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5,573 results for “Optimality”
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 6. Mutation process
<p>The individuals obtained at the end of crossing over might not provide the desired level of variability. In that case, the produced individuals are mutated independently from another individual in such a way that their own gene sequence will change. The mutation process is performed in the event that the mutation possibility that is specified in the beginning comes true. The results obtained from mutation can enhance the outcome or make it worse. It is of utmost importance to specify the most suitable mutation possibility. This possibility should be high enough to prevent the method from becoming stuck at a local point, but at the same time, low enough to allow the best results produced by crossing over and multiplexing. In this study, the mutation possibility was selected as 10%, and the locations of two randomly selected bus stops were changed during the mutation process. As in the crossing over, also during this process, the limitations regarding producing a new individual (route) were adapted. Figure 6 shows an example to mutation process.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 4. Example of a chromosome structure with permutation coding
<p>Each chromosome found in the population formed in the GA is structurally an equal-length coded series. The chromosomes are made of genes. For coding purposes, binary, permutation, and value coding methods are widely used. In the travelling salesman or other similar VRPs, permutation coding technique is preferred over the other techniques. Using the permutation coding technique, each chromosome found in the population is expressed in terms of the numbers of each stop to be followed in the route, as shown in Figure 4.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 3. The flow diagram of the SBRP solution by using GA
<p>The distance optimization needed for the formation of the objective function that can be seen in equation number 1 was done using GA operators and parameters. The flowchart that can be seen in Figure 3 shows how the school bus routes are formed using GA.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 10. Obtained experimental results
<p>The GA procedure was carried out using the parameter values that can be seen in Table 1, for a total of 10 school bus services’ routes identified by means of mobile-based software for a school located in the Ankara Province. The obtained experimental results are shown in Table 2 and Figure 10. The algorithm working duration also includes the formation of a distances’ matrix.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 1. Example of a VRP solution
<p>VRP is widely described as the process where the distribution vehicles deliver goods in a depot to the clients found in geographically dispersed locations and then return to the depot on the optimum route. The VRP, first introduced by Dantzig and Ramser in 1959, aims to minimize the total distance to be covered during the routing of the vehicles in a centrally-located depot (Dantzig & Ramser, 1959). The routing process is operated by taking the vehicle capacities into consideration so as to ensure that each client located on the routing plan is visited only once. The depot, vehicle, and goods concepts mentioned in the general description of VRP can be re-adapted as school, student and bus stop, based on different sectors that the problem applies to. Figure 1 shows a scheme indicating the VRP solution applied to a distribution system, where the clientele network is dispersed over three different zones.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 8. Listing of obtained solutions after running GA on the route
<p>During the route planning, school is the initial point if the students are going home from school, whereas it is the final point if they are going to the school from their homes. The application provides the opportunity to fix not only the school, but also the bus stops as initial or final points. Since the authors hope to develop a routing solution for more than one school in a future study, both the initial and final bus stops were given the opportunity to be selected so that the school bus can complete distribution/collection duties for one school and can go to another school for routing. When these selections are made, the initial and final genes of the chromosomes produced in the population were fixed through these selections. When the TSP option is on, on the other hand, the school bus returning to the initial point after completing the distribution/collection duties is included in the routing process. The information regarding solutions, route distances and the number of iterations produced after all the parameters are defined and the relevant selections are made are listed as shown in Figure 8. Under the solutions list, total crossover, total number of mutations, and algorithm working times are shown.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 5. Crossover process
