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2 repositorios

Awesome GitHub RepositoriesPopulation-Based Optimization

Optimization strategies that maintain and evolve a set of candidate solutions over multiple iterations.

Distinct from Iterative Deepening Search: Existing candidates focus on path-finding or depth-limiting search, not the evolution of a candidate population.

Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Population-Based Optimization. Refine with filters or upvote what's useful.

Awesome Population-Based Optimization GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • guofei9987/scikit-optAvatar de guofei9987

    guofei9987/scikit-opt

    6,583Ver en GitHub↗

    scikit-opt is a Python optimization library and numerical framework designed to solve complex global optimization problems. It provides a suite of metaheuristic algorithms and tools for finding global minima or maxima of objective functions. The library implements a variety of nature-inspired and swarm intelligence algorithms, including Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Simulated Annealing, and Ant Colony Optimization. It includes specialized solvers for discrete combinatorial challenges, such as the Traveling Salesman Problem. The framework supports th

    Maintains a population of candidate solutions that evolve through the search space over multiple generations.

    Python
    Ver en GitHub↗6,583
  • xviniette/flappylearningAvatar de xviniette

    xviniette/FlappyLearning

    3,993Ver en GitHub↗

    FlappyLearning is a machine learning sandbox and neural network trainer designed to simulate neuroevolutionary strategies. It provides an environment where artificial agents are evolved using genetic algorithms to optimize performance within a game simulation. The system employs a neuroevolutionary population model that utilizes feedforward neural networks to develop autonomous game agents. It manages the evolution of these agents through generation-based iteration, using fitness-based selection to determine which networks survive and reproduce. The training framework incorporates stochastic

    Maintains an evolving group of candidate neural networks to optimize game performance.

    JavaScriptflappybirdmachine-learningneuroevolution
    Ver en GitHub↗3,993
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