BeamNG.drive Black Terrain fix for Linux and Mac

A Binance Bot that Trades Macd Crossovers

Genetic algorithms are heuristic search algorithms inspired by the process that supports the evolution of life. The algorithm is designed to replicate the natural selection process to carry generation, i.e. survival of the fittest of beings.

Implementation of NSGA-II in Python

GARNE: Genetic-Algorithm-with-Recurrent-Network-and-Novelty-Exploration

In this project we implement a DNA-Genetic Encryption Technique (D-GET) in order to make the DNA SEQUENCE more secure and less predictable.

a python program thart uses genetic algorithm to find max point of y = sin(10Πx)+2

Using genetic algorithm for finding the shortest and optimized route based on its environment.

A Python script that solves the traveling salesman problem using genetic algorithms. The cities and the distances are predetermined but can also be randomly generated.

This code provides a solver for the Traveling Salesman Problem (TSP) using a genetic algorithm approach.

An audio player for wireless stereo active speakers

This is a script that defines a class for training a neural network using a genetic algorithm. You will have to score it yourself (because of the nature of genetic algorithms), but otherwise it works well. The methods included are slice and random crossover, as well as top 2 and weighted pick for the selection method.

This Python 3 module utilizes the Obspy framework to read SEG2 refraction datasets and plot interactive stream datasets using the matplotlib.pyplot.ginput function.

I developed this project to delve into Genetic Algorithms and their application to optimization problems. Feel free to explore the code, run the algorithm, and share your feedback.

This project offers the method in D. Ge, C. Wang, Z. Xiong, and Y. Ye's paper From an Interior Point to a Corner Point: Smart Crossover.

Using an evolutionary algorithm coupled with methods of genetic programming, this program evolves a random string into my name over a series of generations.

Travelling Salesperson Problem solved using Genetic Algorithm

This project implements Genetic Programming (GP) to evolve and optimize mathematical expressions that best fit given data. It leverages tree-based evolutionary algorithms to generate, evaluate, and refine expressions using selection, crossover, and mutation.

A playground for writing crossover functions and see how they perform on the N-Queens problem!

A simple genetic algorithm that solves the one-max problem