The dual pump CARS technique is often used in the study of turbulent flames. Fast and accurate algorithms are needed for fitting dual-pump CARS spectra for temperature and multiple chemical species. This paper describes the development of such an algorithm. The algorithm employs sparse libraries, whose size grows much more slowly with number of species than a conventional library. The method was demonstrated by fitting synthetic "experimental" spectra containing 4 resonant species (N2, O2, H2 and CO2), both with noise and without it, and by fitting experimental spectra from a H2-air flame produced by a Hencken burner. In both studies, weighted least squares fitting of signal, as opposed to least squares fitting signal or square-root signal, was shown to produce the least random error and minimize bias error in the fitted parameters.

A hybrid algorithm for the segmentation of books in libraries

NASA Astrophysics Data System (ADS)

Hu, Zilong; Tang, Jinshan; Lei, Liang

2016-05-01

This paper proposes an algorithm for book segmentation based on bookshelves images. The algorithm can be separated into three parts. The first part is pre-processing, aiming at eliminating or decreasing the effect of image noise and illumination conditions. The second part is near-horizontal line detection based on Canny edge detector, and separating a bookshelves image into multiple sub-images so that each sub-image contains an individual shelf. The last part is book segmentation. In each shelf image, near-vertical line is detected, and obtained lines are used for book segmentation. The proposed algorithm was tested with the bookshelf images taken from OPIE library in MTU, and the experimental results demonstrate good performance.

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.

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.

An Efficient Rank Based Approach for Closest String and Closest Substring

PubMed Central

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

A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.

75 FR 62549 - National Library of Medicine; Notice of Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-12

... DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health National Library of Medicine... medical genetics area. Place: National Library of Medicine, Building 38, 2nd Floor, Board Room, 8600... Biotechnology Information, National Library of Medicine, Department of Health and Human Services, Building 38A...

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.

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.

CoGAPS matrix factorization algorithm identifies transcriptional changes in AP-2alpha target genes in feedback from therapeutic inhibition of the EGFR network

PubMed Central

Thakar, Manjusha; Howard, Jason D.; Kagohara, Luciane T.; Krigsfeld, Gabriel; Ranaweera, Ruchira S.; Hughes, Robert M.; Perez, Jimena; Jones, Siân; Favorov, Alexander V.; Carey, Jacob; Stein-O'Brien, Genevieve; Gaykalova, Daria A.; Ochs, Michael F.; Chung, Christine H.

2016-01-01

Patients with oncogene driven tumors are treated with targeted therapeutics including EGFR inhibitors. Genomic data from The Cancer Genome Atlas (TCGA) demonstrates molecular alterations to EGFR, MAPK, and PI3K pathways in previously untreated tumors. Therefore, this study uses bioinformatics algorithms to delineate interactions resulting from EGFR inhibitor use in cancer cells with these genetic alterations. We modify the HaCaT keratinocyte cell line model to simulate cancer cells with constitutive activation of EGFR, HRAS, and PI3K in a controlled genetic background. We then measure gene expression after treating modified HaCaT cells with gefitinib, afatinib, and cetuximab. The CoGAPS algorithm distinguishes a gene expression signature associated with the anticipated silencing of the EGFR network. It also infers a feedback signature with EGFR gene expression itself increasing in cells that are responsive to EGFR inhibitors. This feedback signature has increased expression of several growth factor receptors regulated by the AP-2 family of transcription factors. The gene expression signatures for AP-2alpha are further correlated with sensitivity to cetuximab treatment in HNSCC cell lines and changes in EGFR expression in HNSCC tumors with low CDKN2A gene expression. In addition, the AP-2alpha gene expression signatures are also associated with inhibition of MEK, PI3K, and mTOR pathways in the Library of Integrated Network-Based Cellular Signatures (LINCS) data. These results suggest that AP-2 transcription factors are activated as feedback from EGFR network inhibition and may mediate EGFR inhibitor resistance. PMID:27650546

Design considerations for parallel graphics libraries

NASA Technical Reports Server (NTRS)

Crockett, Thomas W.

1994-01-01

Applications which run on parallel supercomputers are often characterized by massive datasets. Converting these vast collections of numbers to visual form has proven to be a powerful aid to comprehension. For a variety of reasons, it may be desirable to provide this visual feedback at runtime. One way to accomplish this is to exploit the available parallelism to perform graphics operations in place. In order to do this, we need appropriate parallel rendering algorithms and library interfaces. This paper provides a tutorial introduction to some of the issues which arise in designing parallel graphics libraries and their underlying rendering algorithms. The focus is on polygon rendering for distributed memory message-passing systems. We illustrate our discussion with examples from PGL, a parallel graphics library which has been developed on the Intel family of parallel systems.

Computer-Assisted Transgenesis of Caenorhabditis elegans for Deep Phenotyping

PubMed Central

Gilleland, Cody L.; Falls, Adam T.; Noraky, James; Heiman, Maxwell G.; Yanik, Mehmet F.

2015-01-01

A major goal in the study of human diseases is to assign functions to genes or genetic variants. The model organism Caenorhabditis elegans provides a powerful tool because homologs of many human genes are identifiable, and large collections of genetic vectors and mutant strains are available. However, the delivery of such vector libraries into mutant strains remains a long-standing experimental bottleneck for phenotypic analysis. Here, we present a computer-assisted microinjection platform to streamline the production of transgenic C. elegans with multiple vectors for deep phenotyping. Briefly, animals are immobilized in a temperature-sensitive hydrogel using a standard multiwell platform. Microinjections are then performed under control of an automated microscope using precision robotics driven by customized computer vision algorithms. We demonstrate utility by phenotyping the morphology of 12 neuronal classes in six mutant backgrounds using combinations of neuron-type-specific fluorescent reporters. This technology can industrialize the assignment of in vivo gene function by enabling large-scale transgenic engineering. PMID:26163188

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…

Software Helps Retrieve Information Relevant to the User

NASA Technical Reports Server (NTRS)

Mathe, Natalie; Chen, James

2003-01-01

The Adaptive Indexing and Retrieval Agent (ARNIE) is a code library, designed to be used by an application program, that assists human users in retrieving desired information in a hypertext setting. Using ARNIE, the program implements a computational model for interactively learning what information each human user considers relevant in context. The model, called a "relevance network," incrementally adapts retrieved information to users individual profiles on the basis of feedback from the users regarding specific queries. The model also generalizes such knowledge for subsequent derivation of relevant references for similar queries and profiles, thereby, assisting users in filtering information by relevance. ARNIE thus enables users to categorize and share information of interest in various contexts. ARNIE encodes the relevance and structure of information in a neural network dynamically configured with a genetic algorithm. ARNIE maintains an internal database, wherein it saves associations, and from which it returns associated items in response to a query. A C++ compiler for a platform on which ARNIE will be utilized is necessary for creating the ARNIE library but is not necessary for the execution of the software.

AeroADL: applying the integration of the Suomi-NPP science algorithms with the Algorithm Development Library to the calibration and validation task

NASA Astrophysics Data System (ADS)

Houchin, J. S.

2014-09-01

A common problem for the off-line validation of the calibration algorithms and algorithm coefficients is being able to run science data through the exact same software used for on-line calibration of that data. The Joint Polar Satellite System (JPSS) program solved part of this problem by making the Algorithm Development Library (ADL) available, which allows the operational algorithm code to be compiled and run on a desktop Linux workstation using flat file input and output. However, this solved only part of the problem, as the toolkit and methods to initiate the processing of data through the algorithms were geared specifically toward the algorithm developer, not the calibration analyst. In algorithm development mode, a limited number of sets of test data are staged for the algorithm once, and then run through the algorithm over and over as the software is developed and debugged. In calibration analyst mode, we are continually running new data sets through the algorithm, which requires significant effort to stage each of those data sets for the algorithm without additional tools. AeroADL solves this second problem by providing a set of scripts that wrap the ADL tools, providing both efficient means to stage and process an input data set, to override static calibration coefficient look-up-tables (LUT) with experimental versions of those tables, and to manage a library containing multiple versions of each of the static LUT files in such a way that the correct set of LUTs required for each algorithm are automatically provided to the algorithm without analyst effort. Using AeroADL, The Aerospace Corporation's analyst team has demonstrated the ability to quickly and efficiently perform analysis tasks for both the VIIRS and OMPS sensors with minimal training on the software tools.

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

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.

Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration

PubMed Central

Woodruff, Lauren B. A.; Gorochowski, Thomas E.; Roehner, Nicholas; Densmore, Douglas; Gordon, D. Benjamin; Nicol, Robert

2017-01-01

Abstract Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated. PMID:28007941

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.

A lanthipeptide library used to identify a protein-protein interaction inhibitor.

PubMed

Yang, Xiao; Lennard, Katherine R; He, Chang; Walker, Mark C; Ball, Andrew T; Doigneaux, Cyrielle; Tavassoli, Ali; van der Donk, Wilfred A

2018-04-01

In this article we describe the production and screening of a genetically encoded library of 10 6 lanthipeptides in Escherichia coli using the substrate-tolerant lanthipeptide synthetase ProcM. This plasmid-encoded library was combined with a bacterial reverse two-hybrid system for the interaction of the HIV p6 protein with the UEV domain of the human TSG101 protein, which is a critical protein-protein interaction for HIV budding from infected cells. Using this approach, we identified an inhibitor of this interaction from the lanthipeptide library, whose activity was verified in vitro and in cell-based virus-like particle-budding assays. Given the variety of lanthipeptide backbone scaffolds that may be produced with ProcM, this method may be used for the generation of genetically encoded libraries of natural product-like lanthipeptides containing substantial structural diversity. Such libraries may be combined with any cell-based assay to identify lanthipeptides with new biological activities.

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.

Filetype Identification Using Long, Summarized N-Grams

DTIC Science & Technology

2011-03-01

compressed or encrypted data . If the algorithm used to compress or encrypt the data can be determined, then it is frequently possible to uncom- press...fragments. His implementation utilized the bzip2 library to compress the file fragments. The bzip2 library is based off the Lempel - Ziv -Markov chain... algorithm that uses a dictionary compression scheme to remove repeating data patterns within a set of data . The removed patterns are listed within the

DFT Performance Prediction in FFTW

NASA Astrophysics Data System (ADS)

Gu, Liang; Li, Xiaoming

Fastest Fourier Transform in the West (FFTW) is an adaptive FFT library that generates highly efficient Discrete Fourier Transform (DFT) implementations. It is one of the fastest FFT libraries available and it outperforms many adaptive or hand-tuned DFT libraries. Its success largely relies on the huge search space spanned by several FFT algorithms and a set of compiler generated C code (called codelets) for small size DFTs. FFTW empirically finds the best algorithm by measuring the performance of different algorithm combinations. Although the empirical search works very well for FFTW, the search process does not explain why the best plan found performs best, and the search overhead grows polynomially as the DFT size increases. The opposite of empirical search is model-driven optimization. However, it is widely believed that model-driven optimization is inferior to empirical search and is particularly powerless to solve problems as complex as the optimization of DFT.

Comparison of three methods for materials identification and mapping with imaging spectroscopy

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Swayze, Gregg; Boardman, Joe; Kruse, Fred

1993-01-01

We are comparing three methods of mapping analysis tools for imaging spectroscopy data. The purpose of this comparison is to understand the advantages and disadvantages of each algorithm so others would be better able to choose the best algorithm or combinations of algorithms for a particular problem. The three algorithms are: (1) the spectralfeature modified least squares mapping algorithm of Clark et al (1990, 1991): programs mbandmap and tricorder; (2) the Spectral Angle Mapper Algorithm(Boardman, 1993) found in the CU CSES SIPS package; and (3) the Expert System of Kruse et al. (1993). The comparison uses a ground-calibrated 1990 AVIRIS scene of 400 by 410 pixels over Cuprite, Nevada. Along with the test data set is a spectral library of 38 minerals. Each algorithm is tested with the same AVIRIS data set and spectral library. Field work has confirmed the presence of many of these minerals in the AVIRIS scene (Swayze et al. 1992).

Javascript Library for Developing Interactive Micro-Level Animations for Teaching and Learning Algorithms on One-Dimensional Arrays

ERIC Educational Resources Information Center

Végh, Ladislav

2016-01-01

The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…

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.

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.

Combining a Deconvolution and a Universal Library Search Algorithm for the Nontarget Analysis of Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution Mass Spectrometry Results.

PubMed

Samanipour, Saer; Reid, Malcolm J; Bæk, Kine; Thomas, Kevin V

2018-04-17

Nontarget analysis is considered one of the most comprehensive tools for the identification of unknown compounds in a complex sample analyzed via liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Due to the complexity of the data generated via LC-HRMS, the data-dependent acquisition mode, which produces the MS 2 spectra of a limited number of the precursor ions, has been one of the most common approaches used during nontarget screening. However, data-independent acquisition mode produces highly complex spectra that require proper deconvolution and library search algorithms. We have developed a deconvolution algorithm and a universal library search algorithm (ULSA) for the analysis of complex spectra generated via data-independent acquisition. These algorithms were validated and tested using both semisynthetic and real environmental data. A total of 6000 randomly selected spectra from MassBank were introduced across the total ion chromatograms of 15 sludge extracts at three levels of background complexity for the validation of the algorithms via semisynthetic data. The deconvolution algorithm successfully extracted more than 60% of the added ions in the analytical signal for 95% of processed spectra (i.e., 3 complexity levels multiplied by 6000 spectra). The ULSA ranked the correct spectra among the top three for more than 95% of cases. We further tested the algorithms with 5 wastewater effluent extracts for 59 artificial unknown analytes (i.e., their presence or absence was confirmed via target analysis). These algorithms did not produce any cases of false identifications while correctly identifying ∼70% of the total inquiries. The implications, capabilities, and the limitations of both algorithms are further discussed.

Study of parameter identification using hybrid neural-genetic algorithm in electro-hydraulic servo system

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.

Computational design of chimeric protein libraries for directed evolution.

PubMed

Silberg, Jonathan J; Nguyen, Peter Q; Stevenson, Taylor

2010-01-01

The best approach for creating libraries of functional proteins with large numbers of nondisruptive amino acid substitutions is protein recombination, in which structurally related polypeptides are swapped among homologous proteins. Unfortunately, as more distantly related proteins are recombined, the fraction of variants having a disrupted structure increases. One way to enrich the fraction of folded and potentially interesting chimeras in these libraries is to use computational algorithms to anticipate which structural elements can be swapped without disturbing the integrity of a protein's structure. Herein, we describe how the algorithm Schema uses the sequences and structures of the parent proteins recombined to predict the structural disruption of chimeras, and we outline how dynamic programming can be used to find libraries with a range of amino acid substitution levels that are enriched in variants with low Schema disruption.

Using Spherical-Harmonics Expansions for Optics Surface Reconstruction from Gradients.

PubMed

Solano-Altamirano, Juan Manuel; Vázquez-Otero, Alejandro; Khikhlukha, Danila; Dormido, Raquel; Duro, Natividad

2017-11-30

In this paper, we propose a new algorithm to reconstruct optics surfaces (aka wavefronts) from gradients, defined on a circular domain, by means of the Spherical Harmonics. The experimental results indicate that this algorithm renders the same accuracy, compared to the reconstruction based on classical Zernike polynomials, using a smaller number of polynomial terms, which potentially speeds up the wavefront reconstruction. Additionally, we provide an open-source C++ library, released under the terms of the GNU General Public License version 2 (GPLv2), wherein several polynomial sets are coded. Therefore, this library constitutes a robust software alternative for wavefront reconstruction in a high energy laser field, optical surface reconstruction, and, more generally, in surface reconstruction from gradients. The library is a candidate for being integrated in control systems for optical devices, or similarly to be used in ad hoc simulations. Moreover, it has been developed with flexibility in mind, and, as such, the implementation includes the following features: (i) a mock-up generator of various incident wavefronts, intended to simulate the wavefronts commonly encountered in the field of high-energy lasers production; (ii) runtime selection of the library in charge of performing the algebraic computations; (iii) a profiling mechanism to measure and compare the performance of different steps of the algorithms and/or third-party linear algebra libraries. Finally, the library can be easily extended to include additional dependencies, such as porting the algebraic operations to specific architectures, in order to exploit hardware acceleration features.

Using Spherical-Harmonics Expansions for Optics Surface Reconstruction from Gradients

PubMed Central

Solano-Altamirano, Juan Manuel; Khikhlukha, Danila

2017-01-01

In this paper, we propose a new algorithm to reconstruct optics surfaces (aka wavefronts) from gradients, defined on a circular domain, by means of the Spherical Harmonics. The experimental results indicate that this algorithm renders the same accuracy, compared to the reconstruction based on classical Zernike polynomials, using a smaller number of polynomial terms, which potentially speeds up the wavefront reconstruction. Additionally, we provide an open-source C++ library, released under the terms of the GNU General Public License version 2 (GPLv2), wherein several polynomial sets are coded. Therefore, this library constitutes a robust software alternative for wavefront reconstruction in a high energy laser field, optical surface reconstruction, and, more generally, in surface reconstruction from gradients. The library is a candidate for being integrated in control systems for optical devices, or similarly to be used in ad hoc simulations. Moreover, it has been developed with flexibility in mind, and, as such, the implementation includes the following features: (i) a mock-up generator of various incident wavefronts, intended to simulate the wavefronts commonly encountered in the field of high-energy lasers production; (ii) runtime selection of the library in charge of performing the algebraic computations; (iii) a profiling mechanism to measure and compare the performance of different steps of the algorithms and/or third-party linear algebra libraries. Finally, the library can be easily extended to include additional dependencies, such as porting the algebraic operations to specific architectures, in order to exploit hardware acceleration features. PMID:29189722

Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity

PubMed Central

Yeger-Lotem, Esti; Riva, Laura; Su, Linhui Julie; Gitler, Aaron D.; Cashikar, Anil; King, Oliver D.; Auluck, Pavan K.; Geddie, Melissa L.; Valastyan, Julie S.; Karger, David R.; Lindquist, Susan; Fraenkel, Ernest

2009-01-01

Cells respond to stimuli by changes in various processes, including signaling pathways and gene expression. Efforts to identify components of these responses increasingly depend on mRNA profiling and genetic library screens, yet the functional roles of the genes identified by these assays often remain enigmatic. By comparing the results of these two assays across various cellular responses, we found that they are consistently distinct. Moreover, genetic screens tend to identify response regulators, while mRNA profiling frequently detects metabolic responses. We developed an integrative approach that bridges the gap between these data using known molecular interactions, thus highlighting major response pathways. We harnessed this approach to reveal cellular pathways related to alpha-synuclein, a small lipid-binding protein implicated in several neurodegenerative disorders including Parkinson disease. For this we screened an established yeast model for alpha-synuclein toxicity to identify genes that when overexpressed alter cellular survival. Application of our algorithm to these data and data from mRNA profiling provided functional explanations for many of these genes and revealed novel relations between alpha-synuclein toxicity and basic cellular pathways. PMID:19234470

Machine learning patterns for neuroimaging-genetic studies in the cloud.

PubMed

Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

2014-01-01

Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

A Model for the Stop Planning and Timetables of Customized Buses

PubMed Central

Yang, Yang

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space–time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs. PMID:28056041

A Model for the Stop Planning and Timetables of Customized Buses.

PubMed

Ma, Jihui; Zhao, Yanqing; Yang, Yang; Liu, Tao; Guan, Wei; Wang, Jiao; Song, Cuiying

2017-01-01

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space-time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.

Stochastic optimization of GeantV code by use of genetic algorithms

DOE PAGES

Amadio, G.; Apostolakis, J.; Bandieramonte, M.; ...

2017-10-01

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) andmore » handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.« less

Stochastic optimization of GeantV code by use of genetic algorithms

NASA Astrophysics Data System (ADS)

Amadio, G.; Apostolakis, J.; Bandieramonte, M.; Behera, S. P.; Brun, R.; Canal, P.; Carminati, F.; Cosmo, G.; Duhem, L.; Elvira, D.; Folger, G.; Gheata, A.; Gheata, M.; Goulas, I.; Hariri, F.; Jun, S. Y.; Konstantinov, D.; Kumawat, H.; Ivantchenko, V.; Lima, G.; Nikitina, T.; Novak, M.; Pokorski, W.; Ribon, A.; Seghal, R.; Shadura, O.; Vallecorsa, S.; Wenzel, S.

2017-10-01

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) and handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. The goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.

Stochastic optimization of GeantV code by use of genetic algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Amadio, G.; Apostolakis, J.; Bandieramonte, M.

GeantV is a complex system based on the interaction of different modules needed for detector simulation, which include transport of particles in fields, physics models simulating their interactions with matter and a geometrical modeler library for describing the detector and locating the particles and computing the path length to the current volume boundary. The GeantV project is recasting the classical simulation approach to get maximum benefit from SIMD/MIMD computational architectures and highly massive parallel systems. This involves finding the appropriate balance between several aspects influencing computational performance (floating-point performance, usage of off-chip memory bandwidth, specification of cache hierarchy, etc.) andmore » handling a large number of program parameters that have to be optimized to achieve the best simulation throughput. This optimization task can be treated as a black-box optimization problem, which requires searching the optimum set of parameters using only point-wise function evaluations. Here, the goal of this study is to provide a mechanism for optimizing complex systems (high energy physics particle transport simulations) with the help of genetic algorithms and evolution strategies as tuning procedures for massive parallel simulations. One of the described approaches is based on introducing a specific multivariate analysis operator that could be used in case of resource expensive or time consuming evaluations of fitness functions, in order to speed-up the convergence of the black-box optimization problem.« less

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.

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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

AISL-CRYPTO is a library of cryptography functions supporting other AISL software. It provides various crypto functions for Common Lisp, including Digital Signature Algorithm, Data Encryption Standard, Secure Hash Algorithm, and public-key cryptography.

