genetic-algorithm | Generic implementation of genetic algorithm in Java | Machine Learning library
kandi X-RAY | genetic-algorithm Summary
kandi X-RAY | genetic-algorithm Summary
Generic implementation of [Genetic algorithm] in Java.
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- Main program
- Returns the fitness for the given chromosome
- Trim the population to a specific length
- Adds a chromosome
- Sets the terminate flag
- evolution
- Gets the best chromosome
- Gets the iteration number
- Add an iteration listener
- Sort population by fitness
- Returns a random chromosome
- Creates a random population
- Add listener to listen for each iteration
- Returns the worst chromosome
- Gets a chromosome by index
- Get the number of chromosomes
- Clear the cache
- Removes an iteration listener
- Returns an iterator over the chromosomes in reverse order
genetic-algorithm Key Features
genetic-algorithm Examples and Code Snippets
Lines of Code : 40License : Permissive (MIT License)
Copydef genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True):
global route
global progress
population = initial_population(popsize=popsize, cities=cities)
if plot:
animate_progress()
def train():
cities = load_cities()
generations = 1000
popsizes = [60, 100, 140, 180]
elitesizes = [5, 15, 25, 35, 45]
mutation_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01]
total_iterations = len(popsizes) * len(elitesizes) * Community Discussions
Trending Discussions on genetic-algorithm
QUESTION
Here is the code I am writing: Fake News Detection Google Colab Notebook
The dataset I use: fake_or_real_news
The glove embedding layer: glove.twitter.27B.200d
I've been trying out PyGAD, a python library for genetic algorithm used on machine learning.
What I want to achieve is fake news detection. What I have done is preprocess the articles, and transform them into vectors. I use Glove as the embedding layer in the NN. I've tried to train using the NN model without GA, and it worked fine. Then I applied the NN to PyGAD GA following the tutorial: How To Train Keras Models Using the Genetic Algorithm with PyGAD, the process seemed to be running fine, but the fitness score wasn't going up at all even after 200 generations. I've tried to change the mutation method and some other hyper parameters, but it doesn't seem to change the outcome. What have I done wrong in the process of building the PyGAD GA model? Most of the PyGAD model settings are the same as the examples in the tutorial above.
To specify the problem I am encountering: Below is the main PyGAD code I am using:
Training input(X_train):
...ANSWER
Answered 2021-May-01 at 18:28Your model has large number of parameters (>6.1M). Only the embedding layer has 6M for itself. For a number of parameters like that, the genetic algorithm is expected to take much time for training the model. It does not mean you made a problem. I already tried working with a huge CNN before and there was a progress but very small.
According to the capabilities of your machine, you should increase the number of solutions as much as possible. Also use as many generation as possible.
Thanks for using PyGAD!
QUESTION
Im trying to recreate this code: https://github.com/Code-Bullet/Smart-Dots-Genetic-Algorithm-Tutorial/tree/master/BestTutorialEver , but in python, and it doesn't work, it keeps mutating the best dot and every generation starts with less dots. Here is the code (i use pygame for graphics):
Brain class:
...ANSWER
Answered 2021-Feb-19 at 05:39I did not try the project you mentioned. You may try PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms. It is open-source where you can find the code at GitHub.
It is simple to use which allows you to control the crossover, mutation, and parent selection operators in an easy way. You can also control many parameters of the genetic algorithm using PyGAD.
PyGAD also works with a user-defined fitness function so you can adapt it to a wide-range of problems.
After installing PyGAD (pip install pygad), here is a simple example to get started that tries to find the best values for W1, W2, and W3 that satisfies the following equation:
44 = 4xW_1 - 2xW_2 + 1.2xW_3
QUESTION
I'm experimenting with a genetic search algorithm and after building the initial population at random, and then selecting the top two fittest entries, I need to 'mate' them (with some random mutation) to create 64 'children'. The crossover part, explained here:
seems easy to follow, but I can't seem to figure out how to implement it in Python. How can I implement this crossover of two integers?
...ANSWER
Answered 2020-Mar-14 at 22:53Here is a function called crossover that takes two parents and a crossover point. The parents should be lists of integers of the same length. The crossover point is the point before which genes get exchanged, as defined in the article that you linked to. It returns the two offspring of the parents.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install genetic-algorithm
git clone https://github.com/lagodiuk/genetic-algorithm.git </li>
mvn -f genetic-algorithm/pom.xml install </li>
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