Hybrid Artificial Neural Network with Genetic Algorithm

Optimization of neural network weights and biases using real genetic algorithm

Hybrid Artificial Neural Network with Genetic Algorithm

The idea here is to employ the Genetic algorithm to optimize ANN parameters to improve performance. ANN provides the search space and utilizes GA to find the best solution by tuning the weights and biases required to achieve lower error rates. The error between the model output and the exact training data can reach a minimum value by iterating the GA until the desired error is met.

The code is written in such a way that it only needs to change the Excel name in CreateData.m to apply it to any data set:

f=readmatrix('DATA(cylindrical)')

In addition, during the implementation of the code, the parts of the program that require customization by researchers to get the best results from the code are asked in the form of "questdlg".

Researchers can also email the following address for article cooperation in optimization algorithms, various types of neural networks, fuzzy logic, and machine learning.

Email: Eng.mehdighasri@gmail