Showing 4 open source projects for "binary differential evolution"

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  • 1
    Evolutionary.jl

    Evolutionary.jl

    Evolutionary & genetic algorithms for Julia

    A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager.
    Downloads: 0 This Week
    Last Update:
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  • 2
    PlatEMO

    PlatEMO

    Evolutionary multi-objective optimization platform

    ...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 published in top journals after 2010. Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values. ...
    Downloads: 8 This Week
    Last Update:
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  • 3

    OpenDino

    Open Source Java platform for Optimization, DoE, and Learning.

    ...It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer
    Downloads: 0 This Week
    Last Update:
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  • 4
    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 meta-heuristic optimization algorithms. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process.
    Downloads: 1 This Week
    Last Update:
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