Can we use genetic algorithms to improve the performance of neural networks?

Can we use genetic algorithms to improve the performance of neural networks?

The study results showed that the genetic algorithm obtained a good result of AUC of on modeling data and on testing data for small range of initial parameter. Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.

What could be done to improve the neural networks performance?

Now we’ll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:

  • Increase hidden Layers.
  • Change Activation function.
  • Change Activation function in Output layer.
  • Increase number of neurons.
  • Weight initialization.
  • More data.
  • Normalizing/Scaling data.

What are Genetic Algorithms good for?

Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection.

How do neural networks reduce losses?

Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer)

Are there genetic algorithms to enhance neural networks?

Anyone having substantial experience with neural networks is well aware of the difficulty in choosing the right hyperparameters for the problem at hand, and also a large amount of time it may take for a network to learn features. But not all of these people have experimented with a potent solution — Genetic Algorithms (GA).

How are genetic algorithms used in deep learning?

A research article in the Journal of Electrical and Computer Engineering (Hindawi), An Experiment on the Use of Genetic Algorithms for Topology Selection in Deep Learning, focussed on the performance of genetic algorithms with neural networks on the MNIST and the CIFAR-10 datasets using Caffe.

How are genetic algorithms used to optimize agents?

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. What are genetic algorithms?

How are genetic algorithms inspired by natural selection?

GAs according to Wikipedia: In computer science and operations research, a genetic algorithm ( GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA).