Showing 6 open source projects for "genetic algorithm cuda"

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  • 1
    TIGRE

    TIGRE

    TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

    ...Its focus is on iterative algorithms for improved image quality that have all been optimized to run on GPUs (including multi-GPUs) for improved speed. It combines the higher-level abstraction of MATLAB or Python with the performance of CUDA at a lower level in order to make it both fast and easy to use. TIGRE is free to download and distribute: use it, modify it, add to it, and share it. Our aim is to provide a wide range of easy-to-use algorithms for the tomographic community "off the shelf". We would like to build a stronger bridge between algorithm developers and imaging researchers/clinicians by encouraging and supporting contributions from both sides to TIGRE.
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  • 2
    TSNE-CUDA

    TSNE-CUDA

    GPU Accelerated t-SNE for CUDA with Python bindings

    This repo is an optimized CUDA version of FIt-SNE algorithm with associated python modules. We find that our implementation of t-SNE can be up to 1200x faster than Sklearn, or up to 50x faster than Multicore-TSNE when used with the right GPU. You can install binaries with anaconda for CUDA version 10.1 and 10.2 using conda install tsnecuda -c conda-forge. Tsnecuda supports CUDA versions 9.0 and later through source installation, check out the wiki for up to date installation instructions. ...
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  • 3

    Sched-SPM

    A C++ schedule generator based on Genetic Algorithm and Hill Climbing

    Sched-SPM is a C++ schedule generator for software project staffing and rescheduling based on Genetic Algorithm (GA) and Hill Climbing (HC). This preliminary tool is mainly for academic purpose. It is implemented with GALib (http://lancet.mit.edu/ga/), an open-source toolkit of Genetic Algorithms in various platforms including Unix and Windows. The input and output files of the software are required as XML format. The input file includes tasks' and employees' information according to predefined file format. ...
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  • 4
    Darwin 2: Java Framework for Evolutionary Computation (genetic algorithm, GA). A true framework with out-of-the-box functionality and extensibility of all classes. Interface-based pattern with dependency-injection to configure components.
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  • 5
    PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.
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  • 6
    TA scheduler based on a genetic algorithm to make the tedious task of scheduling TA's more efficient.
    Downloads: 0 This Week
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