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.

Research on multi - channel interactive virtual assembly system for power equipment under the “VR+” era

NASA Astrophysics Data System (ADS)

Ren, Yilong; Duan, Xitong; Wu, Lei; He, Jin; Xu, Wu

2017-06-01

With the development of the “VR+” era, the traditional virtual assembly system of power equipment has been unable to satisfy our growing needs. In this paper, based on the analysis of the traditional virtual assembly system of electric power equipment and the application of VR technology in the virtual assembly system of electric power equipment in our country, this paper puts forward the scheme of establishing the virtual assembly system of power equipment: At first, we should obtain the information of power equipment, then we should using OpenGL and multi texture technology to build 3D solid graphics library. After the completion of three-dimensional modeling, we can use the dynamic link library DLL package three-dimensional solid graphics generation program to realize the modularization of power equipment model library and power equipment model library generated hidden algorithm. After the establishment of 3D power equipment model database, we set up the virtual assembly system of 3D power equipment to separate the assembly operation of the power equipment from the space. At the same time, aiming at the deficiency of the traditional gesture recognition algorithm, we propose a gesture recognition algorithm based on improved PSO algorithm for BP neural network data glove. Finally, the virtual assembly system of power equipment can really achieve multi-channel interaction function.

Hyperopt: a Python library for model selection and hyperparameter optimization

NASA Astrophysics Data System (ADS)

Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.

2015-01-01

Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.

A part toolbox to tune genetic expression in Bacillus subtilis

PubMed Central

Guiziou, Sarah; Sauveplane, Vincent; Chang, Hung-Ju; Clerté, Caroline; Declerck, Nathalie; Jules, Matthieu; Bonnet, Jerome

2016-01-01

Libraries of well-characterised components regulating gene expression levels are essential to many synthetic biology applications. While widely available for the Gram-negative model bacterium Escherichia coli, such libraries are lacking for the Gram-positive model Bacillus subtilis, a key organism for basic research and biotechnological applications. Here, we engineered a genetic toolbox comprising libraries of promoters, Ribosome Binding Sites (RBS), and protein degradation tags to precisely tune gene expression in B. subtilis. We first designed a modular Expression Operating Unit (EOU) facilitating parts assembly and modifications and providing a standard genetic context for gene circuits implementation. We then selected native, constitutive promoters of B. subtilis and efficient RBS sequences from which we engineered three promoters and three RBS sequence libraries exhibiting ∼14 000-fold dynamic range in gene expression levels. We also designed a collection of SsrA proteolysis tags of variable strength. Finally, by using fluorescence fluctuation methods coupled with two-photon microscopy, we quantified the absolute concentration of GFP in a subset of strains from the library. Our complete promoters and RBS sequences library comprising over 135 constructs enables tuning of GFP concentration over five orders of magnitude, from 0.05 to 700 μM. This toolbox of regulatory components will support many research and engineering applications in B. subtilis. PMID:27402159

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

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.

Self-sorting of dynamic metallosupramolecular libraries (DMLs) via metal-driven selection.

PubMed

Kocsis, Istvan; Dumitrescu, Dan; Legrand, Yves-Marie; van der Lee, Arie; Grosu, Ion; Barboiu, Mihail

2014-03-11

"Metal-driven" selection between finite mononuclear and polymeric metallosupramolecular species can be quantitatively achieved in solution and in a crystalline state via coupled coordination/stacking interactional algorithms within dynamic metallosupramolecular libraries - DMLs.

Aerodynamic Optimization of a Supersonic Bending Body Projectile by a Vector-Evaluated Genetic Algorithm

DTIC Science & Technology

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

YAC cloning Mus musculus telomeric DNA: physical, genetic, in situ and STS markers for the distal telomere of chromosome 10.

PubMed

Kipling, D; Wilson, H E; Thomson, E J; Cooke, H J

1995-06-01

Three Mus musculus DBA/2 YAC libraries were constructed using a half-YAC telomere cloning vector. This functional complementation approach yields libraries which include terminal restriction fragments of the mouse genome. Screening all three libraries led to the isolation of 32 independent clones which carry linear YACs containing the mouse terminal repeat sequence, (TTAGGG)n. These YACs provide a resource to isolate regions of the mouse genome close to chromosome termini and excluded from existing conventional YAC libraries. To demonstrate their utility, a hybridization probe was isolated from Mtel-1, the first (TTAGGG)n-containing YAC isolated. This probe detects a approximately 70 kb Kpnl fragment in the mouse genome which is sensitive to pretreatment with BAL31 exonuclease. A PCR-based genetic marker generated from the sequence of this probe maps 4.4 cM from the most distal anchor locus on chromosome 10 in the EUCIB interspecific backcross. STS primers for this locus, D10Hgu1, were used to isolate YAC 110F4 from a commercially available mouse YAC library. Fluorescence in situ hybridization demonstrates that YAC 110F4 hybridizes to the distal telomere of chromosome 10. Clones in this collection of telomere YACs therefore partially overlap clones in conventional YAC libraries, and thus the previously unavailable terminal regions of the mouse genome can now be linked with the developing mouse STS YAC contig. Genetic markers such as D10Hgu1 allow the ends of the mouse genetic map to be defined, thus closing the map.

Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications.

PubMed

Achakulvisut, Titipat; Acuna, Daniel E; Ruangrong, Tulakan; Kording, Konrad

2016-01-01

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate.

Science Concierge: A Fast Content-Based Recommendation System for Scientific Publications

PubMed Central

Achakulvisut, Titipat; Acuna, Daniel E.; Ruangrong, Tulakan; Kording, Konrad

2016-01-01

Finding relevant publications is important for scientists who have to cope with exponentially increasing numbers of scholarly material. Algorithms can help with this task as they help for music, movie, and product recommendations. However, we know little about the performance of these algorithms with scholarly material. Here, we develop an algorithm, and an accompanying Python library, that implements a recommendation system based on the content of articles. Design principles are to adapt to new content, provide near-real time suggestions, and be open source. We tested the library on 15K posters from the Society of Neuroscience Conference 2015. Human curated topics are used to cross validate parameters in the algorithm and produce a similarity metric that maximally correlates with human judgments. We show that our algorithm significantly outperformed suggestions based on keywords. The work presented here promises to make the exploration of scholarly material faster and more accurate. PMID:27383424

A New Improved and Extended Version of the Multicell Bacterial Simulator gro.

PubMed

Gutiérrez, Martín; Gregorio-Godoy, Paula; Pérez Del Pulgar, Guillermo; Muñoz, Luis E; Sáez, Sandra; Rodríguez-Patón, Alfonso

2017-08-18

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits and microbial consortia. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use, and provide new functionalities. The new version of gro is between 1 and 2 orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 10 5 cells in less than 10 min. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

Adaptation of Flux-Corrected Transport Algorithms for Modeling Dusty Flows.

DTIC Science & Technology

1983-12-20

Defense Comunications Agency Olcy Attn XLA Washington, DC 20305 01cy Attn nTW-2 (ADR CNW D I: Attn Code 240 for) Olcy Attn NL-STN O Library Olcy Attn...Library Olcy Attn TIC-Library Olcy Attn R Welch Olcy Attn M Johnson Los Alamos National Scientific Lab. Mail Station 5000 Information Science, Inc. P

K-shuff: A Novel Algorithm for Characterizing Structural and Compositional Diversity in Gene Libraries.

PubMed

Jangid, Kamlesh; Kao, Ming-Hung; Lahamge, Aishwarya; Williams, Mark A; Rathbun, Stephen L; Whitman, William B

2016-01-01

K-shuff is a new algorithm for comparing the similarity of gene sequence libraries, providing measures of the structural and compositional diversity as well as the significance of the differences between these measures. Inspired by Ripley's K-function for spatial point pattern analysis, the Intra K-function or IKF measures the structural diversity, including both the richness and overall similarity of the sequences, within a library. The Cross K-function or CKF measures the compositional diversity between gene libraries, reflecting both the number of OTUs shared as well as the overall similarity in OTUs. A Monte Carlo testing procedure then enables statistical evaluation of both the structural and compositional diversity between gene libraries. For 16S rRNA gene libraries from complex bacterial communities such as those found in seawater, salt marsh sediments, and soils, K-shuff yields reproducible estimates of structural and compositional diversity with libraries greater than 50 sequences. Similarly, for pyrosequencing libraries generated from a glacial retreat chronosequence and Illumina® libraries generated from US homes, K-shuff required >300 and 100 sequences per sample, respectively. Power analyses demonstrated that K-shuff is sensitive to small differences in Sanger or Illumina® libraries. This extra sensitivity of K-shuff enabled examination of compositional differences at much deeper taxonomic levels, such as within abundant OTUs. This is especially useful when comparing communities that are compositionally very similar but functionally different. K-shuff will therefore prove beneficial for conventional microbiome analysis as well as specific hypothesis testing.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

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.

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.

Bio-Inspired Genetic Algorithms with Formalized Crossover Operators for Robotic Applications.

PubMed

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.

pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library.

PubMed

Röst, Hannes L; Schmitt, Uwe; Aebersold, Ruedi; Malmström, Lars

2014-01-01

pyOpenMS is an open-source, Python-based interface to the C++ OpenMS library, providing facile access to a feature-rich, open-source algorithm library for MS-based proteomics analysis. It contains Python bindings that allow raw access to the data structures and algorithms implemented in OpenMS, specifically those for file access (mzXML, mzML, TraML, mzIdentML among others), basic signal processing (smoothing, filtering, de-isotoping, and peak-picking) and complex data analysis (including label-free, SILAC, iTRAQ, and SWATH analysis tools). pyOpenMS thus allows fast prototyping and efficient workflow development in a fully interactive manner (using the interactive Python interpreter) and is also ideally suited for researchers not proficient in C++. In addition, our code to wrap a complex C++ library is completely open-source, allowing other projects to create similar bindings with ease. The pyOpenMS framework is freely available at https://pypi.python.org/pypi/pyopenms while the autowrap tool to create Cython code automatically is available at https://pypi.python.org/pypi/autowrap (both released under the 3-clause BSD licence). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SkData: data sets and algorithm evaluation protocols in Python

NASA Astrophysics Data System (ADS)

Bergstra, James; Pinto, Nicolas; Cox, David D.

2015-01-01

Machine learning benchmark data sets come in all shapes and sizes, whereas classification algorithms assume sanitized input, such as (x, y) pairs with vector-valued input x and integer class label y. Researchers and practitioners know all too well how tedious it can be to get from the URL of a new data set to a NumPy ndarray suitable for e.g. pandas or sklearn. The SkData library handles that work for a growing number of benchmark data sets (small and large) so that one-off in-house scripts for downloading and parsing data sets can be replaced with library code that is reliable, community-tested, and documented. The SkData library also introduces an open-ended formalization of training and testing protocols that facilitates direct comparison with published research. This paper describes the usage and architecture of the SkData library.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm

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.

Estimating the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm.

PubMed

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.

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…

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.

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.

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.

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.

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.

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…

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.

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.

A Genetic Algorithm Tool (splicer) for Complex Scheduling Problems and the Space Station Freedom Resupply Problem

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.

JPSS Cryosphere Algorithms: Integration and Testing in Algorithm Development Library (ADL)

NASA Astrophysics Data System (ADS)

Tsidulko, M.; Mahoney, R. L.; Meade, P.; Baldwin, D.; Tschudi, M. A.; Das, B.; Mikles, V. J.; Chen, W.; Tang, Y.; Sprietzer, K.; Zhao, Y.; Wolf, W.; Key, J.

2014-12-01

JPSS is a next generation satellite system that is planned to be launched in 2017. The satellites will carry a suite of sensors that are already on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite. The NOAA/NESDIS/STAR Algorithm Integration Team (AIT) works within the Algorithm Development Library (ADL) framework which mimics the operational JPSS Interface Data Processing Segment (IDPS). The AIT contributes in development, integration and testing of scientific algorithms employed in the IDPS. This presentation discusses cryosphere related activities performed in ADL. The addition of a new ancillary data set - NOAA Global Multisensor Automated Snow/Ice data (GMASI) - with ADL code modifications is described. Preliminary GMASI impact on the gridded Snow/Ice product is estimated. Several modifications to the Ice Age algorithm that demonstrates mis-classification of ice type for certain areas/time periods are tested in the ADL. Sensitivity runs for day time, night time and terminator zone are performed and presented. Comparisons between the original and modified versions of the Ice Age algorithm are also presented.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blom, Philip Stephen; Marcillo, Omar Eduardo; Euler, Garrett Gene

InfraPy is a Python-based analysis toolkit being development at LANL. The algorithms are intended for ground-based nuclear detonation detection applications to detect, locate, and characterize explosive sources using infrasonic observations. The implementation is usable as a stand-alone Python library or as a command line driven tool operating directly on a database. With multiple scientists working on the project, we've begun using a LANL git repository for collaborative development and version control. Current and planned work on InfraPy focuses on the development of new algorithms and propagation models. Collaboration with Southern Methodist University (SMU) has helped identify bugs and limitations ofmore » the algorithms. The current focus of usage development is focused on library imports and CLI.« less

Algorithm-Based Fault Tolerance for Numerical Subroutines

NASA Technical Reports Server (NTRS)

Tumon, Michael; Granat, Robert; Lou, John

2007-01-01

A software library implements a new methodology of detecting faults in numerical subroutines, thus enabling application programs that contain the subroutines to recover transparently from single-event upsets. The software library in question is fault-detecting middleware that is wrapped around the numericalsubroutines. Conventional serial versions (based on LAPACK and FFTW) and a parallel version (based on ScaLAPACK) exist. The source code of the application program that contains the numerical subroutines is not modified, and the middleware is transparent to the user. The methodology used is a type of algorithm- based fault tolerance (ABFT). In ABFT, a checksum is computed before a computation and compared with the checksum of the computational result; an error is declared if the difference between the checksums exceeds some threshold. Novel normalization methods are used in the checksum comparison to ensure correct fault detections independent of algorithm inputs. In tests of this software reported in the peer-reviewed literature, this library was shown to enable detection of 99.9 percent of significant faults while generating no false alarms.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries.

PubMed

O'Hagan, Steve; Kell, Douglas B

2018-01-01

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates. © 2017 The Authors. Biotechnology Journal Published by Wiley-VCH Verlag GmbH & Co. KGaA.

MorphoGraphX: A platform for quantifying morphogenesis in 4D

PubMed Central

Barbier de Reuille, Pierre; Routier-Kierzkowska, Anne-Lise; Kierzkowski, Daniel; Bassel, George W; Schüpbach, Thierry; Tauriello, Gerardo; Bajpai, Namrata; Strauss, Sören; Weber, Alain; Kiss, Annamaria; Burian, Agata; Hofhuis, Hugo; Sapala, Aleksandra; Lipowczan, Marcin; Heimlicher, Maria B; Robinson, Sarah; Bayer, Emmanuelle M; Basler, Konrad; Koumoutsakos, Petros; Roeder, Adrienne HK; Aegerter-Wilmsen, Tinri; Nakayama, Naomi; Tsiantis, Miltos; Hay, Angela; Kwiatkowska, Dorota; Xenarios, Ioannis; Kuhlemeier, Cris; Smith, Richard S

2015-01-01

Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX (www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth. DOI: http://dx.doi.org/10.7554/eLife.05864.001 PMID:25946108

Pure JavaScript Storyline Layout Algorithm

DOE Office of Scientific and Technical Information (OSTI.GOV)

This is a JavaScript library for a storyline layout algorithm. Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The library in this disclosure takes as input a list of objects containing an id, time, and state. The output is a data structure that can be used to conveniently render a storyline visualization. Most importantly, the library computes the y-coordinate of the entities over time that decreases layout artifacts including crossings, wiggles, and whitespace. This is accomplished through multi-objective, multi-stage optimizationmore » problem, where the output of one stage produces input and constraints for the next stage.« less

SOA-based digital library services and composition in biomedical applications.

PubMed

Zhao, Xia; Liu, Enjie; Clapworthy, Gordon J; Viceconti, Marco; Testi, Debora

2012-06-01

Carefully collected, high-quality data are crucial in biomedical visualization, and it is important that the user community has ready access to both this data and the high-performance computing resources needed by the complex, computational algorithms that will process it. Biological researchers generally require data, tools and algorithms from multiple providers to achieve their goals. This paper illustrates our response to the problems that result from this. The Living Human Digital Library (LHDL) project presented in this paper has taken advantage of Web Services to build a biomedical digital library infrastructure that allows clinicians and researchers not only to preserve, trace and share data resources, but also to collaborate at the data-processing level. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Writing DNA with GenoCAD.

PubMed

Czar, Michael J; Cai, Yizhi; Peccoud, Jean

2009-07-01

Chemical synthesis of custom DNA made to order calls for software streamlining the design of synthetic DNA sequences. GenoCAD (www.genocad.org) is a free web-based application to design protein expression vectors, artificial gene networks and other genetic constructs composed of multiple functional blocks called genetic parts. By capturing design strategies in grammatical models of DNA sequences, GenoCAD guides the user through the design process. By successively clicking on icons representing structural features or actual genetic parts, complex constructs composed of dozens of functional blocks can be designed in a matter of minutes. GenoCAD automatically derives the construct sequence from its comprehensive libraries of genetic parts. Upon completion of the design process, users can download the sequence for synthesis or further analysis. Users who elect to create a personal account on the system can customize their workspace by creating their own parts libraries, adding new parts to the libraries, or reusing designs to quickly generate sets of related constructs.

Information resources at the National Center for Biotechnology Information.

PubMed Central

Woodsmall, R M; Benson, D A

1993-01-01

The National Center for Biotechnology Information (NCBI), part of the National Library of Medicine, was established in 1988 to perform basic research in the field of computational molecular biology as well as build and distribute molecular biology databases. The basic research has led to new algorithms and analysis tools for interpreting genomic data and has been instrumental in the discovery of human disease genes for neurofibromatosis and Kallmann syndrome. The principal database responsibility is the National Institutes of Health (NIH) genetic sequence database, GenBank. NCBI, in collaboration with international partners, builds, distributes, and provides online and CD-ROM access to over 112,000 DNA sequences. Another major program is the integration of multiple sequences databases and related bibliographic information and the development of network-based retrieval systems for Internet access. PMID:8374583

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

DOE PAGES

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S.; ...

2017-02-23

Here, a newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides,more » important for metal extraction chemistry, are parametrized using ParFit.« less

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S.

Here, a newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides,more » important for metal extraction chemistry, are parametrized using ParFit.« less

Development of microsatellite markers from an enriched genomic library for genetic analysis of melon (Cucumis melo L.)

PubMed Central

Ritschel, Patricia Silva; Lins, Tulio Cesar de Lima; Tristan, Rodrigo Lourenço; Buso, Gláucia Salles Cortopassi; Buso, José Amauri; Ferreira, Márcio Elias

2004-01-01

Background Despite the great advances in genomic technology observed in several crop species, the availability of molecular tools such as microsatellite markers has been limited in melon (Cucumis melo L.) and cucurbit species. The development of microsatellite markers will have a major impact on genetic analysis and breeding of melon, especially on the generation of marker saturated genetic maps and implementation of marker assisted breeding programs. Genomic microsatellite enriched libraries can be an efficient alternative for marker development in such species. Results Seven hundred clones containing microsatellite sequences from a Tsp-AG/TC microsatellite enriched library were identified and one-hundred and forty-four primer pairs designed and synthesized. When 67 microsatellite markers were tested on a panel of melon and other cucurbit accessions, 65 revealed DNA polymorphisms among the melon accessions. For some cucurbit species, such as Cucumis sativus, up to 50% of the melon microsatellite markers could be readily used for DNA polymophism assessment, representing a significant reduction of marker development costs. A random sample of 25 microsatellite markers was extracted from the new microsatellite marker set and characterized on 40 accessions of melon, generating an allelic frequency database for the species. The average expected heterozygosity was 0.52, varying from 0.45 to 0.70, indicating that a small set of selected markers should be sufficient to solve questions regarding genotype identity and variety protection. Genetic distances based on microsatellite polymorphism were congruent with data obtained from RAPD marker analysis. Mapping analysis was initiated with 55 newly developed markers and most primers showed segregation according to Mendelian expectations. Linkage analysis detected linkage between 56% of the markers, distributed in nine linkage groups. Conclusions Genomic library microsatellite enrichment is an efficient procedure for marker development in melon. One-hundred and forty-four new markers were developed from Tsp-AG/TC genomic library. This is the first reported attempt of successfully using enriched library for microsatellite marker development in the species. A sample of the microsatellite markers tested proved efficient for genetic analysis of melon, including genetic distance estimates and identity tests. Linkage analysis indicated that the markers developed are dispersed throughout the genome and should be very useful for genetic analysis of melon. PMID:15149552

Evaluation of methods to produce an image library for automatic patient model localization for dose mapping during fluoroscopically guided procedures

NASA Astrophysics Data System (ADS)

Kilian-Meneghin, Josh; Xiong, Z.; Rudin, S.; Oines, A.; Bednarek, D. R.

2017-03-01

The purpose of this work is to evaluate methods for producing a library of 2D-radiographic images to be correlated to clinical images obtained during a fluoroscopically-guided procedure for automated patient-model localization. The localization algorithm will be used to improve the accuracy of the skin-dose map superimposed on the 3D patient- model of the real-time Dose-Tracking-System (DTS). For the library, 2D images were generated from CT datasets of the SK-150 anthropomorphic phantom using two methods: Schmid's 3D-visualization tool and Plastimatch's digitally-reconstructed-radiograph (DRR) code. Those images, as well as a standard 2D-radiographic image, were correlated to a 2D-fluoroscopic image of a phantom, which represented the clinical-fluoroscopic image, using the Corr2 function in Matlab. The Corr2 function takes two images and outputs the relative correlation between them, which is fed into the localization algorithm. Higher correlation means better alignment of the 3D patient-model with the patient image. In this instance, it was determined that the localization algorithm will succeed when Corr2 returns a correlation of at least 50%. The 3D-visualization tool images returned 55-80% correlation relative to the fluoroscopic-image, which was comparable to the correlation for the radiograph. The DRR images returned 61-90% correlation, again comparable to the radiograph. Both methods prove to be sufficient for the localization algorithm and can be produced quickly; however, the DRR method produces more accurate grey-levels. Using the DRR code, a library at varying angles can be produced for the localization algorithm.

