Showing 12 open source projects for "genetic algorithm engine"

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  • 1
    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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  • 2
    Evolutionary Algorithm

    Evolutionary Algorithm

    Evolutionary Algorithm using Python

    ...Users can explore basic genetic algorithm setups, match phrase examples, pathfinding challenges, and microbial GA variants, as well as evolution strategy approaches like NES. The project also links classical evolutionary approaches with neural networks, illustrating how evolution can be used for model training in reinforcement learning and supervised contexts.
    Downloads: 0 This Week
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  • 3
    Sudoku Maker is a generator for Sudoku number puzzles. It uses a genetic algorithm internally, so it can serve as an introduction to genetic algorithms. The generated Sudokus are usually very hard to solve -- good for getting rid of a Sudoku addiction.
    Downloads: 1 This Week
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  • 4
    Zipline

    Zipline

    Zipline, a Pythonic algorithmic trading library

    Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python...
    Downloads: 0 This Week
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  • 5
    Solid Python

    Solid Python

    A comprehensive gradient-free optimization framework written in Python

    Solid is a Python framework for gradient-free optimization. It contains basic versions of many of the most common optimization algorithms that do not require the calculation of gradients, and allows for very rapid development using them.
    Downloads: 3 This Week
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  • 6
    This project provides a set of Python tools for creating various kinds of neural networks, which can also be powered by genetic algorithms using grammatical evolution. MLP, backpropagation, recurrent, sparse, and skip-layer networks are supported.
    Downloads: 0 This Week
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  • 7
    Evolving Objects

    Evolving Objects

    This project have been merged within Paradiseo.

    See the new project page: https://nojhan.github.io/paradiseo/ (Archived project page: http://eodev.sourceforge.net/)
    Downloads: 0 This Week
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  • 8
    This project is a complete cross-platform (Windows, Linux) framework for Evolutionary Computation in pure python. See the project site at http://pyevolve.sourceforge.net or the blog at http://pyevolve.sourceforge.net/wordpress
    Downloads: 1 This Week
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  • 9
    Based on the introduction of Genetic Algorithms in the excellent book "Collective Intelligence" I have put together some python classes to extend the original concepts.
    Downloads: 0 This Week
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  • 10
    The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.
    Downloads: 0 This Week
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  • 11
    Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The
    Downloads: 0 This Week
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  • 12
    cosmos

    cosmos

    Algorithms that run our universe | Your personal library of every algo

    Cosmos (by OpenGenus Foundation) is your personal offline collection of every algorithm and data structure one will ever encounter and use in a lifetime. This provides solutions in various languages spanning C, C++, Java, JavaScript, Swift, Python, Go and others. This work is maintained by a community of hundreds of people and is a massive collaborative effort to bring the readily available coding knowledge offline.
    Downloads: 0 This Week
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