Genetic algorithm python github

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genetic-algorithm

Here are 1,746 public repositories matching this topic.

eriklindernoren / ML-From-Scratch

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 learning.

guofei9987 / scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

tradytics / eiten

Statistical and Algorithmic Investing Strategies for Everyone

anyoptimization / pymoo

NSGA2, NSGA3, R-NSGA3, MOEAD, Genetic Algorithms (GA), Differential Evolution (DE), CMAES, PSO

ahmedfgad / GeneticAlgorithmPython

Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).

MilesCranmer / PySR

High-Performance Symbolic Regression in Python and Julia

MorvanZhou / Evolutionary-Algorithm

Evolutionary Algorithm using Python, 莫烦Python 中文AI教学

joeddav / devol

Genetic neural architecture search with Keras

PytLab / gaft

A Genetic Algorithm Framework in Python (not for production level)

nemanja-m / gaps

A Genetic Algorithm-Based Solver for Jigsaw Puzzles 🌀

mpatacchiola / dissecting-reinforcement-learning

Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

100 / Solid

🎯 A comprehensive gradient-free optimization framework written in Python

thieu1995 / mealpy

A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)

iRB-Lab / py-ga-VRPTW

A Python Implementation of a Genetic Algorithm-based Solution to Vehicle Routing Problem with Time Windows

ahmedkhalf / Circle-Evolution

Evolutionary Art Using Circles in Python

kaushalshetty / FeatureSelectionGA

Feature Selection using Genetic Algorithm (DEAP Framework)

rishal-hurbans / Grokking-Artificial-Intelligence-Algorithms

The official code repository supporting the book, Grokking Artificial Intelligence Algorithms

aqibsaeed / Genetic-CNN

CNN architecture exploration using Genetic Algorithm

ahmedfgad / NeuralGenetic

Building and training artificial neural networks (regression or classification) using the genetic algorithm.

jaswinder9051998 / zoofs

zoofs is a python library for performing feature selection using a variety of nature-inspired wrapper algorithms. The algorithms range from swarm-intelligence to physics-based to Evolutionary. It’s easy to use , flexible and powerful tool to reduce your feature size.

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Implementation of a genetic algorithm in python

Itensoft/Genetic-Algorithm-Python

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Implementation of a genetic algorithm in python

Implement an optimization algorithm that finds and prints the following sentence:

«Individuals and interactions over processes and tools»

When the program is executed, it is requested to enter the following information

$> Enter the target text: $> Enter number of individuals per population [100 to 300]: $> Enter the mutation rate [0 to 1]: 

$> Enter the target text: Individuals and interactions over processes and tools $> Enter number of individuals per population [100 to 300]: 250 $> Enter the mutation rate [0 to 1]: 0.85 

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Implementation of a genetic algorithm in python

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Genetic Algorithms in Python

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jamwine/Genetic-Algorithms

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README.md

Genetic Algorithms in Python

FINAL SNAPSHOT

Traveling Salesman Solution

FINAL SNAPSHOT

Introduction to Genetic Algorithms

A type of machine learning that uses principles from nature to evolve a solution. Optimization is performed using evolutionary algorithms (EAs). The difference between traditional algorithms and EAs is that EAs are not static but dynamic as they can evolve over time.

The genetic algorithm is a random-based classical evolutionary algorithm. By random here we mean that in order to find a solution using the GA, random changes applied to the current solutions to generate new ones. Note that GA may be called Simple GA (SGA) due to its simplicity compared to other EAs. GA is based on Darwin’s theory of evolution. It is a slow gradual process that works by making changes to the making slight and slow changes. Also, GA makes slight changes to its solutions slowly until getting the best solution.

To summarize, the first step is to generate a random population of candidate solutions with each solution being a collection of data. The specifics of the representation of that data depends on the problem. After defining an initial population of randomly generated candidate solutions, the next step is to evaluate the fitness for each candidate in our population. That evaluation is specific to the particular problem we’re trying to solve, but it always involves using the candidate data, and it always results in a numeric fitness score for each candidate. Based on those fitness scores we select parent solutions with generally higher fitness values, and then crossover genetic information from each to form child solutions. Those child solutions may undergo a mutation, and then the children form the next generation of the population. This process is repeated until after a certain number of generations that candidate with the best fitness is chosen as the ultimate solution to the problem.

One of the important ideas in evolutionary computing is the solutions space. The solution space is a set of all possible solutions to a given problem. NP-hard or non-deterministic polynomial-time hardness problems refers to a class of problems that are difficult to find a solution for. Non-deterministic means that for any given attempt to solve a problem, we may end up with a different solution due to a strong element of randomness in evolutionary computing. The polynomial-time part of it refers to measuring how long it takes to find a solution. Combinatorial optimization is a technique of finding an optimal combination of a given set of objects where an exhaustive search is not feasible.

The genetic algorithm are effective in finding the optimal solutions, for example, finding an optimal subset of items to fit within a constrained area, optimally packing containers, resources allocation, shipping or dispatching, optimal ordering of data (where the number of possible permutations makes a brute-force approach impossible), optimal time manufacturing or scheduling. The genetic programming helps us in finding an equation to fit a set of data, to control a process, to control the movement of an object in space, to select stocks or investments, to generate keys for different types of cryptography, to create a strategy for picking a good starting hand for Texas Hold’em Poker, etc.

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Genetic Algorithms in Python

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