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.

VIP: Vortex Image Processing Package for High-contrast Direct Imaging

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, Carlos Alberto; Wertz, Olivier; Absil, Olivier; Christiaens, Valentin; Defrère, Denis; Mawet, Dimitri; Milli, Julien; Absil, Pierre-Antoine; Van Droogenbroeck, Marc; Cantalloube, Faustine; Hinz, Philip M.; Skemer, Andrew J.; Karlsson, Mikael; Surdej, Jean

2017-07-01

We present the Vortex Image Processing (VIP) library, a python package dedicated to astronomical high-contrast imaging. Our package relies on the extensive python stack of scientific libraries and aims to provide a flexible framework for high-contrast data and image processing. In this paper, we describe the capabilities of VIP related to processing image sequences acquired using the angular differential imaging (ADI) observing technique. VIP implements functionalities for building high-contrast data processing pipelines, encompassing pre- and post-processing algorithms, potential source position and flux estimation, and sensitivity curve generation. Among the reference point-spread function subtraction techniques for ADI post-processing, VIP includes several flavors of principal component analysis (PCA) based algorithms, such as annular PCA and incremental PCA algorithms capable of processing big datacubes (of several gigabytes) on a computer with limited memory. Also, we present a novel ADI algorithm based on non-negative matrix factorization, which comes from the same family of low-rank matrix approximations as PCA and provides fairly similar results. We showcase the ADI capabilities of the VIP library using a deep sequence on HR 8799 taken with the LBTI/LMIRCam and its recently commissioned L-band vortex coronagraph. Using VIP, we investigated the presence of additional companions around HR 8799 and did not find any significant additional point source beyond the four known planets. VIP is available at http://github.com/vortex-exoplanet/VIP and is accompanied with Jupyter notebook tutorials illustrating the main functionalities of the library.

Barcoding the butterflies of southern South America: Species delimitation efficacy, cryptic diversity and geographic patterns of divergence.

PubMed

Lavinia, Pablo D; Núñez Bustos, Ezequiel O; Kopuchian, Cecilia; Lijtmaer, Darío A; García, Natalia C; Hebert, Paul D N; Tubaro, Pablo L

2017-01-01

Because the tropical regions of America harbor the highest concentration of butterfly species, its fauna has attracted considerable attention. Much less is known about the butterflies of southern South America, particularly Argentina, where over 1,200 species occur. To advance understanding of this fauna, we assembled a DNA barcode reference library for 417 butterfly species of Argentina, focusing on the Atlantic Forest, a biodiversity hotspot. We tested the efficacy of this library for specimen identification, used it to assess the frequency of cryptic species, and examined geographic patterns of genetic variation, making this study the first large-scale genetic assessment of the butterflies of southern South America. The average sequence divergence to the nearest neighbor (i.e. minimum interspecific distance) was 6.91%, ten times larger than the mean distance to the furthest conspecific (0.69%), with a clear barcode gap present in all but four of the species represented by two or more specimens. As a consequence, the DNA barcode library was extremely effective in the discrimination of these species, allowing a correct identification in more than 95% of the cases. Singletons (i.e. species represented by a single sequence) were also distinguishable in the gene trees since they all had unique DNA barcodes, divergent from those of the closest non-conspecific. The clustering algorithms implemented recognized from 416 to 444 barcode clusters, suggesting that the actual diversity of butterflies in Argentina is 3%-9% higher than currently recognized. Furthermore, our survey added three new records of butterflies for the country (Eurema agave, Mithras hannelore, Melanis hillapana). In summary, this study not only supported the utility of DNA barcoding for the identification of the butterfly species of Argentina, but also highlighted several cases of both deep intraspecific and shallow interspecific divergence that should be studied in more detail.

Barcoding the butterflies of southern South America: Species delimitation efficacy, cryptic diversity and geographic patterns of divergence

PubMed Central

Núñez Bustos, Ezequiel O.; Kopuchian, Cecilia; Lijtmaer, Darío A.; García, Natalia C.; Hebert, Paul D. N.; Tubaro, Pablo L.

2017-01-01

Because the tropical regions of America harbor the highest concentration of butterfly species, its fauna has attracted considerable attention. Much less is known about the butterflies of southern South America, particularly Argentina, where over 1,200 species occur. To advance understanding of this fauna, we assembled a DNA barcode reference library for 417 butterfly species of Argentina, focusing on the Atlantic Forest, a biodiversity hotspot. We tested the efficacy of this library for specimen identification, used it to assess the frequency of cryptic species, and examined geographic patterns of genetic variation, making this study the first large-scale genetic assessment of the butterflies of southern South America. The average sequence divergence to the nearest neighbor (i.e. minimum interspecific distance) was 6.91%, ten times larger than the mean distance to the furthest conspecific (0.69%), with a clear barcode gap present in all but four of the species represented by two or more specimens. As a consequence, the DNA barcode library was extremely effective in the discrimination of these species, allowing a correct identification in more than 95% of the cases. Singletons (i.e. species represented by a single sequence) were also distinguishable in the gene trees since they all had unique DNA barcodes, divergent from those of the closest non-conspecific. The clustering algorithms implemented recognized from 416 to 444 barcode clusters, suggesting that the actual diversity of butterflies in Argentina is 3%–9% higher than currently recognized. Furthermore, our survey added three new records of butterflies for the country (Eurema agave, Mithras hannelore, Melanis hillapana). In summary, this study not only supported the utility of DNA barcoding for the identification of the butterfly species of Argentina, but also highlighted several cases of both deep intraspecific and shallow interspecific divergence that should be studied in more detail. PMID:29049373

Construction of the BAC Library of Small Abalone (Haliotis diversicolor) for Gene Screening and Genome Characterization.

PubMed

Jiang, Likun; You, Weiwei; Zhang, Xiaojun; Xu, Jian; Jiang, Yanliang; Wang, Kai; Zhao, Zixia; Chen, Baohua; Zhao, Yunfeng; Mahboob, Shahid; Al-Ghanim, Khalid A; Ke, Caihuan; Xu, Peng

2016-02-01

The small abalone (Haliotis diversicolor) is one of the most important aquaculture species in East Asia. To facilitate gene cloning and characterization, genome analysis, and genetic breeding of it, we constructed a large-insert bacterial artificial chromosome (BAC) library, which is an important genetic tool for advanced genetics and genomics research. The small abalone BAC library includes 92,610 clones with an average insert size of 120 Kb, equivalent to approximately 7.6× of the small abalone genome. We set up three-dimensional pools and super pools of 18,432 BAC clones for target gene screening using PCR method. To assess the approach, we screened 12 target genes in these 18,432 BAC clones and identified 16 positive BAC clones. Eight positive BAC clones were then sequenced and assembled with the next generation sequencing platform. The assembled contigs representing these 8 BAC clones spanned 928 Kb of the small abalone genome, providing the first batch of genome sequences for genome evaluation and characterization. The average GC content of small abalone genome was estimated as 40.33%. A total of 21 protein-coding genes, including 7 target genes, were annotated into the 8 BACs, which proved the feasibility of PCR screening approach with three-dimensional pools in small abalone BAC library. One hundred fifty microsatellite loci were also identified from the sequences for marker development in the future. The BAC library and clone pools provided valuable resources and tools for genetic breeding and conservation of H. diversicolor.

ODTbrain: a Python library for full-view, dense diffraction tomography.

PubMed

Müller, Paul; Schürmann, Mirjam; Guck, Jochen

2015-11-04

Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far. We present a Python library that implements the backpropagation algorithm for diffraction tomography in 3D. By establishing benchmarks based on finite-difference time-domain (FDTD) simulations, we showcase the superiority of the backpropagation algorithm over the backprojection algorithm. Furthermore, we discuss how measurment parameters influence the reconstructed refractive index distribution and we also give insights into the applicability of diffraction tomography to biological cells. The present software library contains a robust implementation of the backpropagation algorithm. The algorithm is ideally suited for the application to biological cells. Furthermore, the implementation is a drop-in replacement for the classical backprojection algorithm and is made available to the large user community of the Python programming language.

Some Determinants of Journal Holding Patterns in Academic Libraries.

ERIC Educational Resources Information Center

McCain, Katherine W.

1992-01-01

Reports results of a study that examined academic library holdings for core journals in genetics and economics to identify quantitative and qualitative journal characteristics that are predictors of the number of libraries subscribing to the journal. Variables investigated include subject area, publisher, price, longevity, impact factor, prestige…

Alzheimer's Disease Diagnosis in Individual Subjects using Structural MR Images: Validation Studies

PubMed Central

Vemuri, Prashanthi; Gunter, Jeffrey L.; Senjem, Matthew L.; Whitwell, Jennifer L.; Kantarci, Kejal; Knopman, David S.; Boeve, Bradley F.; Petersen, Ronald C.; Jack, Clifford R.

2008-01-01

OBJECTIVE To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM) based classification of structural MR (sMR) images. BACKGROUND Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects. METHODS 190 patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training was done by four-fold cross validation. The remaining independent sample of 50 AD and 50 CN were used to obtain a minimally biased estimate of the generalization error of the algorithm. RESULTS The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3% respectively and the developed models generalized well on the independent test datasets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology. CONCLUSIONS This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy. PMID:18054253

Registry in a tube: multiplexed pools of retrievable parts for genetic design space exploration.

PubMed

Woodruff, Lauren B A; Gorochowski, Thomas E; Roehner, Nicholas; Mikkelsen, Tarjei S; Densmore, Douglas; Gordon, D Benjamin; Nicol, Robert; Voigt, Christopher A

2017-02-17

Genetic designs can consist of dozens of genes and hundreds of genetic parts. After evaluating a design, it is desirable to implement changes without the cost and burden of starting the construction process from scratch. Here, we report a two-step process where a large design space is divided into deep pools of composite parts, from which individuals are retrieved and assembled to build a final construct. The pools are built via multiplexed assembly and sequenced using next-generation sequencing. Each pool consists of ∼20 Mb of up to 5000 unique and sequence-verified composite parts that are barcoded for retrieval by PCR. This approach is applied to a 16-gene nitrogen fixation pathway, which is broken into pools containing a total of 55 848 composite parts (71.0 Mb). The pools encompass an enormous design space (1043 possible 23 kb constructs), from which an algorithm-guided 192-member 4.5 Mb library is built. Next, all 1030 possible genetic circuits based on 10 repressors (NOR/NOT gates) are encoded in pools where each repressor is fused to all permutations of input promoters. These demonstrate that multiplexing can be applied to encompass entire design spaces from which individuals can be accessed and evaluated. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

K-shuff: A Novel Algorithm for Characterizing Structural and Compositional Diversity in Gene Libraries

PubMed Central

Jangid, Kamlesh; Kao, Ming-Hung; Lahamge, Aishwarya; Williams, Mark A.; Rathbun, Stephen L.; Whitman, William B.

2016-01-01

K-shuff is a new algorithm for comparing the similarity of gene sequence libraries, providing measures of the structural and compositional diversity as well as the significance of the differences between these measures. Inspired by Ripley’s K-function for spatial point pattern analysis, the Intra K-function or IKF measures the structural diversity, including both the richness and overall similarity of the sequences, within a library. The Cross K-function or CKF measures the compositional diversity between gene libraries, reflecting both the number of OTUs shared as well as the overall similarity in OTUs. A Monte Carlo testing procedure then enables statistical evaluation of both the structural and compositional diversity between gene libraries. For 16S rRNA gene libraries from complex bacterial communities such as those found in seawater, salt marsh sediments, and soils, K-shuff yields reproducible estimates of structural and compositional diversity with libraries greater than 50 sequences. Similarly, for pyrosequencing libraries generated from a glacial retreat chronosequence and Illumina® libraries generated from US homes, K-shuff required >300 and 100 sequences per sample, respectively. Power analyses demonstrated that K-shuff is sensitive to small differences in Sanger or Illumina® libraries. This extra sensitivity of K-shuff enabled examination of compositional differences at much deeper taxonomic levels, such as within abundant OTUs. This is especially useful when comparing communities that are compositionally very similar but functionally different. K-shuff will therefore prove beneficial for conventional microbiome analysis as well as specific hypothesis testing. PMID:27911946

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.

A Comparative Study of Optimization Algorithms for Engineering Synthesis.

DTIC Science & Technology

1983-03-01

the ADS program demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular...demonstrates the flexibility a design engineer would have in selecting an optimization algorithm best suited to solve a particular problem. 4 TABLE OF...algorithm to suit a particular problem. The ADS library of design optimization algorithms was . developed by Vanderplaats in response to the first

Cognitive Nonlinear Radar

DTIC Science & Technology

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

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.

A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

PubMed

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.

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.

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.

Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

PubMed Central

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

Scalability problems of simple genetic algorithms.

PubMed

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.

Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

PubMed

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.

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.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

Diversity of Marine-Derived Fungal Cultures Exposed by DNA Barcodes: The Algorithm Matters

PubMed Central

Andreakis, Nikos; Høj, Lone; Kearns, Philip; Hall, Michael R.; Ericson, Gavin; Cobb, Rose E.; Gordon, Benjamin R.; Evans-Illidge, Elizabeth

2015-01-01

Marine fungi are an understudied group of eukaryotic microorganisms characterized by unresolved genealogies and unstable classification. Whereas DNA barcoding via the nuclear ribosomal internal transcribed spacer (ITS) provides a robust and rapid tool for fungal species delineation, accurate classification of fungi is often arduous given the large number of partial or unknown barcodes and misidentified isolates deposited in public databases. This situation is perpetuated by a paucity of cultivable fungal strains available for phylogenetic research linked to these data sets. We analyze ITS barcodes produced from a subsample (290) of 1781 cultured isolates of marine-derived fungi in the Bioresources Library located at the Australian Institute of Marine Science (AIMS). Our analysis revealed high levels of under-explored fungal diversity. The majority of isolates were ascomycetes including representatives of the subclasses Eurotiomycetidae, Hypocreomycetidae, Sordariomycetidae, Pleosporomycetidae, Dothideomycetidae, Xylariomycetidae and Saccharomycetidae. The phylum Basidiomycota was represented by isolates affiliated with the genera Tritirachium and Tilletiopsis. BLAST searches revealed 26 unknown OTUs and 50 isolates corresponding to previously uncultured, unidentified fungal clones. This study makes a significant addition to the availability of barcoded, culturable marine-derived fungi for detailed future genomic and physiological studies. We also demonstrate the influence of commonly used alignment algorithms and genetic distance measures on the accuracy and comparability of estimating Operational Taxonomic Units (OTUs) by the automatic barcode gap finder (ABGD) method. Large scale biodiversity screening programs that combine datasets using algorithmic OTU delineation pipelines need to ensure compatible algorithms have been used because the algorithm matters. PMID:26308620

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.

Construction and Analysis of Siberian Tiger Bacterial Artificial Chromosome Library with Approximately 6.5-Fold Genome Equivalent Coverage

PubMed Central

Liu, Changqing; Bai, Chunyu; Guo, Yu; Liu, Dan; Lu, Taofeng; Li, Xiangchen; Ma, Jianzhang; Ma, Yuehui; Guan, Weijun

2014-01-01

Bacterial artificial chromosome (BAC) libraries are extremely valuable for the genome-wide genetic dissection of complex organisms. The Siberian tiger, one of the most well-known wild primitive carnivores in China, is an endangered animal. In order to promote research on its genome, a high-redundancy BAC library of the Siberian tiger was constructed and characterized. The library is divided into two sub-libraries prepared from blood cells and two sub-libraries prepared from fibroblasts. This BAC library contains 153,600 individually archived clones; for PCR-based screening of the library, BACs were placed into 40 superpools of 10 × 384-deep well microplates. The average insert size of BAC clones was estimated to be 116.5 kb, representing approximately 6.46 genome equivalents of the haploid genome and affording a 98.86% statistical probability of obtaining at least one clone containing a unique DNA sequence. Screening the library with 19 microsatellite markers and a SRY sequence revealed that each of these markers were present in the library; the average number of positive clones per marker was 6.74 (range 2 to 12), consistent with 6.46 coverage of the tiger genome. Additionally, we identified 72 microsatellite markers that could potentially be used as genetic markers. This BAC library will serve as a valuable resource for physical mapping, comparative genomic study and large-scale genome sequencing in the tiger. PMID:24608928

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.

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.

Polishing the craft of genetic diversity creation in directed evolution.

PubMed

Tee, Kang Lan; Wong, Tuck Seng

2013-12-01

Genetic diversity creation is a core technology in directed evolution where a high quality mutant library is crucial to its success. Owing to its importance, the technology in genetic diversity creation has seen rapid development over the years and its application has diversified into other fields of scientific research. The advances in molecular cloning and mutagenesis since 2008 were reviewed. Specifically, new cloning techniques were classified based on their principles of complementary overhangs, homologous sequences, overlapping PCR and megaprimers and the advantages, drawbacks and performances of these methods were highlighted. New mutagenesis methods developed for random mutagenesis, focused mutagenesis and DNA recombination were surveyed. The technical requirements of these methods and the mutational spectra were compared and discussed with references to commonly used techniques. The trends of mutant library preparation were summarised. Challenges in genetic diversity creation were discussed with emphases on creating "smart" libraries, controlling the mutagenesis spectrum and specific challenges in each group of mutagenesis methods. An outline of the wider applications of genetic diversity creation includes genome engineering, viral evolution, metagenomics and a study of protein functions. The review ends with an outlook for genetic diversity creation and the prospective developments that can have future impact in this field. © 2013. Published by Elsevier Inc. All rights reserved.

Estimates of the atmospheric parameters of M-type stars: a machine-learning perspective

NASA Astrophysics Data System (ADS)

Sarro, L. M.; Ordieres-Meré, J.; Bello-García, A.; González-Marcos, A.; Solano, E.

2018-05-01

Estimating the atmospheric parameters of M-type stars has been a difficult task due to the lack of simple diagnostics in the stellar spectra. We aim at uncovering good sets of predictive features of stellar atmospheric parameters (Teff, log (g), [M/H]) in spectra of M-type stars. We define two types of potential features (equivalent widths and integrated flux ratios) able to explain the atmospheric physical parameters. We search the space of feature sets using a genetic algorithm that evaluates solutions by their prediction performance in the framework of the BT-Settl library of stellar spectra. Thereafter, we construct eight regression models using different machine-learning techniques and compare their performances with those obtained using the classical χ2 approach and independent component analysis (ICA) coefficients. Finally, we validate the various alternatives using two sets of real spectra from the NASA Infrared Telescope Facility (IRTF) and Dwarf Archives collections. We find that the cross-validation errors are poor measures of the performance of regression models in the context of physical parameter prediction in M-type stars. For R ˜ 2000 spectra with signal-to-noise ratios typical of the IRTF and Dwarf Archives, feature selection with genetic algorithms or alternative techniques produces only marginal advantages with respect to representation spaces that are unconstrained in wavelength (full spectrum or ICA). We make available the atmospheric parameters for the two collections of observed spectra as online material.

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.

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.

A Strassen-Newton algorithm for high-speed parallelizable matrix inversion

NASA Technical Reports Server (NTRS)

Bailey, David H.; Ferguson, Helaman R. P.

1988-01-01

Techniques are described for computing matrix inverses by algorithms that are highly suited to massively parallel computation. The techniques are based on an algorithm suggested by Strassen (1969). Variations of this scheme use matrix Newton iterations and other methods to improve the numerical stability while at the same time preserving a very high level of parallelism. One-processor Cray-2 implementations of these schemes range from one that is up to 55 percent faster than a conventional library routine to one that is slower than a library routine but achieves excellent numerical stability. The problem of computing the solution to a single set of linear equations is discussed, and it is shown that this problem can also be solved efficiently using these techniques.

[RNA interference library research progress and its application in cancer research].

PubMed

Zhao, Ning; Cai, Li

2013-02-01

RNA interference is a homologous mRNA special degradation phenomenon which is caused by the double-stranded RNA. RNAi library is a pooled library that is artificially constructed using RNAi technology. As RNAi library has made a major breakthrough in the field of genetic research, it has been widely used in the field of medical research, especially in the field of cancer research. This review discussed the research progress of RNAi library and its applications in cancer research.

Microsatellite DNA library for Caiman latirostris.

PubMed

Zucoloto, Rodrigo Barban; Verdade, Luciano Martins; Coutinho, Luiz Lehmann

2002-12-15

New genetic markers were characterized for the broad-snouted caiman (Caiman latirostris) by constructing libraries enriched for microsatellite DNA. Construction and characterization of these libraries are described in the present study. One microsatellite marker was developed from a (ACC-TGG)(n)enriched microsatellite DNA library, and 12 microsatellite markers were developed from a (AC-TG)(n)enriched microsatellite DNA library. These markers were tested in wild-caught animals, and these tests resulted in ten new polymorphic microsatellites for C. latirostris. Copyright 2002 Wiley-Liss, Inc.

Genetic Algorithm Calibration of Probabilistic Cellular Automata for Modeling Mining Permit Activity

USGS Publications Warehouse

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.

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.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

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

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

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.

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

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.

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…

Breeding and Genetics Symposium: really big data: processing and analysis of very large data sets.

