my qustion about using genetic algorithm for already trained ANN
i have developed ANN for actual system, i have the input patterns and
Target patterns, if i need one target i forward the ANN with iterative
inputs and check the ANN output for the required target. i need to
implement genetic algorithm to select the proper input combination to
achieve the required target quickly without too much iterations, can
Genetic algorithm do that?
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Probably not without “too much iterations”. It takes a lot of
generations and a pretty large population to achieve anything usable.
How many is “many iterations” in your case? What kind of population
If you have specific input and output patterns for all cases available
you might want to try using backpropagation. Training the network
genetically gives good results if you don’t have specific input and
output patterns but if you actually simulate the agent and its
surroundings using the network and afterwards assess the quality of the
agent within the given settings using rather abstract assessment.
So if you need an agent that behaves well in a certain simulateable
environment then genetic training might be the way to go. If you want
specific “answer” signal patterns given a specific input pattern (for
example training the network to do a xor) then other methods might be
The easiest way is to simply analyze the network. What inputs from the
last hidden layer will produce the desired output? Then what inputs to
the next layer will produce the values in that layer. You keep doing
this until you hit the input layer. Then find any solution to your
system of equations and you have it solved :) Note for the general
equation there are likely very many solutions, but you have to restrict
it to only allow values between 0..1