<p>In the selection mechanism, the individuals passed down from the previous generation occasionally cannot produce a better individual. In that case, the compatibility of the individuals might worsen, while producing the exact opposite is what is expected. To avoid this, the elitism operator is used and it is ensured that the best individual of the previous generation is passed down to the next generation, even though the current population is diminishing on an overall basis as a result of the production operators (Goldberg, 1989). In the current study, the consecutive selection method and elitism selection were preferred. For this aim, after calculating the compatibility function, the population was ranked according to the population function values (total route length). In case the crossover possibility is realized, the number of individuals to select will be determined according to the parameter related to the crossover size. To ensure a high level of variability in the generation, it is suggested that this possibility is taken as 50% and 95% (Goldberg, 1989). The crossover process allows the production of a new individual using the genes taken from two individuals, based on the selected crossover method. In this study, a single point crossover method was selected. During the crossing over, limitations that were previously mentioned regarding the formation of a new initial population were taken into consideration. The identified initial or final point was fixed and kept out of the context of crossing over. An example of crossover can be seen in Figure 5.</p>
Using the Genetic Algorithm for the Optimization of Dynamic School Bus Routing Problem-Figure 2. A dynamic school bus routing case
<p>VRP’s can also be classified into two categories as dynamic and static routing problems. In the static VRP’s, the stops/locations that the vehicle will visit are pre-specified and do not change during the distribution/collection process. In dynamic VRP’s, on the other hand, new stops can be added to the planned route during the process or certain stops can be omitted. In similar dynamic problems, some or all of the access points are not known in the beginning. These points are dynamically defined during the route design or planning stages. In the dynamic VRP, using a real- time communication network between the vehicle and decision-making system, the vehicle routes can be re-defined during the operation. This type of problems is defined as online or real-time problems by some scholars (Pillac, Gendreau, Guéret & Medaglia, 2013). Two examples of this can be certain orders getting cancelled or new orders being taken while a water distribution vehicle is on its route, or a school bus being informed on its route that certain students will be absent from school that day. In current conditions, dynamic VRP’s are more frequently needed, and are attributed with a more specific importance. The first study dealing with dynamic VRP was carried out by Wilson and Colvin (Pillac, Gendreau, Guéret & Medaglia, 2013). The enhancements in GPS, traffic sensors, and mobile communication systems caused a further acceleration in studies carried out in this field. Within the context of this study, DSBRP will be investigated. DSBRP is graphically explained in Figure 2.</p>
Towards identifying the optimal datasize for lexically-based Bayesian inference of linguistic phylogenies
<p>This repository contains the nexus files and MrBayes command files needed for running the experiments to determine the optimal word list size required for inferring the best phylogenies.</p> <p>The paper is forthcoming at <strong>The 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New-Mexico, USA</strong>.</p>
The Optimal Species Richness Environments for Human Populations
<p>This supporting document should allow one to recreate the analysis performed as part of “The optimal species richness environments for human populations, ” By Freeman et al. 2018 Submitted to PNAS July 2018. The annotated scripts in this directory (RichnessSIScripts.pdf) contain code to replicate the analysis, as well as code for additional analyses not included in the main paper or supplemental information. To replicate the analysis, one can either analyze the data files provided or build their own data set. As discussed in the main body of the text, we built three data sets following the procedures outlined by Tallavaara et al. (2017) for linking species richness values, net primary productivity and pathogen stress to each ethnographic case. We do not replicate the scripts provided by Tallavaara et al.(2017) as these are available, clear and should be cited when used.To replicate our analysis, one needs to set their working directory in R to the file location that contains the data files. There are 11 files that follow the naming convention “name.csv.” The 11 files are “MainFinal.csv”. “AGPOP3Eco.csv”, “HGFEM4R.csv”, “AGPOPClass.csv”, “CountryMeansEco2.csv”, “AGPOP3EcoH.csv”, “AGPOP3EcoL.csv”, “HiHG.csv”, “LowHG,csv”, “CountryMeansEco2H.csv”, and “CountryMeansEco2L.csv”. The first five files are the main files, the second six files are divided into high and low species richness environments by economy type for convenience. In each file, the variables are defined as follows:</p> <p>1. Group/Country–name of the ethnographic society of country</p> <p>2. Latitude–the latitude at the geographic center of a group’s territory or a country’s territory.