PubMed

Cole, J B; Newman, S; Foertter, F; Aguilar, I; Coffey, M

2012-03-01

Modern animal breeding data sets are large and getting larger, due in part to recent availability of high-density SNP arrays and cheap sequencing technology. High-performance computing methods for efficient data warehousing and analysis are under development. Financial and security considerations are important when using shared clusters. Sound software engineering practices are needed, and it is better to use existing solutions when possible. Storage requirements for genotypes are modest, although full-sequence data will require greater storage capacity. Storage requirements for intermediate and results files for genetic evaluations are much greater, particularly when multiple runs must be stored for research and validation studies. The greatest gains in accuracy from genomic selection have been realized for traits of low heritability, and there is increasing interest in new health and management traits. The collection of sufficient phenotypes to produce accurate evaluations may take many years, and high-reliability proofs for older bulls are needed to estimate marker effects. Data mining algorithms applied to large data sets may help identify unexpected relationships in the data, and improved visualization tools will provide insights. Genomic selection using large data requires a lot of computing power, particularly when large fractions of the population are genotyped. Theoretical improvements have made possible the inversion of large numerator relationship matrices, permitted the solving of large systems of equations, and produced fast algorithms for variance component estimation. Recent work shows that single-step approaches combining BLUP with a genomic relationship (G) matrix have similar computational requirements to traditional BLUP, and the limiting factor is the construction and inversion of G for many genotypes. A naïve algorithm for creating G for 14,000 individuals required almost 24 h to run, but custom libraries and parallel computing reduced that to 15 m. Large data sets also create challenges for the delivery of genetic evaluations that must be overcome in a way that does not disrupt the transition from conventional to genomic evaluations. Processing time is important, especially as real-time systems for on-farm decisions are developed. The ultimate value of these systems is to decrease time-to-results in research, increase accuracy in genomic evaluations, and accelerate rates of genetic improvement.

Open source clustering software.

PubMed

de Hoon, M J L; Imoto, S; Nolan, J; Miyano, S

2004-06-12

We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

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.

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.

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.

Flexible Space-Filling Designs for Complex System Simulations

DTIC Science & Technology

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

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.

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.

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.

How enhanced molecular ions in Cold EI improve compound identification by the NIST library.

PubMed

Alon, Tal; Amirav, Aviv

2015-12-15

Library-based compound identification with electron ionization (EI) mass spectrometry (MS) is a well-established identification method which provides the names and structures of sample compounds up to the isomer level. The library (such as NIST) search algorithm compares different EI mass spectra in the library's database with the measured EI mass spectrum, assigning each of them a similarity score called 'Match' and an overall identification probability. Cold EI, electron ionization of vibrationally cold molecules in supersonic molecular beams, provides mass spectra with all the standard EI fragment ions combined with enhanced Molecular Ions and high-mass fragments. As a result, Cold EI mass spectra differ from those provided by standard EI and tend to yield lower matching scores. However, in most cases, library identification actually improves with Cold EI, as library identification probabilities for the correct library mass spectra increase, despite the lower matching factors. This research examined the way that enhanced molecular ion abundances affect library identification probability and the way that Cold EI mass spectra, which include enhanced molecular ions and high-mass fragment ions, typically improve library identification results. It involved several computer simulations, which incrementally modified the relative abundances of the various ions and analyzed the resulting mass spectra. The simulation results support previous measurements, showing that while enhanced molecular ion and high-mass fragment ions lower the matching factor of the correct library compound, the matching factors of the incorrect library candidates are lowered even more, resulting in a rise in the identification probability for the correct compound. This behavior which was previously observed by analyzing Cold EI mass spectra can be explained by the fact that high-mass ions, and especially the molecular ion, characterize a compound more than low-mass ions and therefore carries more weight in library search identification algorithms. These ions are uniquely abundant in Cold EI, which therefore enables enhanced compound characterization along with improved NIST library based identification. Copyright © 2015 John Wiley & Sons, Ltd.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

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

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.

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.

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.

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR

PubMed Central

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

Sequential and Mixed Genetic Algorithm and Learning Automata (SGALA, MGALA) for Feature Selection in QSAR.

PubMed

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.

Information retrieval algorithms: A survey

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raghavan, P.

We give an overview of some algorithmic problems arising in the representation of text/image/multimedia objects in a form amenable to automated searching, and in conducting these searches efficiently. These operations are central to information retrieval and digital library systems.

An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

PubMed Central

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

An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

PubMed

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.

Construction of a general human chromosome jumping library, with application to cystic fibrosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, F.S.; Drumm, M.L.; Cole, J.L.

1987-02-27

In many genetic disorders, the responsible gene and its protein product are unknown. The technique known as reverse genetics, in which chromosomal map positions and genetically linked DNA markers are used to identify and clone such genes, is complicated by the fact that the molecular distances from the closest DNA markers to the gene itself are often too large to traverse by standard cloning techniques. To address this situation, a general human chromosome jumping library was constructed that allows the cloning of DNA sequences approximately 100 kilobases away from any starting point in genomic DNA. As an illustration of itsmore » usefulness, this library was searched for a jumping clone, starting at the met oncogene, which is a marker tightly linked to the cystic fibrosis gene that is located on human chromosome 7. Mapping of the new genomic fragment by pulsed field gel electrophoresis confirmed that it resides on chromosome 7 within 240 kilobases downstream of the met gene. The use of chromosome jumping should be applicable to any genetic locus for which a closely linked DNA marker is available.« less

Highly efficient spatial data filtering in parallel using the opensource library CPPPO

NASA Astrophysics Data System (ADS)

Municchi, Federico; Goniva, Christoph; Radl, Stefan

2016-10-01

CPPPO is a compilation of parallel data processing routines developed with the aim to create a library for "scale bridging" (i.e. connecting different scales by mean of closure models) in a multi-scale approach. CPPPO features a number of parallel filtering algorithms designed for use with structured and unstructured Eulerian meshes, as well as Lagrangian data sets. In addition, data can be processed on the fly, allowing the collection of relevant statistics without saving individual snapshots of the simulation state. Our library is provided with an interface to the widely-used CFD solver OpenFOAM®, and can be easily connected to any other software package via interface modules. Also, we introduce a novel, extremely efficient approach to parallel data filtering, and show that our algorithms scale super-linearly on multi-core clusters. Furthermore, we provide a guideline for choosing the optimal Eulerian cell selection algorithm depending on the number of CPU cores used. Finally, we demonstrate the accuracy and the parallel scalability of CPPPO in a showcase focusing on heat and mass transfer from a dense bed of particles.

Pose estimation for augmented reality applications using genetic algorithm.

PubMed

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.

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.

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 ...

Creating Engaging Online Learning Material with the JSAV JavaScript Algorithm Visualization Library

ERIC Educational Resources Information Center

Karavirta, Ville; Shaffer, Clifford A.

2016-01-01

Data Structures and Algorithms are a central part of Computer Science. Due to their abstract and dynamic nature, they are a difficult topic to learn for many students. To alleviate these learning difficulties, instructors have turned to algorithm visualizations (AV) and AV systems. Research has shown that especially engaging AVs can have an impact…

Engineered Intrinsic Bioremediation of Ammonium Perchlorate in Groundwater

DTIC Science & Technology

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

New algorithm for tensor contractions on multi-core CPUs, GPUs, and accelerators enables CCSD and EOM-CCSD calculations with over 1000 basis functions on a single compute node.

PubMed

Kaliman, Ilya A; Krylov, Anna I

2017-04-30

A new hardware-agnostic contraction algorithm for tensors of arbitrary symmetry and sparsity is presented. The algorithm is implemented as a stand-alone open-source code libxm. This code is also integrated with general tensor library libtensor and with the Q-Chem quantum-chemistry package. An overview of the algorithm, its implementation, and benchmarks are presented. Similarly to other tensor software, the algorithm exploits efficient matrix multiplication libraries and assumes that tensors are stored in a block-tensor form. The distinguishing features of the algorithm are: (i) efficient repackaging of the individual blocks into large matrices and back, which affords efficient graphics processing unit (GPU)-enabled calculations without modifications of higher-level codes; (ii) fully asynchronous data transfer between disk storage and fast memory. The algorithm enables canonical all-electron coupled-cluster and equation-of-motion coupled-cluster calculations with single and double substitutions (CCSD and EOM-CCSD) with over 1000 basis functions on a single quad-GPU machine. We show that the algorithm exhibits predicted theoretical scaling for canonical CCSD calculations, O(N 6 ), irrespective of the data size on disk. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

[Algorithm of toxigenic genetically altered Vibrio cholerae El Tor biovar strain identification].

PubMed

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.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

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.

The Modular Modeling System (MMS): User's Manual

USGS Publications Warehouse

Leavesley, G.H.; Restrepo, Pedro J.; Markstrom, S.L.; Dixon, M.; Stannard, L.G.

1996-01-01

The Modular Modeling System (MMS) is an integrated system of computer software that has been developed to provide the research and operational framework needed to support development, testing, and evaluation of physical-process algorithms and to facilitate integration of user-selected sets of algorithms into operational physical-process models. MMS uses a module library that contains modules for simulating a variety of water, energy, and biogeochemical processes. A model is created by selectively coupling the most appropriate modules from the library to create a 'suitable' model for the desired application. Where existing modules do not provide appropriate process algorithms, new modules can be developed. The MMS user's manual provides installation instructions and a detailed discussion of system concepts, module development, and model development and application using the MMS graphical user interface.

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.

AstroCV: Astronomy computer vision library

NASA Astrophysics Data System (ADS)

González, Roberto E.; Muñoz, Roberto P.; Hernández, Cristian A.

2018-04-01

AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.

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.

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.

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

Critical Features of Fragment Libraries for Protein Structure Prediction

PubMed Central

dos Santos, Karina Baptista

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction. PMID:28085928

Critical Features of Fragment Libraries for Protein Structure Prediction.

PubMed

Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

2017-01-01

The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

Comparison of manual and automated AmpliSeq™ workflows in the typing of a Somali population with the Precision ID Identity Panel.

PubMed

van der Heijden, Suzanne; de Oliveira, Susanne Juel; Kampmann, Marie-Louise; Børsting, Claus; Morling, Niels

2017-11-01

The Precision ID Identity Panel was used to type 109 Somali individuals in order to obtain allele frequencies for the Somali population. These frequencies were used to establish a Somali HID-SNP database, which will be used for the biostatistic calculations in family and immigration cases. Genotypes obtained with the Precision ID Identity Panel were found to be almost in complete concordance with genotypes obtained with the SNPforID PCR-SBE-CE assay. In seven SNP loci, silent alleles were identified, of which most were previously described in the literature. The project also set out to compare different AmpliSeq™ workflows to investigate the possibility of using automated library building in forensic genetic case work. In order to do so, the SNP typing of the Somalis was performed using three different workflows: 1) manual library building and sequencing on the Ion PGM™, 2) automated library building using the Biomek ® 3000 and sequencing on the Ion PGM™, and 3) automated library building using the Ion Chef™ and sequencing on the Ion S5™. AmpliSeq™ workflows were compared based on coverage, locus balance, noise, and heterozygote balance. Overall, the Ion Chef™/Ion S5™ workflow was found to give the best results and required least hands-on time in the laboratory. However, the Ion Chef™/Ion S5™ workflow was also the most expensive. The number of libraries that may be constructed in one Ion Chef™ library building run was limited to eight, which is too little for high throughput workflows. The Biomek ® 3000/Ion PGM™ workflow was found to perform similarly to the manual/Ion PGM™ workflow. This argues for the use of automated library building in forensic genetic case work. Automated library building decreases the workload of the laboratory staff, decreases the risk of pipetting errors, and simplifies the daily workflow in forensic genetic laboratories. Copyright © 2017 Elsevier B.V. All rights reserved.

Performance Analysis of Combined Methods of Genetic Algorithm and K-Means Clustering in Determining the Value of Centroid

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.

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

PubMed

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.

Model-Checking with Edge-Valued Decision Diagrams

NASA Technical Reports Server (NTRS)

Roux, Pierre; Siminiceanu, Radu I.

2010-01-01

We describe an algebra of Edge-Valued Decision Diagrams (EVMDDs) to encode arithmetic functions and its implementation in a model checking library along with state-of-the-art algorithms for building the transition relation and the state space of discrete state systems. We provide efficient algorithms for manipulating EVMDDs and give upper bounds of the theoretical time complexity of these algorithms for all basic arithmetic and relational operators. We also demonstrate that the time complexity of the generic recursive algorithm for applying a binary operator on EVMDDs is no worse than that of Multi-Terminal Decision Diagrams. We have implemented a new symbolic model checker with the intention to represent in one formalism the best techniques available at the moment across a spectrum of existing tools: EVMDDs for encoding arithmetic expressions, identity-reduced MDDs for representing the transition relation, and the saturation algorithm for reachability analysis. We compare our new symbolic model checking EVMDD library with the widely used CUDD package and show that, in many cases, our tool is several orders of magnitude faster than CUDD.

Cloud computing-based TagSNP selection algorithm for human genome data.

PubMed

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.

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.

The Applications of Genetic Algorithms in Medicine.

PubMed

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.].

The Applications of Genetic Algorithms in Medicine

PubMed Central

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

Cloud Computing-Based TagSNP Selection Algorithm for Human Genome Data

PubMed Central

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

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.

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.

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.

HDL Based FPGA Interface Library for Data Acquisition and Multipurpose Real Time Algorithms

NASA Astrophysics Data System (ADS)

Fernandes, Ana M.; Pereira, R. C.; Sousa, J.; Batista, A. J. N.; Combo, A.; Carvalho, B. B.; Correia, C. M. B. A.; Varandas, C. A. F.

2011-08-01

The inherent parallelism of the logic resources, the flexibility in its configuration and the performance at high processing frequencies makes the field programmable gate array (FPGA) the most suitable device to be used both for real time algorithm processing and data transfer in instrumentation modules. Moreover, the reconfigurability of these FPGA based modules enables exploiting different applications on the same module. When using a reconfigurable module for various applications, the availability of a common interface library for easier implementation of the algorithms on the FPGA leads to more efficient development. The FPGA configuration is usually specified in a hardware description language (HDL) or other higher level descriptive language. The critical paths, such as the management of internal hardware clocks that require deep knowledge of the module behavior shall be implemented in HDL to optimize the timing constraints. The common interface library should include these critical paths, freeing the application designer from hardware complexity and able to choose any of the available high-level abstraction languages for the algorithm implementation. With this purpose a modular Verilog code was developed for the Virtex 4 FPGA of the in-house Transient Recorder and Processor (TRP) hardware module, based on the Advanced Telecommunications Computing Architecture (ATCA), with eight channels sampling at up to 400 MSamples/s (MSPS). The TRP was designed to perform real time Pulse Height Analysis (PHA), Pulse Shape Discrimination (PSD) and Pile-Up Rejection (PUR) algorithms at a high count rate (few Mevent/s). A brief description of this modular code is presented and examples of its use as an interface with end user algorithms, including a PHA with PUR, are described.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

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.

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.

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.

A High Fuel Consumption Efficiency Management Scheme for PHEVs Using an Adaptive Genetic Algorithm

PubMed Central

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

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.

Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods

NASA Astrophysics Data System (ADS)

Rogers, Adam; Safi-Harb, Samar; Fiege, Jason

2015-08-01

The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.

PTools: an opensource molecular docking library

PubMed Central

Saladin, Adrien; Fiorucci, Sébastien; Poulain, Pierre; Prévost, Chantal; Zacharias, Martin

2009-01-01

Background Macromolecular docking is a challenging field of bioinformatics. Developing new algorithms is a slow process generally involving routine tasks that should be found in a robust library and not programmed from scratch for every new software application. Results We present an object-oriented Python/C++ library to help the development of new docking methods. This library contains low-level routines like PDB-format manipulation functions as well as high-level tools for docking and analyzing results. We also illustrate the ease of use of this library with the detailed implementation of a 3-body docking procedure. Conclusion The PTools library can handle molecules at coarse-grained or atomic resolution and allows users to rapidly develop new software. The library is already in use for protein-protein and protein-DNA docking with the ATTRACT program and for simulation analysis. This library is freely available under the GNU GPL license, together with detailed documentation. PMID:19409097

PTools: an opensource molecular docking library.

PubMed

Saladin, Adrien; Fiorucci, Sébastien; Poulain, Pierre; Prévost, Chantal; Zacharias, Martin

2009-05-01

Macromolecular docking is a challenging field of bioinformatics. Developing new algorithms is a slow process generally involving routine tasks that should be found in a robust library and not programmed from scratch for every new software application. We present an object-oriented Python/C++ library to help the development of new docking methods. This library contains low-level routines like PDB-format manipulation functions as well as high-level tools for docking and analyzing results. We also illustrate the ease of use of this library with the detailed implementation of a 3-body docking procedure. The PTools library can handle molecules at coarse-grained or atomic resolution and allows users to rapidly develop new software. The library is already in use for protein-protein and protein-DNA docking with the ATTRACT program and for simulation analysis. This library is freely available under the GNU GPL license, together with detailed documentation.

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…

A CRISPR Cas9-based gene drive platform for genetic interaction analysis in Candida albicans

PubMed Central

Shapiro, Rebecca S.; Chavez, Alejandro; Porter, Caroline B. M.; Hamblin, Meagan; Kaas, Christian S.; DiCarlo, James E.; Zeng, Guisheng; Xu, Xiaoli; Revtovich, Alexey V.; Kirienko, Natalia V.; Wang, Yue; Church, George M.; Collins, James J.

2018-01-01

Candida albicans is the leading cause of fungal infections; yet, complex genetic interaction analysis remains cumbersome in this diploid pathogen. Here, we developed a CRISPR-Cas9-based ‘gene drive array’ (GDA) platform to facilitate efficient genetic analysis in C. albicans. In our system, a modified DNA donor molecule acts as a selfish genetic element, replaces the targeted site, and propagates to replace additional wild-type loci. Using mating-competent C. albicans haploids, each carrying a different gene drive disabling a gene of interest, we are able to create diploid strains that are homozygous double-deletion mutants. We generate double-gene deletion libraries to demonstrate this technology, targeting antifungal efflux and biofilm adhesion factors. We screen these libraries to identify virulence regulators and determine how genetic networks shift under diverse conditions. This platform transforms our ability to perform genetic interaction analysis in C. albicans and is readily extended to other fungal pathogens. PMID:29062088

Genome-scale CRISPR-Cas9 Knockout and Transcriptional Activation Screening

PubMed Central

Joung, Julia; Konermann, Silvana; Gootenberg, Jonathan S.; Abudayyeh, Omar O.; Platt, Randall J.; Brigham, Mark D.; Sanjana, Neville E.; Zhang, Feng

2017-01-01

Forward genetic screens are powerful tools for the unbiased discovery and functional characterization of specific genetic elements associated with a phenotype of interest. Recently, the RNA-guided endonuclease Cas9 from the microbial CRISPR (clustered regularly interspaced short palindromic repeats) immune system has been adapted for genome-scale screening by combining Cas9 with pooled guide RNA libraries. Here we describe a protocol for genome-scale knockout and transcriptional activation screening using the CRISPR-Cas9 system. Custom- or ready-made guide RNA libraries are constructed and packaged into lentiviral vectors for delivery into cells for screening. As each screen is unique, we provide guidelines for determining screening parameters and maintaining sufficient coverage. To validate candidate genes identified from the screen, we further describe strategies for confirming the screening phenotype as well as genetic perturbation through analysis of indel rate and transcriptional activation. Beginning with library design, a genome-scale screen can be completed in 9–15 weeks followed by 4–5 weeks of validation. PMID:28333914

Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening.

PubMed

Joung, Julia; Konermann, Silvana; Gootenberg, Jonathan S; Abudayyeh, Omar O; Platt, Randall J; Brigham, Mark D; Sanjana, Neville E; Zhang, Feng

2017-04-01

Forward genetic screens are powerful tools for the unbiased discovery and functional characterization of specific genetic elements associated with a phenotype of interest. Recently, the RNA-guided endonuclease Cas9 from the microbial CRISPR (clustered regularly interspaced short palindromic repeats) immune system has been adapted for genome-scale screening by combining Cas9 with pooled guide RNA libraries. Here we describe a protocol for genome-scale knockout and transcriptional activation screening using the CRISPR-Cas9 system. Custom- or ready-made guide RNA libraries are constructed and packaged into lentiviral vectors for delivery into cells for screening. As each screen is unique, we provide guidelines for determining screening parameters and maintaining sufficient coverage. To validate candidate genes identified by the screen, we further describe strategies for confirming the screening phenotype, as well as genetic perturbation, through analysis of indel rate and transcriptional activation. Beginning with library design, a genome-scale screen can be completed in 9-15 weeks, followed by 4-5 weeks of validation.

LibKiSAO: a Java library for Querying KiSAO.

PubMed

Zhukova, Anna; Adams, Richard; Laibe, Camille; Le Novère, Nicolas

2012-09-24

The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of Systems Biology models, their characteristics, parameters and inter-relationships. KiSAO enables the unambiguous identification of algorithms from simulation descriptions. Information about analogous methods having similar characteristics and about algorithm parameters incorporated into KiSAO is desirable for simulation tools. To retrieve this information programmatically an application programming interface (API) for KiSAO is needed. We developed libKiSAO, a Java library to enable querying of the KiSA Ontology. It implements methods to retrieve information about simulation algorithms stored in KiSAO, their characteristics and parameters, and methods to query the algorithm hierarchy and search for similar algorithms providing comparable results for the same simulation set-up. Using libKiSAO, simulation tools can make logical inferences based on this knowledge and choose the most appropriate algorithm to perform a simulation. LibKiSAO also enables simulation tools to handle a wider range of simulation descriptions by determining which of the available methods are similar and can be used instead of the one indicated in the simulation description if that one is not implemented. LibKiSAO enables Java applications to easily access information about simulation algorithms, their characteristics and parameters stored in the OWL-encoded Kinetic Simulation Algorithm Ontology. LibKiSAO can be used by simulation description editors and simulation tools to improve reproducibility of computational simulation tasks and facilitate model re-use.

