Niko Adrianus Yuwono
Niko Adrianus Yuwono

Reputation: 11122

Genetic Algorithm and Neural Network

Right now I'm doing a research about Genetic Algorithm and Neural Network, I want to use the Genetic Algorithm to train the Neural Network and use the Neural Network to solve OCR problem, what I'm still don't understand is the training method, let's say I have 5 training set each set have 26 character (A-Z), should I do the training from A1-Z1 to A5-Z5 (per set) or A1-A5 to Z1-Z5 (per character) and how many generations should be generated per character?or until the error is minimum?

I think that's all I want to ask right now

if is there anything unclear about my question please tell me

Thank You

Upvotes: 1

Views: 1872

Answers (3)

Todor Balabanov
Todor Balabanov

Reputation: 388

It will be very slowly to train ANN with GA. Maybe you should think for some hybrid approach. You will need to do a lot of image preprocessing before to feed data in the ANN. Also you will need to design your ANN very carefully. You should think about the size of input and the size of output. With GA you can optimize two things: 1. ANN topology or 2. ANN weights.

Upvotes: 0

Diemisom Melo
Diemisom Melo

Reputation: 11

Actually, you really can develop a neural network for each letter. Just use Autoassociative Networks (Autoencoders). These networks are trained so that the output reproduces the input. That way you would train 26 networks. At validation time, for each letter, the network that obtains the lowest error is the network that represents the letter. This approach has achieved excellent results for classification problems.

Upvotes: 1

Mehraban
Mehraban

Reputation: 3324

A neural network with 26 outputs is odd. You should build a network for each letter, but let it see all letters during training so it could tell you when it sees intended letter. For number of generations typically you should meet one of two conditions: Error becomes lower then a threshold or number of generations exceed a limit. It depends on you to choose these params.

Upvotes: 0

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