Showing 13 open source projects for "knapsack genetic algorithm"

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  • 1
    Geophysical Library is a C++-library to handle geophysical data. At the moment the focus is on magnetotelluric and seismic data. It also includes a genetic algorithm for data inversion, signal processing and statistics routines.
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  • 2

    popt4jlib

    Parallel Optimization Library for Java

    popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known network flow and other graph algorithms. A fast parallel implementation of the network simplex method, and some full-fledged parallel/distributed MIP solvers will be added in the next version. ...
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  • 3
    Opt4J

    Opt4J

    Modular Java framework for meta-heuristic optimization

    Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary...
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  • 4

    PyGAO

    Genetic Algorithm Optimization for Python

    A simple interface for performing genetic algorithm optimization for numerical problems. I am starting with a stripped-down version, where a solution can be described using a single vector of float numbers. Eventually, I will expand to more generic data structures and add multiple-species search options. For the time being, I have no plans of developing a GUI. For now, this is strictly a computational module.
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  • 5

    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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  • 6

    Bouc-Wen-Baber-Noori model of hysteresis

    Compute parameters of the Bouc-Wen-Baber-Noori model of hysteresis

    ..."A Fast and Elitist multi-objective Genetic Algorithm: NSGA-II". Journal IEEE Transactions on Evolutionary Computation (IEEE-TEC). 2002. Vol. 6. No. 2. pp. 182-197. - Gilberto A. Ortiz, Diego A. Alvarez, Daniel Bedoya-Ruiz. "Identification of Bouc-Wen type models using multi-objective optimization algorithms". Computers & Structures. Vol. 114-115. pp. 121-132. 2013.
    Downloads: 1 This Week
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  • 7
    PyPedal is a Python module that provides tools for the manipulation of pedigrees, simple visualization of pedigrees, and the calculation of measures of genetic diversity from pedigrees.
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    Downloads: 1 This Week
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  • 8
    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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  • 9
    GA++ is a Genetic Algorithm library written in C++,
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  • 10
    A framework for utilising the Genetic Algorithm in the domain of Game Theory
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  • 11

    gaDesignLib

    Genetic algorithm for mechatronic product design

    This C++/STL library provides you with the basic genetic algorithms needed for engineering design, such as simple GA and variable population.
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  • 12
    C++ genetic algorithms library (solving of the NP opnimisation problems). More GA strategies, more useability, more algorithm speed.
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  • 13
    Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
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