MOESHA: A genetic algorithm for automatic calibration and estimation of parameter uncertainty and sensitivity of hydrologic models

EPA Science Inventory

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...

Range Image Flow using High-Order Polynomial Expansion

DTIC Science & Technology

2013-09-01

included as a default algorithm in the OpenCV library [2]. The research of estimating the motion between range images, or range flow, is much more...Journal of Computer Vision, vol. 92, no. 1, pp. 1‒31. 2. G. Bradski and A. Kaehler. 2008. Learning OpenCV : Computer Vision with the OpenCV Library

Development of an on-site screening system for amphetamine-type stimulant tablets with a portable attenuated total reflection Fourier transform infrared spectrometer.

PubMed

Tsujikawa, Kenji; Kuwayama, Kenji; Miyaguchi, Hajime; Kanamori, Tatsuyuki; Iwata, Yuko T; Yoshida, Takemi; Inoue, Hiroyuki

2008-02-04

We tried to develop a library search system using a portable, attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectrometer for on-site identification of 3,4-methylenedioxymethamphetamine (MDMA) and 3,4-methylenedioxyamphetamine (MDA) tablets. The library consisted of the spectra from mixtures of controlled drugs (e.g. MDMA and ketamine), adulterants (e.g. caffeine), and diluents (e.g. lactose). In the seven library search algorithms, the derivative correlation coefficient showed the best discriminant capability. This was enhanced by segmentation of the search area. The optimized search algorithm was validated by the positive (n=154, e.g. the standard mixtures containing the controlled drug, and the MDMA/MDA tablets confiscated) and negative samples (n=56, e.g. medicinal tablets). All validation samples except for four were judged truly. Final criteria for positive identification were decided on the basis of the results of the validation. In conclusion, a portable ATR-FT-IR spectrometer with our library search system would be a useful tool for on-site identification of amphetamine-type stimulant tablets.

Reduced Order Model Basis Vector Generation: Generates Basis Vectors fro ROMs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arrighi, Bill

2016-03-03

libROM is a library that implements order reduction via singular value decomposition (SVD) of sampled state vectors. It implements 2 parallel, incremental SVD algorithms and one serial, non-incremental algorithm. It also provides a mechanism for adaptive sampling of basis vectors.

Marathon: An Open Source Software Library for the Analysis of Markov-Chain Monte Carlo Algorithms

PubMed Central

Rechner, Steffen; Berger, Annabell

2016-01-01

We present the software library marathon, which is designed to support the analysis of sampling algorithms that are based on the Markov-Chain Monte Carlo principle. The main application of this library is the computation of properties of so-called state graphs, which represent the structure of Markov chains. We demonstrate applications and the usefulness of marathon by investigating the quality of several bounding methods on four well-known Markov chains for sampling perfect matchings and bipartite graphs. In a set of experiments, we compute the total mixing time and several of its bounds for a large number of input instances. We find that the upper bound gained by the famous canonical path method is often several magnitudes larger than the total mixing time and deteriorates with growing input size. In contrast, the spectral bound is found to be a precise approximation of the total mixing time. PMID:26824442

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.

The simulation method of chemical composition of vermicular graphite iron on the basis of genetic algorithm

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

MULTI-OBJECTIVE OPTIMAL DESIGN OF GROUNDWATER REMEDIATION SYSTEMS: APPLICATION OF THE NICHED PARETO GENETIC ALGORITHM (NPGA). (R826614)

EPA Science Inventory

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...

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

DTIC Science & Technology

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

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.

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.

Cognitive Foundry v. 3.0 (OSS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basilico, Justin; Dixon, Kevin; McClain, Jonathan

2009-11-18

The Cognitive Foundry is a unified collection of tools designed for research and applications that use cognitive modeling, machine learning, or pattern recognition. The software library contains design patterns, interface definitions, and default implementations of reusable software components and algorithms designed to support a wide variety of research and development needs. The library contains three main software packages: the Common package that contains basic utilities and linear algebraic methods, the Cognitive Framework package that contains tools to assist in implementing and analyzing theories of cognition, and the Machine Learning package that provides general algorithms and methods for populating Cognitive Frameworkmore » components from domain-relevant data.« less

Optimizing the Performance of Radionuclide Identification Software in the Hunt for Nuclear Security Threats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fotion, Katherine A.

2016-08-18

The Radionuclide Analysis Kit (RNAK), my team’s most recent nuclide identification software, is entering the testing phase. A question arises: will removing rare nuclides from the software’s library improve its overall performance? An affirmative response indicates fundamental errors in the software’s framework, while a negative response confirms the effectiveness of the software’s key machine learning algorithms. After thorough testing, I found that the performance of RNAK cannot be improved with the library choice effect, thus verifying the effectiveness of RNAK’s algorithms—multiple linear regression, Bayesian network using the Viterbi algorithm, and branch and bound search.

Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

PubMed

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.

[Application of genetic algorithm in blending technology for extractions of Cortex Fraxini].

PubMed

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.

Graphical Representation of Parallel Algorithmic Processes

DTIC Science & Technology

1990-12-01

interface with the AAARF main process . The source code for the AAARF class-common library is in the common subdi- rectory and consists of the following files... for public release; distribution unlimited AFIT/GCE/ENG/90D-07 Graphical Representation of Parallel Algorithmic Processes THESIS Presented to the...goal of this study is to develop an algorithm animation facility for parallel processes executing on different architectures, from multiprocessor

Improving Brain Magnetic Resonance Image (MRI) Segmentation via a Novel Algorithm based on Genetic and Regional Growth

PubMed Central

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

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information, non-gaps percentage and totally conserved columns.

PubMed

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.

Design of redundant array of independent DVD libraries based on iSCSI

NASA Astrophysics Data System (ADS)

Chen, Yupeng; Pan, Longfa

2003-04-01

This paper presents a new approach to realize the redundant array of independent DVD libraries (RAID-LoIP) by using the iSCSI technology and traditional RAID algorithms. Our design reaches the high performance of optical storage system with following features: large storage size, highly accessing rate, random access, long distance of DVD libraries, block I/O storage, long storage life. Our RAID-LoIP system can be a good solution for broadcasting media asset storage system.

Systems Design and Pilot Operation of a Regional Center for Technical Processing for the Libraries of the New England State Universities. NELINET, New England Library Information Network. Progress Report, July 1, 1967 - March 30, 1968, Volume II, Appendices.

ERIC Educational Resources Information Center

Agenbroad, James E.; And Others

Included in this volume of appendices to LI 000 979 are acquisitions flow charts; a current operations questionnaire; an algorithm for splitting the Library of Congress call number; analysis of the Machine-Readable Cataloging (MARC II) format; production problems and decisions; operating procedures for information transmittal in the New England…

Plans and progress for building a Great Lakes fauna DNA barcode reference library

EPA Science Inventory

DNA reference libraries provide researchers with an important tool for assessing regional biodiversity by allowing unknown genetic sequences to be assigned identities, while also providing a means for taxonomists to validate identifications. Expanding the representation of Great...

Hip Replacement: MedlinePlus Health Topic

MedlinePlus

... damage. The most common cause of damage is osteoarthritis . Osteoarthritis causes pain, swelling, and reduced motion in your ... Food and Drug Administration) Genetics Genetics Home Reference: osteoarthritis (National Library of Medicine) Images Hip joint replacement - ...

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

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.

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.

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.

Combined use of ESI-QqTOF-MS and ESI-QqTOF-MS/MS with mass-spectral library search for qualitative analysis of drugs.

PubMed

Pavlic, Marion; Libiseller, Kathrin; Oberacher, Herbert

2006-09-01

The potential of the combined use of ESI-QqTOF-MS and ESI-QqTOF-MS/MS with mass-spectral library search for the identification of therapeutic and illicit drugs has been evaluated. Reserpine was used for standardizing experimental conditions and for characterization of the performance of the applied mass spectrometric system. Experiments revealed that because of the mass accuracy, the stability of calibration, and the reproducibility of fragmentation, the QqTOF mass spectrometer is an appropriate platform for establishment of a tandem-mass-spectral library. Three-hundred and nineteen substances were used as reference samples to build the spectral library. For each reference compound, product-ion spectra were acquired at ten different collision-energy values between 5 eV and 50 eV. For identification of unknown compounds, a library search algorithm was developed. The closeness of matching between a measured product-ion spectrum and a spectrum stored in the library was characterized by a value called "match probability", which took into account the number of matched fragment ions, the number of fragment ions observed in the two spectra, and the sum of the intensity differences calculated for matching fragments. A large value for the match probability indicated a close match between the measured and the reference spectrum. A unique feature of the library search algorithm-an implemented spectral purification option-enables characterization of multi-contributor fragment-ion spectra. With the aid of this software feature, substances comprising only 1.0% of the total amount of binary mixtures were unequivocally assigned, in addition to the isobaric main contributors. The spectral library was successfully applied to the characterization of 39 forensic casework samples.

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.

IDEPI: rapid prediction of HIV-1 antibody epitopes and other phenotypic features from sequence data using a flexible machine learning platform.

PubMed

Hepler, N Lance; Scheffler, Konrad; Weaver, Steven; Murrell, Ben; Richman, Douglas D; Burton, Dennis R; Poignard, Pascal; Smith, Davey M; Kosakovsky Pond, Sergei L

2014-09-01

Since its identification in 1983, HIV-1 has been the focus of a research effort unprecedented in scope and difficulty, whose ultimate goals--a cure and a vaccine--remain elusive. One of the fundamental challenges in accomplishing these goals is the tremendous genetic variability of the virus, with some genes differing at as many as 40% of nucleotide positions among circulating strains. Because of this, the genetic bases of many viral phenotypes, most notably the susceptibility to neutralization by a particular antibody, are difficult to identify computationally. Drawing upon open-source general-purpose machine learning algorithms and libraries, we have developed a software package IDEPI (IDentify EPItopes) for learning genotype-to-phenotype predictive models from sequences with known phenotypes. IDEPI can apply learned models to classify sequences of unknown phenotypes, and also identify specific sequence features which contribute to a particular phenotype. We demonstrate that IDEPI achieves performance similar to or better than that of previously published approaches on four well-studied problems: finding the epitopes of broadly neutralizing antibodies (bNab), determining coreceptor tropism of the virus, identifying compartment-specific genetic signatures of the virus, and deducing drug-resistance associated mutations. The cross-platform Python source code (released under the GPL 3.0 license), documentation, issue tracking, and a pre-configured virtual machine for IDEPI can be found at https://github.com/veg/idepi.

ParFit: A Python-Based Object-Oriented Program for Fitting Molecular Mechanics Parameters to ab Initio Data.

PubMed

Zahariev, Federico; De Silva, Nuwan; Gordon, Mark S; Windus, Theresa L; Dick-Perez, Marilu

2017-03-27

A newly created object-oriented program for automating the process of fitting molecular-mechanics parameters to ab initio data, termed ParFit, is presented. ParFit uses a hybrid of deterministic and stochastic genetic algorithms. ParFit can simultaneously handle several molecular-mechanics parameters in multiple molecules and can also apply symmetric and antisymmetric constraints on the optimized parameters. The simultaneous handling of several molecules enhances the transferability of the fitted parameters. ParFit is written in Python, uses a rich set of standard and nonstandard Python libraries, and can be run in parallel on multicore computer systems. As an example, a series of phosphine oxides, important for metal extraction chemistry, are parametrized using ParFit. ParFit is in an open source program available for free on GitHub ( https://github.com/fzahari/ParFit ).

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.

CRISPR library designer (CLD): software for multispecies design of single guide RNA libraries.

PubMed

Heigwer, Florian; Zhan, Tianzuo; Breinig, Marco; Winter, Jan; Brügemann, Dirk; Leible, Svenja; Boutros, Michael

2016-03-24

Genetic screens using CRISPR/Cas9 are a powerful method for the functional analysis of genomes. Here we describe CRISPR library designer (CLD), an integrated bioinformatics application for the design of custom single guide RNA (sgRNA) libraries for all organisms with annotated genomes. CLD is suitable for the design of libraries using modified CRISPR enzymes and targeting non-coding regions. To demonstrate its utility, we perform a pooled screen for modulators of the TNF-related apoptosis inducing ligand (TRAIL) pathway using a custom library of 12,471 sgRNAs. CLD predicts a high fraction of functional sgRNAs and is publicly available at https://github.com/boutroslab/cld.

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.

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«.

Hybrid Architectures for Evolutionary Computing Algorithms

DTIC Science & Technology

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

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

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

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.

MATH77, Version 4.0

NASA Technical Reports Server (NTRS)

Lawson, Charles L.; Krogh, Fred; Van Snyder, W.; Oken, Carol A.; Mccreary, Faith A.; Lieske, Jay H.; Perrine, Jack; Coffin, Ralph S.; Wayne, Warren J.

1994-01-01

MATH77 is high-quality library of ANSI FORTRAN 77 subprograms implementing contemporary algorithms for basic computational processes of science and engineering. Release 4.0 of MATH77 contains 454 user-callable and 136 lower-level subprograms. MATH77 release 4.0 subroutine library designed to be usable on any computer system supporting full ANSI standard FORTRAN 77 language.

Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm.

PubMed

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.

Physical Analysis of the Complex Rye (Secale cereale L.) Alt4 Aluminium (Aluminum) Tolerance Locus Using a Whole-Genome BAC Library of Rye cv. Blanco

USDA-ARS?s Scientific Manuscript database

Rye is a diploid crop species with many outstanding qualities, and is also important as a source of new traits for wheat and triticale improvement. Here we describe a BAC library of rye cv. Blanco, representing a valuable resource for rye molecular genetic studies. The library provides a 6 × genome ...

Engineering Complex Microbial Phenotypes with Continuous Genetic Integration and Plasmid Based Multi-gene Library

DTIC Science & Technology

2013-10-09

have desirable traits. We aim to enlarge the E. coli genome using Lactobacillusplantarum genes to build cells tolerant to EtOH and BT. L. plantarum is...chemicals III. Approach Objective 1 & la: Integrated heterologous (L. plantarum ) DNA into the E. coli chromosome and selected for insertions that...developed in combination with genes identified from screening L. plantarum libraries. Additionally, we have screened heterologous libraries for

Toward functional genomics in bacteria: Analysis of gene expression in Escherichia coli from a bacterial artificial chromosome library of Bacillus cereus

PubMed Central

Rondon, Michelle R.; Raffel, Sandra J.; Goodman, Robert M.; Handelsman, Jo

1999-01-01

As the study of microbes moves into the era of functional genomics, there is an increasing need for molecular tools for analysis of a wide diversity of microorganisms. Currently, biological study of many prokaryotes of agricultural, medical, and fundamental scientific interest is limited by the lack of adequate genetic tools. We report the application of the bacterial artificial chromosome (BAC) vector to prokaryotic biology as a powerful approach to address this need. We constructed a BAC library in Escherichia coli from genomic DNA of the Gram-positive bacterium Bacillus cereus. This library provides 5.75-fold coverage of the B. cereus genome, with an average insert size of 98 kb. To determine the extent of heterologous expression of B. cereus genes in the library, we screened it for expression of several B. cereus activities in the E. coli host. Clones expressing 6 of 10 activities tested were identified in the library, namely, ampicillin resistance, zwittermicin A resistance, esculin hydrolysis, hemolysis, orange pigment production, and lecithinase activity. We analyzed selected BAC clones genetically to identify rapidly specific B. cereus loci. These results suggest that BAC libraries will provide a powerful approach for studying gene expression from diverse prokaryotes. PMID:10339608

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.

An application of traveling salesman problem using the improved genetic algorithm on android google maps

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.

Form Subdivisions: Their Identification and Use in LCSH.

ERIC Educational Resources Information Center

O'Neill, Edward T.; Chan, Lois Mai; Childress, Eric; Dean, Rebecca; El-Hoshy, Lynn M.; Vizine-Goetz, Diane

2001-01-01

Discusses form subdivisions as part of Library of Congress Subject Headings (LCSH) and the MARC format, which did not have a separate subfield code to identify form subdivisions. Describes the development of an algorithm to identify form subdivisions and reports results of an evaluation of the algorithm. (LRW)

A BIOINFORMATIC STRATEGY TO RAPIDLY CHARACTERIZE CDNA LIBRARIES

EPA Science Inventory

A Bioinformatic Strategy to Rapidly Characterize cDNA Libraries

G. Charles Ostermeier1, David J. Dix2 and Stephen A. Krawetz1.
1Departments of Obstetrics and Gynecology, Center for Molecular Medicine and Genetics, & Institute for Scientific Computing, Wayne State Univer...

blend4php: a PHP API for galaxy

PubMed Central

Wytko, Connor; Soto, Brian; Ficklin, Stephen P.

2017-01-01

Galaxy is a popular framework for execution of complex analytical pipelines typically for large data sets, and is a commonly used for (but not limited to) genomic, genetic and related biological analysis. It provides a web front-end and integrates with high performance computing resources. Here we report the development of the blend4php library that wraps Galaxy’s RESTful API into a PHP-based library. PHP-based web applications can use blend4php to automate execution, monitoring and management of a remote Galaxy server, including its users, workflows, jobs and more. The blend4php library was specifically developed for the integration of Galaxy with Tripal, the open-source toolkit for the creation of online genomic and genetic web sites. However, it was designed as an independent library for use by any application, and is freely available under version 3 of the GNU Lesser General Public License (LPGL v3.0) at https://github.com/galaxyproject/blend4php. Database URL: https://github.com/galaxyproject/blend4php PMID:28077564

De novo assembly and next-generation sequencing to analyse full-length gene variants from codon-barcoded libraries.

PubMed

Cho, Namjin; Hwang, Byungjin; Yoon, Jung-ki; Park, Sangun; Lee, Joongoo; Seo, Han Na; Lee, Jeewon; Huh, Sunghoon; Chung, Jinsoo; Bang, Duhee

2015-09-21

Interpreting epistatic interactions is crucial for understanding evolutionary dynamics of complex genetic systems and unveiling structure and function of genetic pathways. Although high resolution mapping of en masse variant libraries renders molecular biologists to address genotype-phenotype relationships, long-read sequencing technology remains indispensable to assess functional relationship between mutations that lie far apart. Here, we introduce JigsawSeq for multiplexed sequence identification of pooled gene variant libraries by combining a codon-based molecular barcoding strategy and de novo assembly of short-read data. We first validate JigsawSeq on small sub-pools and observed high precision and recall at various experimental settings. With extensive simulations, we then apply JigsawSeq to large-scale gene variant libraries to show that our method can be reliably scaled using next-generation sequencing. JigsawSeq may serve as a rapid screening tool for functional genomics and offer the opportunity to explore evolutionary trajectories of protein variants.

Internal combustion engine control for series hybrid electric vehicles by parallel and distributed genetic programming/multiobjective genetic algorithms

NASA Astrophysics Data System (ADS)

Gladwin, D.; Stewart, P.; Stewart, J.

2011-02-01

This article addresses the problem of maintaining a stable rectified DC output from the three-phase AC generator in a series-hybrid vehicle powertrain. The series-hybrid prime power source generally comprises an internal combustion (IC) engine driving a three-phase permanent magnet generator whose output is rectified to DC. A recent development has been to control the engine/generator combination by an electronically actuated throttle. This system can be represented as a nonlinear system with significant time delay. Previously, voltage control of the generator output has been achieved by model predictive methods such as the Smith Predictor. These methods rely on the incorporation of an accurate system model and time delay into the control algorithm, with a consequent increase in computational complexity in the real-time controller, and as a necessity relies to some extent on the accuracy of the models. Two complementary performance objectives exist for the control system. Firstly, to maintain the IC engine at its optimal operating point, and secondly, to supply a stable DC supply to the traction drive inverters. Achievement of these goals minimises the transient energy storage requirements at the DC link, with a consequent reduction in both weight and cost. These objectives imply constant velocity operation of the IC engine under external load disturbances and changes in both operating conditions and vehicle speed set-points. In order to achieve these objectives, and reduce the complexity of implementation, in this article a controller is designed by the use of Genetic Programming methods in the Simulink modelling environment, with the aim of obtaining a relatively simple controller for the time-delay system which does not rely on the implementation of real time system models or time delay approximations in the controller. A methodology is presented to utilise the miriad of existing control blocks in the Simulink libraries to automatically evolve optimal control structures.

Peak-to-average power ratio reduction in orthogonal frequency division multiplexing-based visible light communication systems using a modified partial transmit sequence technique

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.

The McGill Interactive Pediatric OncoGenetic Guidelines: An approach to identifying pediatric oncology patients most likely to benefit from a genetic evaluation.

PubMed

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.

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 ...

GPU Accelerated Chemical Similarity Calculation for Compound Library Comparison

PubMed Central

Ma, Chao; Wang, Lirong; Xie, Xiang-Qun

2012-01-01

Chemical similarity calculation plays an important role in compound library design, virtual screening, and “lead” optimization. In this manuscript, we present a novel GPU-accelerated algorithm for all-vs-all Tanimoto matrix calculation and nearest neighbor search. By taking advantage of multi-core GPU architecture and CUDA parallel programming technology, the algorithm is up to 39 times superior to the existing commercial software that runs on CPUs. Because of the utilization of intrinsic GPU instructions, this approach is nearly 10 times faster than existing GPU-accelerated sparse vector algorithm, when Unity fingerprints are used for Tanimoto calculation. The GPU program that implements this new method takes about 20 minutes to complete the calculation of Tanimoto coefficients between 32M PubChem compounds and 10K Active Probes compounds, i.e., 324G Tanimoto coefficients, on a 128-CUDA-core GPU. PMID:21692447

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.

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.

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...

