Statistical Machine Intelligence & Learning Engine
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Updated
Jul 16, 2024 - Java
Statistical Machine Intelligence & Learning Engine
Statistical genetic tool to infer common variants pleiotropy, using GWAS summary statistics
The software package SiteGA for de novo motif search in ChIP-seq data
Genetic Programming (GP) / Evolutonary Computation (EC) library for C++. Type-safe replacement for lilgp, et al. Integreates directly into the C++ typesystem.
Optimize linear regression parameters using a genetic algorithm. This project uses selection, crossover, and mutation to minimize the mean squared error all coded from scratch, demonstrating the application of genetic algorithms in machine learning model optimization.
This project uses a genetic algorithm to search for a target string. By evolving a population of random strings through selection, crossover, and mutation, the algorithm progressively approximates the target string over multiple generations.
High-Performance Symbolic Regression in Python and Julia
Proyecto basado en un sistema de control de nivel de agua por un controlador PI sintonizado por un algoritmo de optimización genético para el Laboratorio de Sistemas de Control de la UTN Facultad Regional Paraná.
Collaborative project by Ritik Bompilwar and Pratheesh. This repository focuses on classifying cricket shots using advanced machine learning techniques. Equal contributions were made in developing and optimizing the model to enhance accuracy and performance.
upload the trials concerning mathematics
A web-based fullstack application for scheduling staff by utilizing the power of a genetic algorithm.
Purely functional genetic algorithms for multi-objective optimisation
frontend for Flower Evolver website.
NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO
Collection of personal tools used for strategies analysis and algorithmic trading on Binance exchange.
Flappy Bird Game using Genetic Algorithm
🧬OO implement of genetic algorithm by python
This course covers the applied side of algorithmics in machine learning and deep learning, focusing on hands-on coding experience in Python.
Genetic Algorithms in Python3 (work in progress).
olfactory tracking with a focus on bioplausibility: genetic algorithm, spiking neural network, reinforcement learning
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