Showing 5 open source projects for "genetic algorithm java"

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  • 1

    TimescapeGlobal

    a space-time interpolation geostatistical tool

    This software is a time-aware geostatistical tool. Including time variability adds further issues to the already complicated field of geostatistical interpolation. On the other hand, most of the distributions of variables used in Earth and environmental sciences show patterns of change over time, not only in space. This tool is a free, open-source software, based on a generalization of the Inverse Distance Weighted algorithm. It outputs three-dimensional, layered discrete models from which...
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  • 2

    spectralHMM

    A spectral method for inferring selection from time series data

    ***WARNING*** This software was migrated to: https://github.com/popgenmethods/spectralHMM Support and updates will only be available at this new address. This software implements the algorithms described in the following paper: Steinrücken, M., Bhaskar, A. and Song, Y.S. A novel spectral method for inferring general diploid selection from time series genetic data. Annals of Applied Statistics, Vol. 8, No. 4 (2014) 2203-2222
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  • 3
    coalescent

    coalescent

    Software tool for Research in Computational Population Genetics

    Development of exact and approximate methods (Importance Sampling and MCMC based) for computing likelihoods under the standard population genetic models of mutation,migration & recombination. Project issues are mainatined at https://freecode4susant.atlassian.net/browse/COALESCENT
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  • 4

    libfgen

    Library for optimization using a genetic algorithm or particle swarms

    libfgen is a library that implements an efficient and customizable genetic algorithm (GA). It also provides particle swarm optimization (PSO) functionality and an interface for real-valued function minimization or model fitting. It is written in C, but can also be compiled with a C++ compiler. Both Linux and Windows are supported.
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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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