8 results ·
● Live web index
T
towardsdatascience.com
article
https://towardsdatascience.com/using-genetic-algorithms-to-train-neural-netwo…
# Using Genetic Algorithms to Train Neural Networks. Many people use genetic algorithms as unsupervised algorithms, to optimize agents in certain environments, but do not realize that the implementation of neural networks into the agents as a possibility. Genetic Algorithms are a type of learning algorithm, that uses the idea that crossing over the weights of two good neural networks, would result in a better neural network. This is the neural network of the first agent. One could create a GAN within a Genetic algorithm, by making the agents propagate Generator networks, and the tests being the discriminators. def execute(pop_size,generations,threshold,X,y,network):. The main function is the execute function, that takes pop\_size,generations,threshold,X,y,network as parameters. This script describes the initialization of the weights and the propagation of the network for each agent’s neural network. yhat = agent.neural_network.propagate(X). `def crossover(agents,network,pop_size):. shapes = [a.shape for a in parent1.neural_network.weights]. genes1 = np.concatenate([a.flatten() for a in parent1.neural_network.weights]). genes2 = np.concatenate([a.flatten() for a in parent2.neural_network.weights]). agents = crossover(agents,network,pop_size). agent = ga.execute(100,5000,0.1,X,y,network).
M
medium.com
article
https://medium.com/@roopal.tatiwar20/neuroevolution-evolving-neural-network-w…
# Neuroevolution: Evolving Neural Network with Genetic Algorithms. Neuroevolution is a subfield of artificial intelligence (AI) and machine learning that combines evolutionary algorithms(like Genetic Algorithm) with neural networks. The primary idea behind neuroevolution is to evolve neural network architectures and/or their weights to solve problems or perform specific tasks. Before getting into neuroevolution in detail, let us first overview the concepts of neural networks and genetic algorithm. By marrying biological evolution principles with computational models, neuroevolution introduces a paradigm shift in the way neural networks learn, adapt, and solve complex problems. At its essence, neuroevolution harmonizes two powerful concepts — neural networks and genetic algorithms. Neuroevolution involves the application of genetic algorithms to enhance neural networks. They involve creating a population of neural networks, evaluating their performance on a given task, selecting the best-performing networks to serve as parents, and applying genetic operations (crossover and mutation) to produce a new generation of networks. Using Genetic Algorithms to Optimize Artificial Neural Networks..
R
reddit.com
article
https://www.reddit.com/r/genetic_algorithms/comments/zlmi2i/genetic_algorithm…
It uses a genetic Algorithm to train a population of Neural Networks based on fitness function. Our motivation was to bring Machine Learning
D
datascience.stackexchange.com
article
https://datascience.stackexchange.com/questions/38321/why-arent-genetic-algor…
#### Stack Exchange Network. # Why aren't Genetic Algorithms used for optimizing neural networks? Furthermore, training Neural Networks (especially deep ones) is hard and has many issues (non-convex cost functions - local minima, vanishing and exploding gradients etc.). Training Neural Networks (NNs) with Genetic Algorithms (GAs) is not only feasible, there are some niche areas where the performance is good enough to be used frequently. Genetic algorithms and other global searches for optimal parameters are robust in ways that gradient-based algorithms are not. In practice on a large multi-layer network, gradient methods are likely orders of magnitude faster than GA searches such as NEAT for finding network parameters. Still, their research uses backpropagation to train networks, but they use genetic algorithms to find a good architecture. There is another research for network architecture search, but they use bayesian optimization instead of genetic algorithms. ##### Stack Exchange Network.
C
comet.com
article
https://www.comet.com/site/blog/train-neural-networks-using-a-genetic-algorit…
This tutorial discussed how to build and train both classification and regression neural networks using the genetic algorithm using a Python library called
P
people.csail.mit.edu
research
https://people.csail.mit.edu/people/koehn/publications/gann94.pdf
Table of Content Abstract - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -2 Table of Content - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -3 Introduction - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -4 Overview- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -7 1 Defining the Problem - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -8 1.1 Neural Networks - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -8 1.2 Genetic Algorithms - - - - - - - - - - - - - - - - - - - - - - - - - - -
Y
youtube.com
video
https://www.youtube.com/watch?v=I_p-yfdyP1A
This is a first dive into Neural Network. Github https://github.com/johnBuffer/AutoFlappy The use of genetic algorithm makes it quite easy
D
discuss.pytorch.org
article
https://discuss.pytorch.org/t/genetic-algorithm-for-a-neural-network-training…
I am trying to set up a basic CNN that is going to be used later for GA training. It takes the structure of a CNN and outputs the fitness function and the chromosome (an array of weights). Right now I am reading this paper, and also looking into PyGAD library. from torch.utils.data import DataLoader. 'train' : torch.utils.data.DataLoader(train_data,. 'test' : torch.utils.data.DataLoader(test_data,. import torch.nn as nn. nn.Linear(16 * 14 * 14, batch_size * 8),. self.out = nn.Sequential(nn.Linear(batch_size*8, 10), nn.Softmax(dim=1)). h1 = cnn.conv1.register_forward_hook(getActivation('conv1')). h2 = cnn.conv2.register_forward_hook(getActivation('conv2')). h3 = cnn.out.register_forward_hook(getActivation('out')). from torch.autograd import Variable. images, labels = next(iter(loaders['train'])). def single_run(images, labels, num_epochs, chromosome, fitness):. def train(num_epochs, cnn, loaders):. for i, (images, labels) in enumerate(loaders['train']):. b_x = Variable(images) # batch x. b_y = Variable(labels) # batch y. fitness, chromosome = single_run(images, labels,num_epochs, chromosome, fitness). I am also learning PyGAD. I recommend an example in PyGAD. I get to know how to use PyGAD for deep learning with the code.