Showing 3 open source projects for "parallel genetic"

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  • 1
    Nevergrad

    Nevergrad

    A Python toolbox for performing gradient-free optimization

    Nevergrad is a Python library for derivative-free optimization, offering robust implementations of many algorithms suited for black-box functions (i.e. functions where gradients are unavailable or unreliable). It targets hyperparameter search, architecture search, control problems, and experimental tuning—domains in which gradient-based methods may fail or be inapplicable. The library provides an easy interface to define an optimization problem (parameter space, loss function, budget) and...
    Downloads: 4 This Week
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  • 2
    pgapack, the parallel genetic algorithm library is a powerfull genetic algorithm library by D. Levine, Mathematics and Computer Science Division Argonne National Laboratory. The library is written in C. PGAPy wraps this library for use with Python.
    Downloads: 0 This Week
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  • 3
    scikit-opt

    scikit-opt

    Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing

    scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an...
    Downloads: 0 This Week
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