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

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.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

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.

[Reconstruction of Vehicle-human Crash Accident and Injury Analysis Based on 3D Laser Scanning, Multi-rigid-body Reconstruction and Optimized Genetic Algorithm].

PubMed

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

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

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.

Wrapper-based selection of genetic features in genome-wide association studies through fast matrix operations

PubMed Central

2012-01-01

Background Through the wealth of information contained within them, genome-wide association studies (GWAS) have the potential to provide researchers with a systematic means of associating genetic variants with a wide variety of disease phenotypes. Due to the limitations of approaches that have analyzed single variants one at a time, it has been proposed that the genetic basis of these disorders could be determined through detailed analysis of the genetic variants themselves and in conjunction with one another. The construction of models that account for these subsets of variants requires methodologies that generate predictions based on the total risk of a particular group of polymorphisms. However, due to the excessive number of variants, constructing these types of models has so far been computationally infeasible. Results We have implemented an algorithm, known as greedy RLS, that we use to perform the first known wrapper-based feature selection on the genome-wide level. The running time of greedy RLS grows linearly in the number of training examples, the number of features in the original data set, and the number of selected features. This speed is achieved through computational short-cuts based on matrix calculus. Since the memory consumption in present-day computers can form an even tighter bottleneck than running time, we also developed a space efficient variation of greedy RLS which trades running time for memory. These approaches are then compared to traditional wrapper-based feature selection implementations based on support vector machines (SVM) to reveal the relative speed-up and to assess the feasibility of the new algorithm. As a proof of concept, we apply greedy RLS to the Hypertension – UK National Blood Service WTCCC dataset and select the most predictive variants using 3-fold external cross-validation in less than 26 minutes on a high-end desktop. On this dataset, we also show that greedy RLS has a better classification performance on independent test data than a classifier trained using features selected by a statistical p-value-based filter, which is currently the most popular approach for constructing predictive models in GWAS. Conclusions Greedy RLS is the first known implementation of a machine learning based method with the capability to conduct a wrapper-based feature selection on an entire GWAS containing several thousand examples and over 400,000 variants. In our experiments, greedy RLS selected a highly predictive subset of genetic variants in a fraction of the time spent by wrapper-based selection methods used together with SVM classifiers. The proposed algorithms are freely available as part of the RLScore software library at http://users.utu.fi/aatapa/RLScore/. PMID:22551170

Research on laser marking speed optimization by using genetic algorithm.

PubMed

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%.

Tag SNP selection via a genetic algorithm.

PubMed

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.

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.

Genomics & Genetics | National Agricultural Library

Science.gov Websites

Skip to main content Home National Agricultural Library United States Department of Agriculture Ag agricultural and environmental settings. Deadpool proximal sensing cart docx xlsx 3x jpeg 5x pdf Data from Buytaert. NAL Home | USDA.gov | Agricultural Research Service | Plain Language | FOIA | Accessibility

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

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…

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

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…

A Parallel Genetic Algorithm to Discover Patterns in Genetic Markers that Indicate Predisposition to Multifactorial Disease

PubMed Central

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

A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization.

PubMed

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.

Gaze3DFix: Detecting 3D fixations with an ellipsoidal bounding volume.

PubMed

Weber, Sascha; Schubert, Rebekka S; Vogt, Stefan; Velichkovsky, Boris M; Pannasch, Sebastian

2017-10-26

Nowadays, the use of eyetracking to determine 2-D gaze positions is common practice, and several approaches to the detection of 2-D fixations exist, but ready-to-use algorithms to determine eye movements in three dimensions are still missing. Here we present a dispersion-based algorithm with an ellipsoidal bounding volume that estimates 3D fixations. Therefore, 3D gaze points are obtained using a vector-based approach and are further processed with our algorithm. To evaluate the accuracy of our method, we performed experimental studies with real and virtual stimuli. We obtained good congruence between stimulus position and both the 3D gaze points and the 3D fixation locations within the tested range of 200-600 mm. The mean deviation of the 3D fixations from the stimulus positions was 17 mm for the real as well as for the virtual stimuli, with larger variances at increasing stimulus distances. The described algorithms are implemented in two dynamic linked libraries (Gaze3D.dll and Fixation3D.dll), and we provide a graphical user interface (Gaze3DFixGUI.exe) that is designed for importing 2-D binocular eyetracking data and calculating both 3D gaze points and 3D fixations using the libraries. The Gaze3DFix toolkit, including both libraries and the graphical user interface, is available as open-source software at https://github.com/applied-cognition-research/Gaze3DFix .

Performance impact of mutation operators of a subpopulation-based genetic algorithm for multi-robot task allocation problems.

PubMed

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.

A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform

PubMed Central

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

A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

PubMed

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.

ChemTS: an efficient python library for de novo molecular generation.

PubMed

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

ChemTS: an efficient python library for de novo molecular generation

NASA Astrophysics Data System (ADS)

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-12-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

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.

High-throughput screens in mammalian cells using the CRISPR-Cas9 system.

PubMed

Peng, Jingyu; Zhou, Yuexin; Zhu, Shiyou; Wei, Wensheng

2015-06-01

As a powerful genome-editing tool, the clustered regularly interspaced short palindromic repeats (CRISPR)-clustered regularly interspaced short palindromic repeats-associated protein 9 (Cas9) system has been quickly developed into a large-scale function-based screening strategy in mammalian cells. This new type of genetic library is constructed through the lentiviral delivery of single-guide RNA collections that direct Cas9 or inactive dead Cas9 fused with effectors to interrogate gene function or regulate gene transcription in targeted cells. Compared with RNA interference screening, the CRISPR-Cas9 system demonstrates much higher levels of effectiveness and reliability with respect to both loss-of-function and gain-of-function screening. Unlike the RNA interference strategy, a CRISPR-Cas9 library can target both protein-coding sequences and regulatory elements, including promoters, enhancers and elements transcribing microRNAs and long noncoding RNAs. This powerful genetic tool will undoubtedly accelerate the mechanistic discovery of various biological processes. In this mini review, we summarize the general procedure of CRISPR-Cas9 library mediated functional screening, system optimization strategies and applications of this new genetic toolkit. © 2015 FEBS.

Scientific and Technical Serials Holdings Optimization in an Inefficient Market: A LSU Serials Redesign Project Exercise.

ERIC Educational Resources Information Center

Bensman, Stephen J.; Wilder, Stanley J.

1998-01-01

Analyzes the structure of the library market for scientific and technical (ST) serials. Describes an exercise aimed at a theoretical reconstruction of the ST-serials holdings of Louisiana State University (LSU) Libraries. Discusses the set definitions, measures, and algorithms necessary in the design of a computer program to appraise ST serials.…

Scheduling language and algorithm development study. Volume 1: Study summary and overview

NASA Technical Reports Server (NTRS)

1974-01-01

A high level computer programming language and a program library were developed to be used in writing programs for scheduling complex systems such as the space transportation system. The objectives and requirements of the study are summarized and unique features of the specified language and program library are described and related to the why of the objectives and requirements.

A High-Quality Reference Genome for the Invasive Mosquitofish Gambusia affinis Using a Chicago Library.

PubMed

Hoffberg, Sandra L; Troendle, Nicholas J; Glenn, Travis C; Mahmud, Ousman; Louha, Swarnali; Chalopin, Domitille; Bennetzen, Jeffrey L; Mauricio, Rodney

2018-04-27

The western mosquitofish, Gambusia affinis, is a freshwater poecilid fish native to the southeastern United States but with a global distribution due to widespread human introduction. Gambusia affinis has been used as a model species for a broad range of evolutionary and ecological studies. We sequenced the genome of a male G. affinis to facilitate genetic studies in diverse fields including invasion biology and comparative genetics. We generated Illumina short read data from paired-end libraries and in vitro proximity-ligation libraries. We obtained 54.9× coverage, N50 contig length of 17.6 kb, and N50 scaffold length of 6.65 Mb. Compared to two other species in the Poeciliidae family, G. affinis has slightly fewer genes that have shorter total, exon, and intron length on average. Using a set of universal single-copy orthologs in fish genomes, we found 95.5% of these genes were complete in the G. affinis assembly. The number of transposable elements in the G. affinis assembly is similar to those of closely related species. The high-quality genome sequence and annotations we report will be valuable resources for scientists to map the genetic architecture of traits of interest in this species. Copyright © 2018, G3: Genes, Genomes, Genetics.

Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

PubMed Central

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

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.

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.

Application of a Genetic Algorithm and Multi Agent System to Explore Emergent Patterns of Social Rationality and a Distress-Based Model for Deceit in the Workplace

DTIC Science & Technology

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

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.

Effects of field-grown genetically modified Zoysia grass on bacterial community structure.

PubMed

Lee, Yong-Eok; Yang, Sang-Hwan; Bae, Tae-Woong; Kang, Hong-Gyu; Lim, Pyung-Ok; Lee, Hyo-Yeon

2011-04-01

Herbicide-tolerant Zoysia grass has been previously developed through Agrobacterium-mediated transformation. We investigated the effects of genetically modified (GM) Zoysia grass and the associated herbicide application on bacterial community structure by using culture-independent approaches. To assess the possible horizontal gene transfer (HGT) of transgenic DNA to soil microorganisms, total soil DNAs were amplified by PCR with two primer sets for the bar and hpt genes, which were introduced into the GM Zoysia grass by a callus-type transformation. The transgenic genes were not detected from the total genomic DNAs extracted from 1.5 g of each rhizosphere soils of GM and non-GM Zoysia grasses. The structures and diversities of the bacterial communities in rhizosphere soils of GM and non-GM Zoysia grasses were investigated by constructing 16S rDNA clone libraries. Classifier, provided in the RDP II, assigned 100 clones in the 16S rRNA gene sequences library into 11 bacterial phyla. The most abundant phyla in both clone libraries were Acidobacteria and Proteobacteria. The bacterial diversity of the GM clone library was lower than that of the non- GM library. The former contained four phyla, whereas the latter had seven phyla. Phylogenetic trees were constructed to confirm these results. Phylogenetic analyses of the two clone libraries revealed considerable difference from each other. The significance of difference between clone libraries was examined with LIBSHUFF statistics. LIBSHUFF analysis revealed that the two clone libraries differed significantly (P〈0.025), suggesting alterations in the composition of the microbial community associated with GM Zoysia grass.

Investigation on application of genetic algorithms to optimal reactive power dispatch of power systems

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.

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.

Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

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.

Research on Laser Marking Speed Optimization by Using Genetic Algorithm

PubMed Central

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

Approximate string matching algorithms for limited-vocabulary OCR output correction

NASA Astrophysics Data System (ADS)

Lasko, Thomas A.; Hauser, Susan E.

2000-12-01

Five methods for matching words mistranslated by optical character recognition to their most likely match in a reference dictionary were tested on data from the archives of the National Library of Medicine. The methods, including an adaptation of the cross correlation algorithm, the generic edit distance algorithm, the edit distance algorithm with a probabilistic substitution matrix, Bayesian analysis, and Bayesian analysis on an actively thinned reference dictionary were implemented and their accuracy rates compared. Of the five, the Bayesian algorithm produced the most correct matches (87%), and had the advantage of producing scores that have a useful and practical interpretation.

Parana Basin Structure from Multi-Objective Inversion of Surface Wave and Receiver Function by Competent Genetic Algorithm

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.

AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data

NASA Astrophysics Data System (ADS)

Laura, Jason; Rodriguez, Kelvin; Paquette, Adam C.; Dunn, Evin

2018-01-01

In this work we describe the AutoCNet library, written in Python, to support the application of computer vision techniques for n-image correspondence identification in remotely sensed planetary images and subsequent bundle adjustment. The library is designed to support exploratory data analysis, algorithm and processing pipeline development, and application at scale in High Performance Computing (HPC) environments for processing large data sets and generating foundational data products. We also present a brief case study illustrating high level usage for the Apollo 15 Metric camera.

Pairwise Sequence Alignment Library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jeff Daily, PNNL

2015-05-20

Vector extensions, such as SSE, have been part of the x86 CPU since the 1990s, with applications in graphics, signal processing, and scientific applications. Although many algorithms and applications can naturally benefit from automatic vectorization techniques, there are still many that are difficult to vectorize due to their dependence on irregular data structures, dense branch operations, or data dependencies. Sequence alignment, one of the most widely used operations in bioinformatics workflows, has a computational footprint that features complex data dependencies. The trend of widening vector registers adversely affects the state-of-the-art sequence alignment algorithm based on striped data layouts. Therefore, amore » novel SIMD implementation of a parallel scan-based sequence alignment algorithm that can better exploit wider SIMD units was implemented as part of the Parallel Sequence Alignment Library (parasail). Parasail features: Reference implementations of all known vectorized sequence alignment approaches. Implementations of Smith Waterman (SW), semi-global (SG), and Needleman Wunsch (NW) sequence alignment algorithms. Implementations across all modern CPU instruction sets including AVX2 and KNC. Language interfaces for C/C++ and Python.« less

Probabilistic representation of gene regulatory networks.

PubMed

Mao, Linyong; Resat, Haluk

2004-09-22

Recent experiments have established unambiguously that biological systems can have significant cell-to-cell variations in gene expression levels even in isogenic populations. Computational approaches to studying gene expression in cellular systems should capture such biological variations for a more realistic representation. In this paper, we present a new fully probabilistic approach to the modeling of gene regulatory networks that allows for fluctuations in the gene expression levels. The new algorithm uses a very simple representation for the genes, and accounts for the repression or induction of the genes and for the biological variations among isogenic populations simultaneously. Because of its simplicity, introduced algorithm is a very promising approach to model large-scale gene regulatory networks. We have tested the new algorithm on the synthetic gene network library bioengineered recently. The good agreement between the computed and the experimental results for this library of networks, and additional tests, demonstrate that the new algorithm is robust and very successful in explaining the experimental data. The simulation software is available upon request. Supplementary material will be made available on the OUP server.

SnapDock—template-based docking by Geometric Hashing

PubMed Central

Estrin, Michael; Wolfson, Haim J.

2017-01-01

Abstract Motivation: A highly efficient template-based protein–protein docking algorithm, nicknamed SnapDock, is presented. It employs a Geometric Hashing-based structural alignment scheme to align the target proteins to the interfaces of non-redundant protein–protein interface libraries. Docking of a pair of proteins utilizing the 22 600 interface PIFACE library is performed in < 2 min on the average. A flexible version of the algorithm allowing hinge motion in one of the proteins is presented as well. Results: To evaluate the performance of the algorithm a blind re-modelling of 3547 PDB complexes, which have been uploaded after the PIFACE publication has been performed with success ratio of about 35%. Interestingly, a similar experiment with the template free PatchDock docking algorithm yielded a success rate of about 23% with roughly 1/3 of the solutions different from those of SnapDock. Consequently, the combination of the two methods gave a 42% success ratio. Availability and implementation: A web server of the application is under development. Contact: michaelestrin@gmail.com or wolfson@tau.ac.il PMID:28881968

Blocked inverted indices for exact clustering of large chemical spaces.

PubMed

Thiel, Philipp; Sach-Peltason, Lisa; Ottmann, Christian; Kohlbacher, Oliver

2014-09-22

The calculation of pairwise compound similarities based on fingerprints is one of the fundamental tasks in chemoinformatics. Methods for efficient calculation of compound similarities are of the utmost importance for various applications like similarity searching or library clustering. With the increasing size of public compound databases, exact clustering of these databases is desirable, but often computationally prohibitively expensive. We present an optimized inverted index algorithm for the calculation of all pairwise similarities on 2D fingerprints of a given data set. In contrast to other algorithms, it neither requires GPU computing nor yields a stochastic approximation of the clustering. The algorithm has been designed to work well with multicore architectures and shows excellent parallel speedup. As an application example of this algorithm, we implemented a deterministic clustering application, which has been designed to decompose virtual libraries comprising tens of millions of compounds in a short time on current hardware. Our results show that our implementation achieves more than 400 million Tanimoto similarity calculations per second on a common desktop CPU. Deterministic clustering of the available chemical space thus can be done on modern multicore machines within a few days.

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

PubMed Central

2012-01-01

Background The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. Results The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Conclusions Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net. PMID:22889332

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform.

PubMed

Hanwell, Marcus D; Curtis, Donald E; Lonie, David C; Vandermeersch, Tim; Zurek, Eva; Hutchison, Geoffrey R

2012-08-13

The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

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.

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.

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

On the inter-instrument and the inter-laboratory transferability of a tandem mass spectral reference library: 2. Optimization and characterization of the search algorithm.

PubMed

Oberacher, Herbert; Pavlic, Marion; Libiseller, Kathrin; Schubert, Birthe; Sulyok, Michael; Schuhmacher, Rainer; Csaszar, Edina; Köfeler, Harald C

2009-04-01

A sophisticated matching algorithm developed for highly efficient identity search within tandem mass spectral libraries is presented. For the optimization of the search procedure a collection of 410 tandem mass spectra corresponding to 22 compounds was used. The spectra were acquired in three different laboratories on four different instruments. The following types of tandem mass spectrometric instruments were used: quadrupole-quadrupole-time-of-flight (QqTOF), quadrupole-quadrupole-linear ion trap (QqLIT), quadrupole-quadrupole-quadrupole (QqQ), and linear ion trap-Fourier transform ion cyclotron resonance mass spectrometer (LIT-FTICR). The obtained spectra were matched to an established MS/MS-spectral library that contained 3759 MS/MS-spectra corresponding to 402 different reference compounds. All 22 test compounds were part of the library. A dynamic intensity cut-off, the search for neutral losses, and optimization of the formula used to calculate the match probability were shown to significantly enhance the performance of the presented library search approach. With the aid of these features the average number of correct assignments was increased to 98%. For statistical evaluation of the match reliability the set of fragment ion spectra was extended with 300 spectra corresponding to 100 compounds not included in the reference library. Performance was checked with the aid of receiver operating characteristic (ROC) curves. Using the magnitude of the match probability as well as the precursor ion mass as benchmarks to rate the obtained top hit, overall correct classification of a compound being included or not included in the mass spectrometric library, was obtained in more than 95% of cases clearly indicating a high predictive accuracy of the established matching procedure. Copyright (c) 2009 John Wiley & Sons, Ltd.

Design of Genetic Algorithms for Topology Control of Unmanned Vehicles

DTIC Science & Technology

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

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.

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

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

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

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.

First DNA Barcode Reference Library for the Identification of South American Freshwater Fish from the Lower Paraná River

PubMed Central

Brancolini, Florencia; del Pazo, Felipe; Posner, Victoria Maria; Grimberg, Alexis; Arranz, Silvia Eda

2016-01-01

Valid fish species identification is essential for biodiversity conservation and fisheries management. Here, we provide a sequence reference library based on mitochondrial cytochrome c oxidase subunit I for a valid identification of 79 freshwater fish species from the Lower Paraná River. Neighbour-joining analysis based on K2P genetic distances formed non-overlapping clusters for almost all species with a ≥99% bootstrap support each. Identification was successful for 97.8% of species as the minimum genetic distance to the nearest neighbour exceeded the maximum intraspecific distance in all these cases. A barcoding gap of 2.5% was apparent for the whole data set with the exception of four cases. Within-species distances ranged from 0.00% to 7.59%, while interspecific distances varied between 4.06% and 19.98%, without considering Odontesthes species with a minimum genetic distance of 0%. Sequence library validation was performed by applying BOLDs BIN analysis tool, Poisson Tree Processes model and Automatic Barcode Gap Discovery, along with a reliable taxonomic assignment by experts. Exhaustive revision of vouchers was performed when a conflicting assignment was detected after sequence analysis and BIN discordance evaluation. Thus, the sequence library presented here can be confidently used as a benchmark for identification of half of the fish species recorded for the Lower Paraná River. PMID:27442116

Light-extraction enhancement for light-emitting diodes: a firefly-inspired structure refined by the genetic algorithm

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.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

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

Scenario Decomposition for 0-1 Stochastic Programs: Improvements and Asynchronous Implementation

DOE PAGES

Ryan, Kevin; Rajan, Deepak; Ahmed, Shabbir

2016-05-01

We recently proposed scenario decomposition algorithm for stochastic 0-1 programs finds an optimal solution by evaluating and removing individual solutions that are discovered by solving scenario subproblems. In our work, we develop an asynchronous, distributed implementation of the algorithm which has computational advantages over existing synchronous implementations of the algorithm. Improvements to both the synchronous and asynchronous algorithm are proposed. We also test the results on well known stochastic 0-1 programs from the SIPLIB test library and is able to solve one previously unsolved instance from the test set.

Automated design of degenerate codon libraries.

PubMed

Mena, Marco A; Daugherty, Patrick S

2005-12-01

Degenerate codon libraries are frequently used in protein engineering and evolution studies but are often limited to targeting a small number of positions to adequately limit the search space. To mitigate this, codon degeneracy can be limited using heuristics or previous knowledge of the targeted positions. To automate design of libraries given a set of amino acid sequences, an algorithm (LibDesign) was developed that generates a set of possible degenerate codon libraries, their resulting size, and their score relative to a user-defined scoring function. A gene library of a specified size can then be constructed that is representative of the given amino acid distribution or that includes specific sequences or combinations thereof. LibDesign provides a new tool for automated design of high-quality protein libraries that more effectively harness existing sequence-structure information derived from multiple sequence alignment or computational protein design data.

Creation of BAC genomic resources for cocoa ( Theobroma cacao L.) for physical mapping of RGA containing BAC clones.

