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How to train a neural network with genetic algorithm and back propagation?

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Hello I want to train my neural network using a hybrid model of backpropagation and Genetic algorithm . Is it possible to use the two ona neural network for extremely high performance and also better results in less amount of time? Are there any such model available in MATLAB?

Accepted Answer

Greg Heath
Greg Heath on 10 Jul 2016
I have never found an efficient use of GA for training a fixed topology NN. The only successful adaptive topology NNs I have designed had a single hidden layer with a variable number of elliptical or radial basis functions. However, they were not designed using GA.
I have posted a fixed topology tansig GA design recently
However, the design was more illustrative than useful.
My point of view is that GAs probably excel when the net topology is more complex than the MATLAB feedforward and feedback defaults. In particular, when both number of layers, nodes and connections are variable.
If there is an efficient way to combine GA and backprop I am not familiar with it. (Which doesn't necessarily mean that it doesn't exist).
Good Luck.
PS If you find a good reference, PLEASE let us know.
Thanks in advance,
Greg Heath
Greg Heath on 19 Aug 2016
Sorry, I will not have the time to spend on this right now.
If you just want to find a non-optimal, but good, single-hidden layer model, my double loop search over number of hidden nodes (outer loop) and random number states (inner loop) which yields random trn/val/tst datadivisions and random initial weights has withstood the ravages of time.
I have posted zillions of examples in both the NEWSGROUP and ANSWERS. try searching on the combination
Hmin:dH:Hmax Ntrials
Hope this helps.
PS I have posted a genetic approach recently, however it is not as good as the double loop search.

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