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86 results for “Heuristics”
Theory and a heuristic for the minimum path flow decomposition problem
<p>This is the data used in the following paper:</p> <p>Shao, Mingfu, and Carl Kingsford. "Theory and A Heuristic for the Minimum Path Flow Decomposition Problem." <em>IEEE/ACM Transactions on Computational Biology and Bioinformatics </em>(2017).</p>
Experimental data of the paper "Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces"
<p>This data set contains the code of our planner and of the planner that was used as baseline, the benchmark set that was used to perform experiments as well as the parsed values and basic reports that are reported in the paper. More information can be found in the README that is also included.</p>
Data for: Machine-learning-accelerated simulations enable heuristic-free surface reconstruction
<p>This is the dataset for the publication "Machine-learning-accelerated simulations to enable automatic surface reconstruction", by X. Du, J.K. Damewood, J.R. Lunger, R. Millan, B. Yildiz, L. Li, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to train the neural network force fields (NFF), selected results from our GaN(0001), Si(111), and SrTiO3(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the .ipynb's, you will need to install <a href="https://github.com/learningmatter-mit/surface-sampling">surface-sampling</a> (tested up to commit 02820d339eed6291b6af6ccb809f154ad6244110 on master) and <a href="https://github.com/learningmatter-mit/NeuralForceField">NeuralForceField</a> (tested up to commit 72d1f32f43f202c1a466116beeed15845a6456e7 on master) from the <a href="https://github.com/learningmatter-mit">Rafael Gómez-Bombarelli Group @ MIT</a>.</p>
Data set for risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Izdebski, M. (2023). Risk management in the allocation of vehicles to tasks in transport companies using a heuristic algorithm. Archives of Transport, 67(3), 139-153. https://doi.org/10.5604/01.3001.0053.7463 - published online: 2023-09-30, which discusses the allocation problem of vehicles to tasks, taking into account risk issues.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data used in the model</li> <li>DistributionFit.xlsx: Compliance testing and distribution parameters for road accidents of any type and collision-type</li> <li>OutputAssignment.xlsx: Results of assignment and alghoritm tests</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
GIS as a heuristic tool to interpret ancient historiography: A case study to reconstruct what could plausibly have happened according to the accounts in New Testament texts
<p>This presentation summarizes the research findings of the TiG article (doi 10.1111/tgis.12762) which examines how GIS can be used as a heuristic tool to reconstruct spatial–temporal events from narratives in order to examine whether a scenario is conceivable within the narrative world. The narrative about Paul's escape from Berea (Acts 17:14–15) is used as a case study. Several interpretive issues related to spatial and temporal questions surround these texts. In the case study, three methods are applied: (a) least-cost path analysis on elevation data to construct journeys and travel times for Roman roads; (b) network analysis to find seafaring routes valid for ancient times; and (c) the integration of spatial and temporal data in a space-time cube. Our main finding is that the method yields insights into the spatial–temporal dynamics of the narrative. This helps a modern reader to better understand the narrative conceivability of a story in the mind of a first-century reader.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 7. Execution plot, for instance Eil51 (left figure) and KroB100 (right figure)
<p>The evolution of the best solution found by the proposed algorithm is plotted in Figure 7 during a typical execution when solving instance Eil51 and KroB100. In this figure, the horizontal and vertical axes show the number of iterations and gained values of the proposed algorithm respectively. Besides, there is a fast convergence toward the BKS at the beginning of the execution while in the rest of the search the evolution of the BKS is not that fast. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 5. The process of MICALK for solving the TSP
<p>Moreover, in order to prevent the ICA from getting trapped in stagnation, we used a local searching algorithm when the algorithm attained a better solution compared to previous iterations. In fact, the probability of finding better solutions near a good solution is relatively high. There exist many algorithms for the local search and they have of course their pros and cons. Since LinKernighan algorithm is simple and it is one of the most successful methods for generating optimal or near optimal solutions for the TSP, we have used it in this study. The main steps of MICALK are summarized in the pseudo-code given in Figure 5. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 6. Some best routes found by the proposed algorithm
<p>Figure 6 shows some of the best solutions searched by the proposed method. In this figure, the horizontal axis represents the x-axis with increasing positive values to the right and the vertical axis represents the y-axis with increasing positive values upward. </p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 3. Flowchart of the ICA
