Search Results for "genetic algorithm cuda"

Showing 4 open source projects for "genetic algorithm cuda"

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  • 1
    PlatEMO

    PlatEMO

    Evolutionary multi-objective optimization platform

    Evolutionary multi-objective optimization platform. PlatEMO consists of a number of MATLAB functions without using any other libraries. Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms...
    Downloads: 1 This Week
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  • 2
    TIGRE

    TIGRE

    TIGRE: Tomographic Iterative GPU-based Reconstruction Toolbox

    TIGRE is an open-source toolbox for fast and accurate 3D tomographic reconstruction for any geometry. 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...
    Downloads: 1 This Week
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  • 3

    BiRW

    Bi-random Walks for Phenome-Genome Association Prediction

    The availability of ontologies and systematic documentations of phenotypes and their genetic associations has enabled large-scale network-based global analyses of the association between the complete collection of phenotypes (phenome) and genes. BiRW is a package designed for analysis and prediction of the phenome-genome associations. BiRW package contains a program for analysis of circular bigraphs (CBGs) and the bi-random walk (BiRW) algorithm to capture the CBG patterns in the networks...
    Downloads: 0 This Week
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  • 4

    Open Genetic Algorithm Toolbox

    This is a MATLAB toolbox to run a GA on any problem you want to model.

    This is a toolbox to run a GA on any problem you want to model. You can use one of the sample problems as reference to model your own problem with a few simple functions. You can collaborate by defining new example problems or new functions for GA, such as scaling, selection or adaptation methods. In that case, you should then include your credits in the file, upload it to matlab central and contact the author. Suggestions are also welcome but naturally I won't be able to attend...
    Downloads: 0 This Week
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