How to use genetic algorithm for variable selection for optimizing Neural Network used for regression model?

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Paidi Dinesh

Genetic algorithms can be used to optimize the parameters of an artificial neural network (ANN). Here's a general framework for using a genetic algorithm to optimize an ANN:

Define the problem: Clearly define the problem you want to solve with the ANN, including the input and output data, as well as the performance metrics you want to optimize.

Define the architecture of the ANN: Choose the number of layers, number of nodes per layer, activation functions, and other hyperparameters that define the structure of the ANN.

Encode the ANN: Create a genetic representation of the ANN, which could be a string of binary or real numbers that represent the weights and biases of the network.

Generate an initial population: Create a population of random ANN architectures that conform to the structure you defined in step 2.

Evaluate the fitness of each individual: Calculate the fitness of each individual in the population based on its performance on the problem defined in step 1.

Select parents: Choose individuals from the population to be parents of the next generation. This is typically done through a selection method such as roulette wheel selection, tournament selection, or rank-based selection.

Crossover: Combine the genetic material of the selected parents to create new individuals for the next generation. This can be done using crossover, which combines the genetic material of two parents to create one or more offspring.

Mutation: Introduce random changes in the genetic material of the new individuals to increase the diversity of the population.

Repeat: Repeat steps 5-8 until a stopping criterion is met, such as a maximum number of generations or an acceptable level of fitness.

Decode the best individual: Once the genetic algorithm has converged to a good solution, decode the best individual to obtain the weights and biases of the optimized ANN.

Test the optimized ANN: Test the optimized ANN on a separate validation dataset to confirm that it performs well on new data.

The use of genetic algorithm for optimizing ANN involves defining the problem, defining the architecture of the ANN, encoding the ANN, generating an initial population, evaluating the fitness of each individual, selecting parents, applying crossover and mutation, and repeating the process until a good solution is found. Finally, the best individual is decoded to obtain the optimized ANN weights and biases, which can then be tested on a separate validation dataset.

Shafagat Mahmudova

Dear Atanu Kumar Panja ,

The goal is to solve a diabetes classification problem using an Artificial Neural Network (ANN) optimized by a Genetic Algorithm, discovering the performance difference of different parameters of the ANN model and comparing this training method with additional optimizers like stochastic gradient descent, RMSprop, and Adam optimizer.

https://medium.com/swlh/genetic-algorithm-in-artificial-neural-network-5f5b9c9467d0

Regards,

Shafagat

Amr Elsawy

check here

https://towardsdatascience.com/artificial-neural-networks-optimization-using-genetic-algorithm-with-python-1fe8ed17733e

Shima Shafiee

Hello

Dear Atanu Panja

First, specify the role of the neural network, for example, if it is classified, then the genetic algorithm can be used as a separate phase and as feature selection.

Or the genetic algorithm can be used as an optimizer inside the neural network or adjust the weights of the network

Your question is a bit vague

https://www.researchgate.net/publication/369539251_Overview_of_Neural_network_and_Genetic_algorithm