PubMed

Clément, D; Lanaud, C; Sabau, X; Fouet, O; Le Cunff, L; Ruiz, E; Risterucci, A M; Glaszmann, J C; Piffanelli, P

2004-05-01

We have constructed and validated the first cocoa ( Theobroma cacao L.) BAC library, with the aim of developing molecular resources to study the structure and evolution of the genome of this perennial crop. This library contains 36,864 clones with an average insert size of 120 kb, representing approximately ten haploid genome equivalents. It was constructed from the genotype Scavina-6 (Sca-6), a Forastero clone highly resistant to cocoa pathogens and a parent of existing mapping populations. Validation of the BAC library was carried out with a set of 13 genetically-anchored single copy and one duplicated markers. An average of nine BAC clones per probe was identified, giving an initial experimental estimation of the genome coverage represented in the library. Screening of the library with a set of resistance gene analogues (RGAs), previously mapped in cocoa and co-localizing with QTL for resistance to Phytophthora traits, confirmed at the physical level the tight clustering of RGAs in the cocoa genome and provided the first insights into the relationships between genetic and physical distances in the cocoa genome. This library represents an available BAC resource for structural genomic studies or map-based cloning of genes corresponding to important QTLs for agronomic traits such as resistance genes to major cocoa pathogens like Phytophthora spp ( palmivora and megakarya), Crinipellis perniciosa and Moniliophthora roreri.

MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

EPA Science Inventory

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...

Optimized Next-Generation Sequencing Genotype-Haplotype Calling for Genome Variability Analysis

PubMed Central

Navarro, Javier; Nevado, Bruno; Hernández, Porfidio; Vera, Gonzalo; Ramos-Onsins, Sebastián E

2017-01-01

The accurate estimation of nucleotide variability using next-generation sequencing data is challenged by the high number of sequencing errors produced by new sequencing technologies, especially for nonmodel species, where reference sequences may not be available and the read depth may be low due to limited budgets. The most popular single-nucleotide polymorphism (SNP) callers are designed to obtain a high SNP recovery and low false discovery rate but are not designed to account appropriately the frequency of the variants. Instead, algorithms designed to account for the frequency of SNPs give precise results for estimating the levels and the patterns of variability. These algorithms are focused on the unbiased estimation of the variability and not on the high recovery of SNPs. Here, we implemented a fast and optimized parallel algorithm that includes the method developed by Roesti et al and Lynch, which estimates the genotype of each individual at each site, considering the possibility to call both bases from the genotype, a single one or none. This algorithm does not consider the reference and therefore is independent of biases related to the reference nucleotide specified. The pipeline starts from a BAM file converted to pileup or mpileup format and the software outputs a FASTA file. The new program not only reduces the running times but also, given the improved use of resources, it allows its usage with smaller computers and large parallel computers, expanding its benefits to a wider range of researchers. The output file can be analyzed using software for population genetics analysis, such as the R library PopGenome, the software VariScan, and the program mstatspop for analysis considering positions with missing data. PMID:28894353

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.

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.

GHOST: global hepatitis outbreak and surveillance technology.

PubMed

Longmire, Atkinson G; Sims, Seth; Rytsareva, Inna; Campo, David S; Skums, Pavel; Dimitrova, Zoya; Ramachandran, Sumathi; Medrzycki, Magdalena; Thai, Hong; Ganova-Raeva, Lilia; Lin, Yulin; Punkova, Lili T; Sue, Amanda; Mirabito, Massimo; Wang, Silver; Tracy, Robin; Bolet, Victor; Sukalac, Thom; Lynberg, Chris; Khudyakov, Yury

2017-12-06

Hepatitis C is a major public health problem in the United States and worldwide. Outbreaks of hepatitis C virus (HCV) infections associated with unsafe injection practices, drug diversion, and other exposures to blood are difficult to detect and investigate. Effective HCV outbreak investigation requires comprehensive surveillance and robust case investigation. We previously developed and validated a methodology for the rapid and cost-effective identification of HCV transmission clusters. Global Hepatitis Outbreak and Surveillance Technology (GHOST) is a cloud-based system enabling users, regardless of computational expertise, to analyze and visualize transmission clusters in an independent, accurate and reproducible way. We present and explore performance of several GHOST implemented algorithms using next-generation sequencing data experimentally obtained from hypervariable region 1 of genetically related and unrelated HCV strains. GHOST processes data from an entire MiSeq run in approximately 3 h. A panel of seven specimens was used for preparation of six repeats of MiSeq libraries. Testing sequence data from these libraries by GHOST showed a consistent transmission linkage detection, testifying to high reproducibility of the system. Lack of linkage among genetically unrelated HCV strains and constant detection of genetic linkage between HCV strains from known transmission pairs and from follow-up specimens at different levels of MiSeq-read sampling indicate high specificity and sensitivity of GHOST in accurate detection of HCV transmission. GHOST enables automatic extraction of timely and relevant public health information suitable for guiding effective intervention measures. It is designed as a virtual diagnostic system intended for use in molecular surveillance and outbreak investigations rather than in research. The system produces accurate and reproducible information on HCV transmission clusters for all users, irrespective of their level of bioinformatics expertise. Improvement in molecular detection capacity will contribute to increasing the rate of transmission detection, thus providing opportunity for rapid, accurate and effective response to outbreaks of hepatitis C. Although GHOST was originally developed for hepatitis C surveillance, its modular structure is readily applicable to other infectious diseases. Worldwide availability of GHOST for the detection of HCV transmissions will foster deeper involvement of public health researchers and practitioners in hepatitis C outbreak investigation.

Scheduling language and algorithm development study. Volume 2: Use of the basic language and module library

NASA Technical Reports Server (NTRS)

Chamberlain, R. A.; Cornick, D. E.; Flater, J. F.; Odoherty, R. J.; Peterson, F. M.; Ramsey, H. R.; Willoughby, J. K.

1974-01-01

The capabilities of the specified scheduling language and the program module library are outlined. The summary is written with the potential user in mind and, therefore, provides maximum insight on how the capabilities will be helpful in writing scheduling programs. Simple examples and illustrations are provided to assist the potential user in applying the capabilities of his problem.

Parallel Element Agglomeration Algebraic Multigrid and Upscaling Library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barker, Andrew T.; Benson, Thomas R.; Lee, Chak Shing

ParELAG is a parallel C++ library for numerical upscaling of finite element discretizations and element-based algebraic multigrid solvers. It provides optimal complexity algorithms to build multilevel hierarchies and solvers that can be used for solving a wide class of partial differential equations (elliptic, hyperbolic, saddle point problems) on general unstructured meshes. Additionally, a novel multilevel solver for saddle point problems with divergence constraint is implemented.

Coupling between a multi-physics workflow engine and an optimization framework

NASA Astrophysics Data System (ADS)

Di Gallo, L.; Reux, C.; Imbeaux, F.; Artaud, J.-F.; Owsiak, M.; Saoutic, B.; Aiello, G.; Bernardi, P.; Ciraolo, G.; Bucalossi, J.; Duchateau, J.-L.; Fausser, C.; Galassi, D.; Hertout, P.; Jaboulay, J.-C.; Li-Puma, A.; Zani, L.

2016-03-01

A generic coupling method between a multi-physics workflow engine and an optimization framework is presented in this paper. The coupling architecture has been developed in order to preserve the integrity of the two frameworks. The objective is to provide the possibility to replace a framework, a workflow or an optimizer by another one without changing the whole coupling procedure or modifying the main content in each framework. The coupling is achieved by using a socket-based communication library for exchanging data between the two frameworks. Among a number of algorithms provided by optimization frameworks, Genetic Algorithms (GAs) have demonstrated their efficiency on single and multiple criteria optimization. Additionally to their robustness, GAs can handle non-valid data which may appear during the optimization. Consequently GAs work on most general cases. A parallelized framework has been developed to reduce the time spent for optimizations and evaluation of large samples. A test has shown a good scaling efficiency of this parallelized framework. This coupling method has been applied to the case of SYCOMORE (SYstem COde for MOdeling tokamak REactor) which is a system code developed in form of a modular workflow for designing magnetic fusion reactors. The coupling of SYCOMORE with the optimization platform URANIE enables design optimization along various figures of merit and constraints.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

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.

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

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

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.

USING GENETIC ALGORITHMS TO DESIGN ENVIRONMENTALLY FRIENDLY PROCESSES

EPA Science Inventory

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 ...

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.

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.

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.

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.

Simultaneous optimization of the cavity heat load and trip rates in linacs using a genetic algorithm

DOE PAGES

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.

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.

A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

PubMed Central

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

Evaluation of anonymous and expressed sequence tag derived polymorphic microsatellite markers in the tobacco budworm Heliothis virescens (Lepidoptera: noctuidae)

USDA-ARS?s Scientific Manuscript database

Polymorphic genetic markers were identified and characterized using a partial genomic library of Heliothis virescens enriched for simple sequence repeats (SSR) and nucleotide sequences of expressed sequence tags (EST). Nucleotide sequences of 192 clones from the partial genomic library yielded 147 u...

Development of polymorphic genic-SSR markers by cDNA library sequencing in boxwood, Buxus spp. (Buxaceae)

USDA-ARS?s Scientific Manuscript database

Genic microsatellites or simple sequence repeat (genic-SSR) markers were developed in boxwood (Buxus taxa) for genetic diversity analysis, identification of taxa, and to facilitate breeding. cDNA libraries were developed from mRNA extracted from leaves of Buxus sempervirens ‘Vardar Valley’ and seque...

School Librarians: Vital Educational Leaders

ERIC Educational Resources Information Center

Martineau, Pamela

2010-01-01

In the new millennium, school librarians are more likely to be found sitting behind a computer as they update the library web page or create a wiki on genetically modified organisms. Or they might be seen in the library computer lab as they lead students through tutorials on annotated bibliographies or Google docs. If adequately supported, school…

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

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.

Isolation of mini- and microsatellite loci from chromosome 19 library

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prosnyak, M.I.; Belajeva, O.V.; Polukarova, L.G.

Mini- and microsatellite sequences are abundant in the human genome and are very useful as genetic markers. We report the isolation of a panel of clones containing marker sequences from chromosome 19. We screened 10,000 clones from the chromosome 19 cosmid library for the presence of di-(CA)n, tri-(TCC)n, (CAC)n microsatellites and M13-like minisatellite sequences. For this we have used synthetic oligonucleotides and polynucleotides, including micro- (CA, TCC, CAC) and minisatellite (M13 core) sequences. Preliminary results indicated that the chromosome 19 cosmid library contained both human and hamster clones. In order to identify human sequences from this library we have developedmore » the technique of colony and blot hybridization with Alu-PCR, L1-PCR and B1-PCR probes. Dozens of clones have been selected, some of which were analyzed by conventional Southern blot analysis and non-radioactive in situ hybridization of chromosomes. Highly informative markers derived from these clones will be used for physical and genetic mapping of chromosome 19.« less

Algorithm integration using ADL (Algorithm Development Library) for improving CrIMSS EDR science product quality

NASA Astrophysics Data System (ADS)

Das, B.; Wilson, M.; Divakarla, M. G.; Chen, W.; Barnet, C.; Wolf, W.

2013-05-01

Algorithm Development Library (ADL) is a framework that mimics the operational system IDPS (Interface Data Processing Segment) that is currently being used to process data from instruments aboard Suomi National Polar-orbiting Partnership (S-NPP) satellite. The satellite was launched successfully in October 2011. The Cross-track Infrared and Microwave Sounder Suite (CrIMSS) consists of the Advanced Technology Microwave Sounder (ATMS) and Cross-track Infrared Sounder (CrIS) instruments that are on-board of S-NPP. These instruments will also be on-board of JPSS (Joint Polar Satellite System) that will be launched in early 2017. The primary products of the CrIMSS Environmental Data Record (EDR) include global atmospheric vertical temperature, moisture, and pressure profiles (AVTP, AVMP and AVPP) and Ozone IP (Intermediate Product from CrIS radiances). Several algorithm updates have recently been proposed by CrIMSS scientists that include fixes to the handling of forward modeling errors, a more conservative identification of clear scenes, indexing corrections for daytime products, and relaxed constraints between surface temperature and air temperature for daytime land scenes. We have integrated these improvements into the ADL framework. This work compares the results from ADL emulation of future IDPS system incorporating all the suggested algorithm updates with the current official processing results by qualitative and quantitative evaluations. The results prove these algorithm updates improve science product quality.

Mesh Algorithms for PDE with Sieve I: Mesh Distribution

DOE PAGES

Knepley, Matthew G.; Karpeev, Dmitry A.

2009-01-01

We have developed a new programming framework, called Sieve, to support parallel numerical partial differential equation(s) (PDE) algorithms operating over distributed meshes. We have also developed a reference implementation of Sieve in C++ as a library of generic algorithms operating on distributed containers conforming to the Sieve interface. Sieve makes instances of the incidence relation, or arrows, the conceptual first-class objects represented in the containers. Further, generic algorithms acting on this arrow container are systematically used to provide natural geometric operations on the topology and also, through duality, on the data. Finally, coverings and duality are used to encode notmore » only individual meshes, but all types of hierarchies underlying PDE data structures, including multigrid and mesh partitions. In order to demonstrate the usefulness of the framework, we show how the mesh partition data can be represented and manipulated using the same fundamental mechanisms used to represent meshes. We present the complete description of an algorithm to encode a mesh partition and then distribute a mesh, which is independent of the mesh dimension, element shape, or embedding. Moreover, data associated with the mesh can be similarly distributed with exactly the same algorithm. The use of a high level of abstraction within the Sieve leads to several benefits in terms of code reuse, simplicity, and extensibility. We discuss these benefits and compare our approach to other existing mesh libraries.« less

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

PubMed

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.

Cartographic symbol library considering symbol relations based on anti-aliasing graphic library

NASA Astrophysics Data System (ADS)

Mei, Yang; Li, Lin

2007-06-01

Cartographic visualization represents geographic information with a map form, which enables us retrieve useful geospatial information. In digital environment, cartographic symbol library is the base of cartographic visualization and is an essential component of Geographic Information System as well. Existing cartographic symbol libraries have two flaws. One is the display quality and the other one is relations adjusting. Statistic data presented in this paper indicate that the aliasing problem is a major factor on the symbol display quality on graphic display devices. So, effective graphic anti-aliasing methods based on a new anti-aliasing algorithm are presented and encapsulated in an anti-aliasing graphic library with the form of Component Object Model. Furthermore, cartographic visualization should represent feature relation in the way of correctly adjusting symbol relations besides displaying an individual feature. But current cartographic symbol libraries don't have this capability. This paper creates a cartographic symbol design model to implement symbol relations adjusting. Consequently the cartographic symbol library based on this design model can provide cartographic visualization with relations adjusting capability. The anti-aliasing graphic library and the cartographic symbol library are sampled and the results prove that the two libraries both have better efficiency and effect.

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.

ChemTS: an efficient python library for de novo molecular generation

PubMed Central

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Abstract Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS. PMID:29435094

Evidential significance of automotive paint trace evidence using a pattern recognition based infrared library search engine for the Paint Data Query Forensic Database.

PubMed

Lavine, Barry K; White, Collin G; Allen, Matthew D; Fasasi, Ayuba; Weakley, Andrew

2016-10-01

A prototype library search engine has been further developed to search the infrared spectral libraries of the paint data query database to identify the line and model of a vehicle from the clear coat, surfacer-primer, and e-coat layers of an intact paint chip. For this study, search prefilters were developed from 1181 automotive paint systems spanning 3 manufacturers: General Motors, Chrysler, and Ford. The best match between each unknown and the spectra in the hit list generated by the search prefilters was identified using a cross-correlation library search algorithm that performed both a forward and backward search. In the forward search, spectra were divided into intervals and further subdivided into windows (which corresponds to the time lag for the comparison) within those intervals. The top five hits identified in each search window were compiled; a histogram was computed that summarized the frequency of occurrence for each library sample, with the IR spectra most similar to the unknown flagged. The backward search computed the frequency and occurrence of each line and model without regard to the identity of the individual spectra. Only those lines and models with a frequency of occurrence greater than or equal to 20% were included in the final hit list. If there was agreement between the forward and backward search results, the specific line and model common to both hit lists was always the correct assignment. Samples assigned to the same line and model by both searches are always well represented in the library and correlate well on an individual basis to specific library samples. For these samples, one can have confidence in the accuracy of the match. This was not the case for the results obtained using commercial library search algorithms, as the hit quality index scores for the top twenty hits were always greater than 99%. Copyright © 2016 Elsevier B.V. All rights reserved.

Phenotypic mutant library: potential for gene discovery

USDA-ARS?s Scientific Manuscript database

The rapid development of high throughput and affordable Next- Generation Sequencing (NGS) techniques has renewed interest in gene discovery using forward genetics. The conventional forward genetic approach starts with isolation of mutants with a phenotype of interest, mapping the mutation within a s...

Display of a maize cDNA library on baculovirus infected insect cells.

PubMed

Meller Harel, Helene Y; Fontaine, Veronique; Chen, Hongying; Jones, Ian M; Millner, Paul A

2008-08-12

Maize is a good model system for cereal crop genetics and development because of its rich genetic heritage and well-characterized morphology. The sequencing of its genome is well advanced, and new technologies for efficient proteomic analysis are needed. Baculovirus expression systems have been used for the last twenty years to express in insect cells a wide variety of eukaryotic proteins that require complex folding or extensive posttranslational modification. More recently, baculovirus display technologies based on the expression of foreign sequences on the surface of Autographa californica (AcMNPV) have been developed. We investigated the potential of a display methodology for a cDNA library of maize young seedlings. We constructed a full-length cDNA library of young maize etiolated seedlings in the transfer vector pAcTMVSVG. The library contained a total of 2.5 x 10(5) independent clones. Expression of two known maize proteins, calreticulin and auxin binding protein (ABP1), was shown by western blot analysis of protein extracts from insect cells infected with the cDNA library. Display of the two proteins in infected insect cells was shown by selective biopanning using magnetic cell sorting and demonstrated proof of concept that the baculovirus maize cDNA display library could be used to identify and isolate proteins. The maize cDNA library constructed in this study relies on the novel technology of baculovirus display and is unique in currently published cDNA libraries. Produced to demonstrate proof of principle, it opens the way for the development of a eukaryotic in vivo display tool which would be ideally suited for rapid screening of the maize proteome for binding partners, such as proteins involved in hormone regulation or defence.

The High Level Data Reduction Library

NASA Astrophysics Data System (ADS)

Ballester, P.; Gabasch, A.; Jung, Y.; Modigliani, A.; Taylor, J.; Coccato, L.; Freudling, W.; Neeser, M.; Marchetti, E.

2015-09-01

The European Southern Observatory (ESO) provides pipelines to reduce data for most of the instruments at its Very Large telescope (VLT). These pipelines are written as part of the development of VLT instruments, and are used both in the ESO's operational environment and by science users who receive VLT data. All the pipelines are highly specific geared toward instruments. However, experience showed that the independently developed pipelines include significant overlap, duplication and slight variations of similar algorithms. In order to reduce the cost of development, verification and maintenance of ESO pipelines, and at the same time improve the scientific quality of pipelines data products, ESO decided to develop a limited set of versatile high-level scientific functions that are to be used in all future pipelines. The routines are provided by the High-level Data Reduction Library (HDRL). To reach this goal, we first compare several candidate algorithms and verify them during a prototype phase using data sets from several instruments. Once the best algorithm and error model have been chosen, we start a design and implementation phase. The coding of HDRL is done in plain C and using the Common Pipeline Library (CPL) functionality. HDRL adopts consistent function naming conventions and a well defined API to minimise future maintenance costs, implements error propagation, uses pixel quality information, employs OpenMP to take advantage of multi-core processors, and is verified with extensive unit and regression tests. This poster describes the status of the project and the lesson learned during the development of reusable code implementing algorithms of high scientific quality.

Finite element analysis and genetic algorithm optimization design for the actuator placement on a large adaptive structure

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.

Analysis of BAC end sequences in oak, a keystone forest tree species, providing insight into the composition of its genome

PubMed Central

2011-01-01

Background One of the key goals of oak genomics research is to identify genes of adaptive significance. This information may help to improve the conservation of adaptive genetic variation and the management of forests to increase their health and productivity. Deep-coverage large-insert genomic libraries are a crucial tool for attaining this objective. We report herein the construction of a BAC library for Quercus robur, its characterization and an analysis of BAC end sequences. Results The EcoRI library generated consisted of 92,160 clones, 7% of which had no insert. Levels of chloroplast and mitochondrial contamination were below 3% and 1%, respectively. Mean clone insert size was estimated at 135 kb. The library represents 12 haploid genome equivalents and, the likelihood of finding a particular oak sequence of interest is greater than 99%. Genome coverage was confirmed by PCR screening of the library with 60 unique genetic loci sampled from the genetic linkage map. In total, about 20,000 high-quality BAC end sequences (BESs) were generated by sequencing 15,000 clones. Roughly 5.88% of the combined BAC end sequence length corresponded to known retroelements while ab initio repeat detection methods identified 41 additional repeats. Collectively, characterized and novel repeats account for roughly 8.94% of the genome. Further analysis of the BESs revealed 1,823 putative genes suggesting at least 29,340 genes in the oak genome. BESs were aligned with the genome sequences of Arabidopsis thaliana, Vitis vinifera and Populus trichocarpa. One putative collinear microsyntenic region encoding an alcohol acyl transferase protein was observed between oak and chromosome 2 of V. vinifera. Conclusions This BAC library provides a new resource for genomic studies, including SSR marker development, physical mapping, comparative genomics and genome sequencing. BES analysis provided insight into the structure of the oak genome. These sequences will be used in the assembly of a future genome sequence for oak. PMID:21645357

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.

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.

An Object-Oriented Collection of Minimum Degree Algorithms: Design, Implementation, and Experiences

NASA Technical Reports Server (NTRS)

Kumfert, Gary; Pothen, Alex

1999-01-01

The multiple minimum degree (MMD) algorithm and its variants have enjoyed 20+ years of research and progress in generating fill-reducing orderings for sparse, symmetric positive definite matrices. Although conceptually simple, efficient implementations of these algorithms are deceptively complex and highly specialized. In this case study, we present an object-oriented library that implements several recent minimum degree-like algorithms. We discuss how object-oriented design forces us to decompose these algorithms in a different manner than earlier codes and demonstrate how this impacts the flexibility and efficiency of our C++ implementation. We compare the performance of our code against other implementations in C or Fortran.