</p> <p>3. Longitude–the longitude at the geographic center of a group’s territory or a country’s territory.</p> <p>4. Class–an ordinal ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details)</p> <p>5. Class2–an binary ranking of wealth and status differentiation among the hunter-gatherer and agriculturalists societies (see main text for more details).</p> <p>6. ECI–The average economic complexity index since 1973 as measured among modern countries.</p> <p>7. DENSITY–Population density in people per square kilometer. This is a point in time estimatefor hunter-gatherer and agricultural groups and an average density since 1973 among nation states.</p> <p>8. LnDENSITY–The natural log of population density</p> <p>9. npp–net primary productivity estimated at the center of each group’s territory</p> <p>10. npp2-Net primary productivity squared</p> <p>11. biodiv–Standardized estimate of species richness at the center of each group’s range.</p> <p>12. biodiv2–Species richness *100 ad squared.</p> <p>13. pathos–Index of pathogen stress at the center of a group’s territory.</p> <p>14. DivDiff–The absolute value of species richness-the species richness value of peak population density (values identified in Fig. 2 of the main manuscript).1</p> <p>5. ID–A nominal variable that denotes economy type. HG=hunter-gatherer, AG=subsistence agriculturalist, IND=modern nation state</p> <p> </p> <p>Tallavaara, M., J. T. Eronen, and M. Luoto2017. Supporting data and script for ”productivity, biodiversity, and pathogens influence the global hunter-gatherer population density” (Tallavaara et al. pnas 2018).<a href="https://doi.org/10.5281/zenodo.1167852">https://doi.org/10.5281/zenodo.1167852</a></p>
SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas
<p>This dataset contains several data, results and processing material from the application of GEOBIA-based, Spatially Partitioned Segmentation Parameter Optimization (SPUSPO) in the city of Ouagadougou. In detail in contains:</p> <ul> <li><strong>A Land Use - Land Cover map of Ouagadougou derived through SPUSPO. The classifier used was Extreme Gradient Boosting (XGBoost). </strong> <p>Labels :</p> <p>2 : Artificial Ground Surface</p> <p>0 : Building</p> <p>5 : Low Vegetation</p> <p>4 : Tree</p> <p>1 : Swimming Pool</p> <p>3 : Bare Ground</p> <p>7 : Shadow</p> <p>6 : Inland Water</p> </li> </ul> <p> </p> <ul> <li><strong>The training and test data used in the study (SPUSPO and benchmark approach). </strong></li> </ul> <p>The data are given in a csv format.</p> <ul> <li><strong>The Jupyter notebook code which involves Python and GRASS GIS to automatize and efficiently perform SPUSPO in a large dataset.</strong></li> </ul> <p>Python code calling GRASS GIS functions for automatizing the procedure.</p> <p> </p> <ul> <li><strong>The segmentation layers coming from SPUSPO and the benchmark approaches (in raster formats due to data limitations).</strong></li> </ul> <p>Segmentation rasters for each approach.</p> <ul> <li><strong>The R code for optimization of XGBoost as well as feature selection with VSURF and classification of the whole dataset.</strong></li> </ul> <p> </p> <ul> <li><strong>Segmentation evaluation metrics.</strong></li> </ul> <p>A csv file with the data sued to compute the Area Fit Index for each approach.</p> <ul> <li><strong>Morphological zones of Ouagadougou as created by Grippa et al. 2017 a shp format.</strong></li> </ul>
Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption
<p>The data used to generate the graphs in Figures 2 and 3 of the paper "Less is More: Exploiting the Standard Compiler Optimization Levels for Better Performance and Energy Consumption" at SCOPES'18. The data includes power consumption, code size, and running time, indexed by an ID that identifies the combination of compiler options. Data is stored in a CSV file that identifies the specific benchmark, organized into a zip file that identifies the chip architecture.</p> <p>See README.txt for details.</p>
Dataset and software for "Optimizing beat synchronized running to music"
<p>These are the dataset and Matlab scripts that were used for analysis as described in the publication on "<strong>Optimizing beat synchronized running to music"</strong></p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
Data set of Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical Properties for Quantum Technologies
<p>Data corresponding to the figures of the publication " Ultrathin Eu- and Er-Doped Y2O3 Films with Optimized Optical<br> Properties for Quantum Technologies " by M. Scarafagio et al. J. Phys. Chem. C 2019, 123, 13354-13364<br> (<a href="https://doi.org/10.1021/acs.jpcc.9b02597">https://doi.org/10.1021/acs.jpcc.9b02597</a>). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p>
Matlab data for paper ""Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization" by Sahu et al.
