#c_lang #boids #ecosystem #evolution #game #genetic_algorithm #life #linux #simulation

https://github.com/connor-brooks/ecosim

#python #ai #algorithmic_trading #eigenvalues #free_software #genetic_algorithm #hedgefund #investment_portfolio #machine_learning #opensource #portfolio_optimization #statistics #trading_algorithms #trading_strategies #tradytics

https://github.com/tradytics/eiten

#go #evolutionary_algorithms #evolutionary_art #generative_art #genetic_algorithm #golang #gui #triangles #triangula

https://github.com/RH12503/triangula

#python #automl #distributed #genetic_algorithm #julia #machine_learning #numpy #symbolic_regression

https://github.com/MilesCranmer/PySR

#python #data_mining #data_science #deep_learning #deep_reinforcement_learning #genetic_algorithm #machine_learning #machine_learning_from_scratch

This project offers Python code for many basic machine learning models and algorithms built from scratch, focusing on clear, understandable implementations rather than speed or optimization. You can learn how these algorithms work inside by running examples like polynomial regression, convolutional neural networks, clustering, and genetic algorithms. This hands-on approach helps you deeply understand machine learning concepts and build your own custom models. Using Python makes it easier because of its simple, readable code and flexibility, letting you quickly test and modify algorithms. This can improve your skills and confidence in machine learning development.

https://github.com/eriklindernoren/ML-From-Scratch

#python #ant_colony_algorithm #artificial_intelligence #fish_swarms #genetic_algorithm #heuristic_algorithms #immune #immune_algorithm #optimization #particle_swarm_optimization #pso #simulated_annealing #travelling_salesman_problem #tsp

You can use scikit-opt, a Python library offering many heuristic optimization algorithms like Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony, Immune Algorithm, and Artificial Fish Swarm Algorithm. It supports user-defined functions to customize operators, allows continuing runs from previous iterations, and accelerates computations via vectorization, multithreading, multiprocessing, and caching. GPU support is in development. It helps solve complex optimization problems such as function minimization and the Traveling Salesman Problem efficiently, with easy installation and rich examples. This saves you time and effort in implementing and tuning optimization algorithms yourself.

https://github.com/guofei9987/scikit-opt