# genetic_algorithm_neural_network

**Repository Path**: icze1i0n/genetic_algorithm_neural_network

## Basic Information

- **Project Name**: genetic_algorithm_neural_network
- **Description**: genetic algorithm neural network
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-04
- **Last Updated**: 2021-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Neural Network with Genetic Algorithm Optimizer

For training a neural-network, we have an awesome *back-propagation algorithm* that allows us automatically tweak our weights and biases to fit our dataset. This is just a project came out of curiosity -- to test for another method that is possible to tweak the network without depending on any model-based algorithm.

To be honest, back-propagation is still the winning choice here. This is just a proof-of-concept project, which has proved that randomness of genetic algorithm is still possible to let the network learn, albeit *very very slow learning*.

Please be noted, for large dimension of data (eq: mnist/cifar-10), back-progapation wins the competition by tenfolds. That is why for this project I chose to use Iris dataset, as it is small enough for me to conduct an experiment.

## How to run?

1) Install dependencies

`pip install -U scikit-learn numpy pandas`

2) Run the project

`python neural-network-genetic-algorithm.py`

## Screenshot

![Training Image](image.png)