T7 lytic phage-displayed peptide libraries: construction and diversity characterization.

PubMed

Krumpe, Lauren R H; Mori, Toshiyuki

2014-01-01

In this chapter, we describe the construction of T7 bacteriophage (phage)-displayed peptide libraries and the diversity analyses of random amino acid sequences obtained from the libraries. We used commercially available reagents, Novagen's T7Select system, to construct the libraries. Using a combination of biotinylated extension primer and streptavidin-coupled magnetic beads, we were able to prepare library DNA without applying gel purification, resulting in extremely high ligation efficiencies. Further, we describe the use of bioinformatics tools to characterize library diversity. Amino acid frequency and positional amino acid diversity and hydropathy are estimated using the REceptor LIgand Contacts website http://relic.bio.anl.gov. Peptide net charge analysis and peptide hydropathy analysis are conducted using the Genetics Computer Group Wisconsin Package computational tools. A comprehensive collection of the estimated number of recombinants and titers of T7 phage-displayed peptide libraries constructed in our lab is included.

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment

PubMed Central

Dasari, Surendra; Chambers, Matthew C.; Martinez, Misti A.; Carpenter, Kristin L.; Ham, Amy-Joan L.; Vega-Montoto, Lorenzo J.; Tabb, David L.

2012-01-01

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines. PMID:22217208

JDiffraction: A GPGPU-accelerated JAVA library for numerical propagation of scalar wave fields

NASA Astrophysics Data System (ADS)

Piedrahita-Quintero, Pablo; Trujillo, Carlos; Garcia-Sucerquia, Jorge

2017-05-01

JDiffraction, a GPGPU-accelerated JAVA library for numerical propagation of scalar wave fields, is presented. Angular spectrum, Fresnel transform, and Fresnel-Bluestein transform are the numerical algorithms implemented in the methods and functions of the library to compute the scalar propagation of the complex wavefield. The functionality of the library is tested with the modeling of easy to forecast numerical experiments and also with the numerical reconstruction of a digitally recorded hologram. The performance of JDiffraction is contrasted with a library written for C++, showing great competitiveness in the apparently less complex environment of JAVA language. JDiffraction also includes JAVA easy-to-use methods and functions that take advantage of the computation power of the graphic processing units to accelerate the processing times of 2048×2048 pixel images up to 74 frames per second.

Sensitivity Analysis of Genetic Algorithm Parameters for Optimal Groundwater Monitoring Network Design

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.

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.

Incremental cost-effectiveness of algorithm-driven genetic testing versus no testing for Maturity Onset Diabetes of the Young (MODY) in Singapore.

PubMed

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.

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.

A synthetic genetic edge detection program.

PubMed

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.

A Synthetic Genetic Edge Detection Program

PubMed Central

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

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.

AST: World Coordinate Systems in Astronomy

NASA Astrophysics Data System (ADS)

Berry, David S.; Warren-Smith, Rodney F.

2014-04-01

The AST library provides a comprehensive range of facilities for attaching world coordinate systems to astronomical data, for retrieving and interpreting that information in a variety of formats, including FITS-WCS, and for generating graphical output based on it. Core projection algorithms are provided by WCSLIB (ascl:1108.003) and astrometry is provided by the PAL (ascl:1606.002) and SOFA (ascl:1403.026) libraries. AST bindings are available in Python (pyast), Java (JNIAST) and Perl (Starlink::AST). AST is used as the plotting and astrometry library in DS9 and GAIA, and is distributed separately and as part of the Starlink software collection.

Libpsht - algorithms for efficient spherical harmonic transforms

NASA Astrophysics Data System (ADS)

Reinecke, M.

2011-02-01

Libpsht (or "library for performant spherical harmonic transforms") is a collection of algorithms for efficient conversion between spatial-domain and spectral-domain representations of data defined on the sphere. The package supports both transforms of scalars and spin-1 and spin-2 quantities, and can be used for a wide range of pixelisations (including HEALPix, GLESP, and ECP). It will take advantage of hardware features such as multiple processor cores and floating-point vector operations, if available. Even without this additional acceleration, the employed algorithms are among the most efficient (in terms of CPU time, as well as memory consumption) currently being used in the astronomical community. The library is written in strictly standard-conforming C90, ensuring portability to many different hard- and software platforms, and allowing straightforward integration with codes written in various programming languages like C, C++, Fortran, Python etc. Libpsht is distributed under the terms of the GNU General Public License (GPL) version 2 and can be downloaded from .

Libpsht: Algorithms for Efficient Spherical Harmonic Transforms

NASA Astrophysics Data System (ADS)

Reinecke, Martin

2010-10-01

Libpsht (or "library for Performing Spherical Harmonic Transforms") is a collection of algorithms for efficient conversion between spatial-domain and spectral-domain representations of data defined on the sphere. The package supports transforms of scalars as well as spin-1 and spin-2 quantities, and can be used for a wide range of pixelisations (including HEALPix, GLESP and ECP). It will take advantage of hardware features like multiple processor cores and floating-point vector operations, if available. Even without this additional acceleration, the employed algorithms are among the most efficient (in terms of CPU time as well as memory consumption) currently being used in the astronomical community. The library is written in strictly standard-conforming C90, ensuring portability to many different hard- and software platforms, and allowing straightforward integration with codes written in various programming languages like C, C++, Fortran, Python etc. Libpsht is distributed under the terms of the GNU General Public License (GPL) version 2. Development on this project has ended; its successor is libsharp (ascl:1402.033).

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.

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.

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.

Panel 1: Epidemiology and Diagnosis.

PubMed

Homøe, Preben; Kværner, Kari; Casey, Janet R; Damoiseaux, Roger A M J; van Dongen, Thijs M A; Gunasekera, Hasantha; Jensen, Ramon G; Kvestad, Ellen; Morris, Peter S; Weinreich, Heather M

2017-04-01

Objective To create a literature review between 2011 and June 1, 2015, on advances in otitis media (OM) epidemiology and diagnosis (including relevant audiology studies). Data Sources Electronic search engines (PubMed, EMBASE, and Cochrane Library) with a predefined search strategy. Review Methods Articles with appropriate epidemiologic methodology for OM, including acute mastoiditis and eustachian tube dysfunction. Items included OM worldwide and in high-risk populations, OM-related hearing loss, news in OM diagnostics, prenatal risk factors and comorbidities, postnatal risk factors, genetics, microbiological epidemiology, guidelines, and quality of life. Conclusions Diagnostic evidence and genetic studies are increasing; guidelines are introduced worldwide; and there is evidence of benefit of pneumococcal conjugate vaccines. New risk factors and comordities are identified in the study period, and quality of life is affected in children and their families. Implications for Practice Chronic suppurative OM occurs worldwide and contributes to lifelong hearing loss. Uniform definitions are still lacking and should be provided. An association between HIV and chronic suppurative OM has been found. Tympanometry is recommended for diagnosis, with or without pneumatic otoscopy. Video otoscopy, algorithms, and validated questionnaires may assist clinicians. Childhood obesity is associated with OM. Heritability accounts for 20% to 50% of OM diagnoses. OM-prone children seem to produce weaker immunologic responses to pneumococcal conjugate vaccines. Clinicians tend to individualize treatment without adhering to guidelines.

Genetic Constructor: An Online DNA Design Platform.

PubMed

Bates, Maxwell; Lachoff, Joe; Meech, Duncan; Zulkower, Valentin; Moisy, Anaïs; Luo, Yisha; Tekotte, Hille; Franziska Scheitz, Cornelia Johanna; Khilari, Rupal; Mazzoldi, Florencio; Chandran, Deepak; Groban, Eli

2017-12-15

Genetic Constructor is a cloud Computer Aided Design (CAD) application developed to support synthetic biologists from design intent through DNA fabrication and experiment iteration. The platform allows users to design, manage, and navigate complex DNA constructs and libraries, using a new visual language that focuses on functional parts abstracted from sequence. Features like combinatorial libraries and automated primer design allow the user to separate design from construction by focusing on functional intent, and design constraints aid iterative refinement of designs. A plugin architecture enables contributions from scientists and coders to leverage existing powerful software and connect to DNA foundries. The software is easily accessible and platform agnostic, free for academics, and available in an open-source community edition. Genetic Constructor seeks to democratize DNA design, manufacture, and access to tools and services from the synthetic biology community.

Sequencing cDNAs: An Introduction to DNA Sequence Analysis in the Undergraduate Molecular Genetics Course.

ERIC Educational Resources Information Center

Galewsky, Samuel

2000-01-01

Introduces a series of molecular genetics laboratories where students pick a single colony from a Drosophila melanogester embryo cDNA library and purify the plasmid, then analyze the insert through restriction digests and gel electrophoresis. (Author/YDS)

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.

spammpack, Version 2013-06-18

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-01-17

This library is an implementation of the Sparse Approximate Matrix Multiplication (SpAMM) algorithm introduced. It provides a matrix data type, and an approximate matrix product, which exhibits linear scaling computational complexity for matrices with decay. The product error and the performance of the multiply can be tuned by choosing an appropriate tolerance. The library can be compiled for serial execution or parallel execution on shared memory systems with an OpenMP capable compiler

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

Estimation of radiative and conductive properties of a semitransparent medium using genetic algorithms

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.

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.

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.

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.

Optimized design on condensing tubes high-speed TIG welding technology magnetic control based on genetic algorithm

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.

A Comparison of PETSC Library and HPF Implementations of an Archetypal PDE Computation

NASA Technical Reports Server (NTRS)

Hayder, M. Ehtesham; Keyes, David E.; Mehrotra, Piyush

1997-01-01

Two paradigms for distributed-memory parallel computation that free the application programmer from the details of message passing are compared for an archetypal structured scientific computation a nonlinear, structured-grid partial differential equation boundary value problem using the same algorithm on the same hardware. Both paradigms, parallel libraries represented by Argonne's PETSC, and parallel languages represented by the Portland Group's HPF, are found to be easy to use for this problem class, and both are reasonably effective in exploiting concurrency after a short learning curve. The level of involvement required by the application programmer under either paradigm includes specification of the data partitioning (corresponding to a geometrically simple decomposition of the domain of the PDE). Programming in SPAM style for the PETSC library requires writing the routines that discretize the PDE and its Jacobian, managing subdomain-to-processor mappings (affine global- to-local index mappings), and interfacing to library solver routines. Programming for HPF requires a complete sequential implementation of the same algorithm, introducing concurrency through subdomain blocking (an effort similar to the index mapping), and modest experimentation with rewriting loops to elucidate to the compiler the latent concurrency. Correctness and scalability are cross-validated on up to 32 nodes of an IBM SP2.

Mr.CAS-A minimalistic (pure) Ruby CAS for fast prototyping and code generation

NASA Astrophysics Data System (ADS)

Ragni, Matteo

There are Computer Algebra System (CAS) systems on the market with complete solutions for manipulation of analytical models. But exporting a model that implements specific algorithms on specific platforms, for target languages or for particular numerical library, is often a rigid procedure that requires manual post-processing. This work presents a Ruby library that exposes core CAS capabilities, i.e. simplification, substitution, evaluation, etc. The library aims at programmers that need to rapidly prototype and generate numerical code for different target languages, while keeping separated mathematical expression from the code generation rules, where best practices for numerical conditioning are implemented. The library is written in pure Ruby language and is compatible with most Ruby interpreters.

Structure-Based Virtual Screening of Commercially Available Compound Libraries.

PubMed

Kireev, Dmitri

2016-01-01

Virtual screening (VS) is an efficient hit-finding tool. Its distinctive strength is that it allows one to screen compound libraries that are not available in the lab. Moreover, structure-based (SB) VS also enables an understanding of how the hit compounds bind the protein target, thus laying ground work for the rational hit-to-lead progression. SBVS requires a very limited experimental effort and is particularly well suited for academic labs and small biotech companies that, unlike pharmaceutical companies, do not have physical access to quality small-molecule libraries. Here, we describe SBVS of commercial compound libraries for Mer kinase inhibitors. The screening protocol relies on the docking algorithm Glide complemented by a post-docking filter based on structural protein-ligand interaction fingerprints (SPLIF).

Direct volumetric rendering based on point primitives in OpenGL.

PubMed

da Rosa, André Luiz Miranda; de Almeida Souza, Ilana; Yuuji Hira, Adilson; Zuffo, Marcelo Knörich

2006-01-01

The aim of this project is to present a renderization by software algorithm of acquired volumetric data. The algorithm was implemented in Java language and the LWJGL graphical library was used, allowing the volume renderization by software and thus preventing the necessity to acquire specific graphical boards for the 3D reconstruction. The considered algorithm creates a model in OpenGL, through point primitives, where each voxel becomes a point with the color values related to this pixel position in the corresponding images.

SOI layout decomposition for double patterning lithography on high-performance computer platforms

NASA Astrophysics Data System (ADS)

Verstov, Vladimir; Zinchenko, Lyudmila; Makarchuk, Vladimir

2014-12-01

In the paper silicon on insulator layout decomposition algorithms for the double patterning lithography on high performance computing platforms are discussed. Our approach is based on the use of a contradiction graph and a modified concurrent breadth-first search algorithm. We evaluate our technique on 45 nm Nangate Open Cell Library including non-Manhattan geometry. Experimental results show that our soft computing algorithms decompose layout successfully and a minimal distance between polygons in layout is increased.

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.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

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.

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.

lsjk—a C++ library for arbitrary-precision numeric evaluation of the generalized log-sine functions

NASA Astrophysics Data System (ADS)

Kalmykov, M. Yu.; Sheplyakov, A.

2005-10-01

Generalized log-sine functions Lsj(k)(θ) appear in higher order ɛ-expansion of different Feynman diagrams. We present an algorithm for the numerical evaluation of these functions for real arguments. This algorithm is implemented as a C++ library with arbitrary-precision arithmetics for integer 0⩽k⩽9 and j⩾2. Some new relations and representations of the generalized log-sine functions are given. Program summaryTitle of program:lsjk Catalogue number:ADVS Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVS Program obtained from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing terms: GNU General Public License Computers:all Operating systems:POSIX Programming language:C++ Memory required to execute:Depending on the complexity of the problem, at least 32 MB RAM recommended No. of lines in distributed program, including testing data, etc.:41 975 No. of bytes in distributed program, including testing data, etc.:309 156 Distribution format:tar.gz Other programs called:The CLN library for arbitrary-precision arithmetics is required at version 1.1.5 or greater External files needed:none Nature of the physical problem:Numerical evaluation of the generalized log-sine functions for real argument in the region 0<θ<π. These functions appear in Feynman integrals Method of solution:Series representation for the real argument in the region 0<θ<π Restriction on the complexity of the problem:Limited up to Lsj(9)(θ), and j is an arbitrary integer number. Thus, all function up to the weight 12 in the region 0<θ<π can be evaluated. The algorithm can be extended up to higher values of k(k>9) without modification Typical running time:Depending on the complexity of problem. See text below.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roper, J; Bradshaw, B; Godette, K

Purpose: To create a knowledge-based algorithm for prostate LDR brachytherapy treatment planning that standardizes plan quality using seed arrangements tailored to individual physician preferences while being fast enough for real-time planning. Methods: A dataset of 130 prior cases was compiled for a physician with an active prostate seed implant practice. Ten cases were randomly selected to test the algorithm. Contours from the 120 library cases were registered to a common reference frame. Contour variations were characterized on a point by point basis using principle component analysis (PCA). A test case was converted to PCA vectors using the same process andmore » then compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best-matched library case. The seed arrangement was extracted from the best-matched case and used as a starting point for planning the test case. Computational time was recorded. Any subsequent modifications were recorded that required input from a treatment planner to achieve an acceptable plan. Results: The computational time required to register contours from a test case and evaluate PCA similarity across the library was approximately 10s. Five of the ten test cases did not require any seed additions, deletions, or moves to obtain an acceptable plan. The remaining five test cases required on average 4.2 seed modifications. The time to complete manual plan modifications was less than 30s in all cases. Conclusion: A knowledge-based treatment planning algorithm was developed for prostate LDR brachytherapy based on principle component analysis. Initial results suggest that this approach can be used to quickly create treatment plans that require few if any modifications by the treatment planner. In general, test case plans have seed arrangements which are very similar to prior cases, and thus are inherently tailored to physician preferences.« less

Design and implementation of three-dimension texture mapping algorithm for panoramic system based on smart platform

NASA Astrophysics Data System (ADS)

Liu, Zhi; Zhou, Baotong; Zhang, Changnian

2017-03-01

Vehicle-mounted panoramic system is important safety assistant equipment for driving. However, traditional systems only render fixed top-down perspective view of limited view field, which may have potential safety hazard. In this paper, a texture mapping algorithm for 3D vehicle-mounted panoramic system is introduced, and an implementation of the algorithm utilizing OpenGL ES library based on Android smart platform is presented. Initial experiment results show that the proposed algorithm can render a good 3D panorama, and has the ability to change view point freely.

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.

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.

Supervised chaos genetic algorithm based state of charge determination for LiFePO4 batteries in electric vehicles

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.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

PubMed

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.

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.

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.

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.

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.

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.

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.

SeqTrim: a high-throughput pipeline for pre-processing any type of sequence read

PubMed Central

2010-01-01

Background High-throughput automated sequencing has enabled an exponential growth rate of sequencing data. This requires increasing sequence quality and reliability in order to avoid database contamination with artefactual sequences. The arrival of pyrosequencing enhances this problem and necessitates customisable pre-processing algorithms. Results SeqTrim has been implemented both as a Web and as a standalone command line application. Already-published and newly-designed algorithms have been included to identify sequence inserts, to remove low quality, vector, adaptor, low complexity and contaminant sequences, and to detect chimeric reads. The availability of several input and output formats allows its inclusion in sequence processing workflows. Due to its specific algorithms, SeqTrim outperforms other pre-processors implemented as Web services or standalone applications. It performs equally well with sequences from EST libraries, SSH libraries, genomic DNA libraries and pyrosequencing reads and does not lead to over-trimming. Conclusions SeqTrim is an efficient pipeline designed for pre-processing of any type of sequence read, including next-generation sequencing. It is easily configurable and provides a friendly interface that allows users to know what happened with sequences at every pre-processing stage, and to verify pre-processing of an individual sequence if desired. The recommended pipeline reveals more information about each sequence than previously described pre-processors and can discard more sequencing or experimental artefacts. PMID:20089148

RANdom SAmple Consensus (RANSAC) algorithm for material-informatics: application to photovoltaic solar cells.

PubMed

Kaspi, Omer; Yosipof, Abraham; Senderowitz, Hanoch

2017-06-06

An important aspect of chemoinformatics and material-informatics is the usage of machine learning algorithms to build Quantitative Structure Activity Relationship (QSAR) models. The RANdom SAmple Consensus (RANSAC) algorithm is a predictive modeling tool widely used in the image processing field for cleaning datasets from noise. RANSAC could be used as a "one stop shop" algorithm for developing and validating QSAR models, performing outlier removal, descriptors selection, model development and predictions for test set samples using applicability domain. For "future" predictions (i.e., for samples not included in the original test set) RANSAC provides a statistical estimate for the probability of obtaining reliable predictions, i.e., predictions within a pre-defined number of standard deviations from the true values. In this work we describe the first application of RNASAC in material informatics, focusing on the analysis of solar cells. We demonstrate that for three datasets representing different metal oxide (MO) based solar cell libraries RANSAC-derived models select descriptors previously shown to correlate with key photovoltaic properties and lead to good predictive statistics for these properties. These models were subsequently used to predict the properties of virtual solar cells libraries highlighting interesting dependencies of PV properties on MO compositions.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Characterization of Three Maize Bacterial Artificial Chromosome Libraries toward Anchoring of the Physical Map to the Genetic Map Using High-Density Bacterial Artificial Chromosome Filter Hybridization1

PubMed Central

Yim, Young-Sun; Davis, Georgia L.; Duru, Ngozi A.; Musket, Theresa A.; Linton, Eric W.; Messing, Joachim W.; McMullen, Michael D.; Soderlund, Carol A.; Polacco, Mary L.; Gardiner, Jack M.; Coe, Edward H.

2002-01-01

Three maize (Zea mays) bacterial artificial chromosome (BAC) libraries were constructed from inbred line B73. High-density filter sets from all three libraries, made using different restriction enzymes (HindIII, EcoRI, and MboI, respectively), were evaluated with a set of complex probes including the185-bp knob repeat, ribosomal DNA, two telomere-associated repeat sequences, four centromere repeats, the mitochondrial genome, a multifragment chloroplast DNA probe, and bacteriophage λ. The results indicate that the libraries are of high quality with low contamination by organellar and λ-sequences. The use of libraries from multiple enzymes increased the chance of recovering each region of the genome. Ninety maize restriction fragment-length polymorphism core markers were hybridized to filters of the HindIII library, representing 6× coverage of the genome, to initiate development of a framework for anchoring BAC contigs to the intermated B73 × Mo17 genetic map and to mark the bin boundaries on the physical map. All of the clones used as hybridization probes detected at least three BACs. Twenty-two single-copy number core markers identified an average of 7.4 ± 3.3 positive clones, consistent with the expectation of six clones. This information is integrated into fingerprinting data generated by the Arizona Genomics Institute to assemble the BAC contigs using fingerprint contig and contributed to the process of physical map construction. PMID:12481051

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

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.

Using Genetic Algorithm and MODFLOW to Characterize Aquifer System of Northwest Florida

EPA Science Inventory

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...