<p>This is a Matlab data file for paper A.K. Sahu, D.Jakovetic, D. Bajovic and S. Kar, “Non-Asymptotic Rates for Communication Efficient Distributed Zeroth Order Strongly Convex Optimization”, IEEE Global Conference on Signal and Information Processing(GlobalSIP), 2018, see also: <a href="https://zenodo.org/record/3333576#.XSibRegzZPY">https://zenodo.org/record/3333576#.XSibRegzZPY</a></p>
Test data set for macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins
<p>This a bundle of test data can be used to run the macros accompanying the publication Multi-parameter screening method for developing optimized red fluorescent proteins.</p> <p>These data sets can be used to run the following macros that can be found on GitHub:</p> <ol> <li><a href="https://github.com/molcyto/MC-Ratio-96-wells">https://github.com/molcyto/MC-Ratio-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Ratio-Petri-dish">https://github.com/molcyto/MC-Ratio-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-Petri-dish">https://github.com/molcyto/MC-FLIM-Petri-dish</a></li> <li><a href="https://github.com/molcyto/MC-Bleach-96-wells">https://github.com/molcyto/MC-Bleach-96-wells</a></li> <li><a href="https://github.com/molcyto/MC-Scatter5D">https://github.com/molcyto/MC-Scatter5D</a></li> <li><a href="https://github.com/molcyto/MC-FLIM-96-wells">https://github.com/molcyto/MC-FLIM-96-wells</a></li> </ol> <p>Funding:<br> This work was supported by the NWO CW-Echo grant 711.011.018 (M.A.H. and T.W.J.G.), grant 12149 (T.W.J.G.) from the Foundation for Technological Sciences (STW) from the Netherlands</p> <p> </p>
Data sets for " The nature of mean-field generation in three classes of optimal dynamos"
<pre>The tar archive Optimal_Dynamos.tar contains and index.html file with links to the run directories for each figure and the two tables of the paper "The nature of mean-field generation in three classes of optimal dynamos" by Axel Brandenburg (Nordita) and Long Chen (Durham University) with the temporary URL http://norlx51.nordita.org/~brandenb/tmp/long_chen. Corrections and updates are available on the active URL to this tar archive: https://www.nordita.org/~brandenb/projects/Optimal_Dynamos/</pre>
Figure 4 in Optimal FOraging OF NeOtrOpical Otters (CarnivOra: Mustelidae) in an urban river and predOminance OF generalist and sedentary fish in their diet
Figure 4. Results of the Ivlev's selectivity index for the dataset from: (A) May 2006 to September 2007; (B) September 2006 to January 2007 (wet season); (C) February to August 2007 (dry season).
Figures 1-2 in Optimal foraging or predator avoidance: why does the Amazon spider Hingstepeira folisecens (Araneae: Araneidae) adopt alternative foraging behaviors?
Figures 1-2. Hingstepeira folisecens orb web in an area of the Amazon forest, Brazil: (1) vertical orb web with the rolled dry leaf used as a shelter by the spider attached to the hub; (2) shelter's detail showing the entrance oriented just downwards and the spider leaving it. Scale bars: 1 = 10 mm, 2 = 5 mm.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.