<p>At last, the most powerful empire will take the possession of other empires and will win the competition. In other words, imperialistic competition hopefully converges to a state in which there exists only one empire and its colonies are in the same position and have the same cost as the imperialist. Figure 3 shows the flowchart of the basic ICA.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 1. The Initial Empires
<p>The ICA is a novel global search strategy which uses imperialism and imperialistic competition process as a source of inspiration. This algorithm is based on the fact that in a real world, countries try to extend their power over other countries in order to use their resources and bolster their own government. The first step in ICA is to generate an initial population like other evolutionary algorithms. The population set includes a number of feasible solutions called a ‘country’, which corresponds to the term ‘chromosome’ in the GA method. These countries are of two types: colonies and imperialists that altogether form some empires. As it is shown in Figure 1 (Atashpaz Gargari & Lucas, 2007), bigger and stronger empires have more colonies than smaller and weaker ones.</p>
BRAIN Journal-An Efficient Combined Meta-Heuristic Algorithm for Solving the Traveling Salesman Problem-Figure 2. Eliminate the weakest colony of the weakest empire
<p>After initial empires are formed, their colonies start moving toward their relevant imperialist country. This movement is a simple model of assimilation policy which was pursued by some of the imperialist states. If one of the colonies possesses more power than its relevant imperialist after this movement, they will exchange their positions. To begin the competition between empires, the total objective function of each empire should be calculated. It depends on the objective function of both an imperialist and its colonies. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take the possession of their colonies - Figure 2 (Atashpaz Gargari & Lucas, 2007). The empire, which has lost all its colonies, will collapse. </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 4. The solution of C6 found by CEACO
<p>Furthermore, the GA has not been able to find the best solutions in thirteen of the fourteen examples. Therefore, it is the weakest algorithm among the five presented algorithms. However, SS_ACO has been able to find better solutions than the GA and has come up with the best solution in 12 examples. Among remaining five algorithms, PSO has failed in improving the solutions in 10 examples and has come up with solutions similar to the ones found by GA. From the comparison between GAPSO and CEACO, it can be seen that GAPSO in six examples has been able to find better solutions than the proposed algorithm. However, the CEACO has found better solutions than this algorithm for one example. For example, the solution of C6 is shown in Figure 4 which is the best found solution until now by other algorithms. </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 3. The pseudo of CEACO
<p>The idea here is that a better solution may have a better chance to find a global optimum. After each iteration, if the best solution is not changed during 20 times, the algorithm finished and the best found solution is reported. Otherwise, the algorithm goes to transition rule step. Figure 3 shows the pseudo code of the proposed algorithm. </p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 1. The ACO for the TSP
<p>Ant colony optimization (ACO) is one of the most popular meta-heuristic algorithms inspired by the behavior of real ants seeking a path between their colony and a source of food. For the first time, this algorithm was used to solve the traveling salesman problem (TSP) as shown in Figure 1</p>
BRAIN Journal-A Combination of Meta-heuristic and Heuristic Algorithms for the VRP, OVRP and VRP with Simultaneous Pickup and Delivery- Figure 2. Insert (left), swap (middle) and 2-opt exchanges (right)
<p> The literature on meta-heuristics indicates that a promising approach for obtaining highquality solutions is to couple a local search algorithm with a mechanism to generate initial solutions. So, after all ants have constructed their routes and before updating global pheromone, three types of local search schemes including 2- opt scheme, insert and swap moves are performed to further reduce the routes length (Figure 2). In insert algorithm, a customer is moved to another route but in swap algorithm a customer in a certain route is swapped with another customer from a different route. One of the most commonly encountered moves is the 2-opt which starts with a feasible tour and continues by omitting two arcs of the same route, which are not adjacent and then connects them again by another method in such a way that the new tour length is shorter. In multiple routes, two edges belong to different routes, which form a criss-cross, are selected and two new edges are replaced. It also should be noted that the new solution will be only accepted in state that first, the constraints are not violated specially about each vehicle’s capacity. It can be noted that there are several routes for connecting nodes and producing the tour again, but a state that satisfies the problem’s constraints is acceptable</p>
