Genetic algorithms (GAs) are a class of population-based metaheuristic search methods inspired by principles of natural selection and evolution. They solve complex optimisation problems by encoding ...

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep ...

This repository implements a genetic algorithm (GA) in Python3 programming language, using only Numpy and Joblib as additional libraries. It provides a basic StandardGA model as well as a more ...

Hash functions work by taking the virtual key for unlocking a specific point on a data table, scrambling that key and compressing it into a shorter code. This type of algorithm is already a ...

ABSTRACT: A new nano-based architectural design of multiple-stream convolutional homeomorphic error-control coding will be conducted, and a corresponding hierarchical implementation of important class ...

Functions are the building blocks of Python programs. They let you write reusable code, reduce duplication, and make projects easier to maintain. In this guide, we’ll walk through all the ways you can ...

Abstract: This paper deals with genetic algorithm implementation in Python. Genetic algorithm is a probabilistic search algorithm based on the mechanics of natural selection and natural genetics. In ...

Abstract: This paper describes the Jaya Algorithm and compares its performance with the Genetic Algorithm for optimizing the Himmelblau function and the Rosenbrock function. The Jaya algorithm is ...

ABSTRACT: The alternating direction method of multipliers (ADMM) and its symmetric version are efficient for minimizing two-block separable problems with linear constraints. However, both ADMM and ...