Data to accompany the paper "Improved fragment-based protein structure prediction by redesign of search heuristics"
<p>This repository contains the older and newer input fragment sets and other data used for the analyses in our paper. The filenames for each tarball contain the PDB identifier of each protein along with a chain ID if applicable, followed by 'old' or 'new' for old and new fragments, respectively. Each tarball contains: a .fasta file of the input sequence, a matching PDB structure file, the relevant PSIPRED secondary structure prediction file, and the 9mer and 3mer fragment files. <br> <br> An additional tarball, ScoreRMSDplots_3protocols.tgz, contains extended versions of Figure 3 which show score and RMSD distributions clearly. Additionally, the same data is shown for equivalent experiments using the older fragment set.</p>
Dataset used in the paper "Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions"
<p>This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524</p>
SUSHI🍣: A Dataset of Synthetic Unichannel Signals Based on Heuristic Implementation (Tiny)
<div> <h2>Overview</h2> </div> <p>This dataset consists of pairs of time series signal data and corresponding natural language texts that describe the characteristics of these time series patterns. It has been designed with the objective of creating and evaluating a foundational model that facilitates natural language processing tasks, such as query-by-text retrieval for time series signals and captioning for these signals. All time series signals included in this dataset have been artificially generated from predefined combinations of multiple classes of functions. In addition, the paired natural language texts are primarily based on texts randomly selected from a pre-registered list corresponding to the functions, which have been manually refined through visual inspection.</p> <div> <h2>Specification</h2> </div> <p>We have two versions. This page provides "Tiny" under the Creative Commons Attribution 4.0 International license.</p> <table> <tbody> <tr> <th>Spec</th> <th>Tiny</th> <th>Base</th> </tr> </tbody> <tbody> <tr> <td><strong>Samples</strong></td> <td><strong>1.4K</strong></td> <td>140K</td> </tr> <tr> <td><strong>Time length</strong></td> <td><strong>2048 points</strong></td> <td>2048 points</td> </tr> <tr> <td><strong>Format</strong></td> <td><strong>CSV, NPY, PNG</strong></td> <td>CSV, NPY, PNG</td> </tr> </tbody> </table> <p>In addition, when “SUSHI” is referred to without the word “Tiny” in a paper or other document, it shall be taken to mean “Base”.</p> <h2>Detailed Information</h2> <p>The detailed infromation is availtable in the following documents:</p> <ul> <li>Arxiv preprint: Yohei Kawaguchi, Kota Dohi, and Aoi Ito, "SUSHI: A Dataset of Synthetic Unichannel Signals Based on Heuristic Implementation," in arXiv e-prints: zzzzzzz, 2024. <a href="http://,,,," target="_blank" rel="noopener">http://,,,,</a></li> <li>Githut repository: <a href="https://github.com/y-kawagu/SUSHI/" target="_blank" rel="noopener">https://github.com/y-kawagu/SUSHI/ </a></li> </ul> <h2>Citation</h2> <p>If you use this dataset, please cite the following paper:</p> <ul> <li>Yohei Kawaguchi, Kota Dohi, and Aoi Ito, "SUSHI: A Dataset of Synthetic Unichannel Signals Based on Heuristic Implementation," in arXiv e-prints: zzzzzzz, 2024. [URL]</li> </ul>
User Experience Evaluation of BRI Smart Billing Mobile Application Using System Usability Scale and Heuristic Evaluation
<p>This research aims to evaluate user perceptions of the design and functionality of the BRI Smart Billing Mobile app. Through a questionnaire distributed to active BRI Smart Billing users, this study will identify factors that influence user satisfaction with the visual appearance, layout of elements, and ease of use of features available on the dashboard. The results of this research are expected to provide input to the application developers regarding efforts to improve the quality of digital application services and provide recommendations for improving the design of the BRI Smart Billing Mobile dashboard to be more user-friendly and meet user needs.</p>
Figure 8 in The giraffe (Giraffa camelopardalis) cervical vertebral column: a heuristic example in understanding evolutionary processes?
Figure 8. These graphs compare the measured individual cervical vertebrae lengths of fossil Giraffids (Table 4), compared with extant giraffes and the 'other ungulate' group used in this study. The measurement of total vertebral column lengths (TVLs) for the fossil giraffids were generated from the regressions derived for extant giraffes or 'other ungulates', whereas the lengths of the individual cervical vertebrae were taken from the literature (see Table 4). Note that the specimens for Giraffa sp., Samotherium, and Paleotragus germaini appear to scale in a manner similar to extant giraffes, whereas those of Paleotragus primaevus, Climacoceras, and Canthumeryx appear to fall within the range of ungulates that do not demonstrate cervical elongation.
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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.