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## PowerPoint Slideshow about ' Genetic Programming and Artificial Neural Networks' - zaide

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Presentation Transcript

Overview

- Artificial Neural Network Review
- The First to Try It
- Developmental Approaches
- Good Ideas Always Come Back
- Comparison to Other Evolutionary ANN techniques

Artificial Neural Networks (ANNs)

- Structure of neurons and links
- Neuron has summation of links in, activation function, and output
- Take inputs and give outputs
- Learns by changing the link weights that the inputs are passed through
- Hidden layers between the input and output layers are necessary to handle non-linearly separable problems

The Problem with ANNs

- Wide variety of problems leads to wide variety of network configurations
- No good way to know what network configuration to use
- Trial and error to find good configuration is a lengthy process
- Never sure if you have the optimal network setup
- What is the better way?
- GA was used to some success but a new player had just arrived on the AI scene

GP and ANNs

- Surprise, surprise, Koza is one of the first to try it
- Used a direct representation of an ANN in GP language form
- Tree would organize itself into a single root tree that could be seen as a single output arbitrary shape ANN
- No regular layering of the tree
- Nodes could be anywhere in his tree and there was no guaranteed organized structure of layers except for the output layer

Koza’s ANN GP Language

- F = {P,W,+,-,*,/}
- P is the linear activation function of a neuron
- W multiplies all the branches coming into itself
- Restrictions
- Root of the tree must be P
- All branches coming into a P must be Ws
- Any subtree below and arithmetic operation must only contain more arithmetic operations or terminals
- LIST function could be used to give multiple outputs
- Only used as root and could only have P branches coming into it
- T= {D0,D1,R}
- Two inputs and ephemeral random constant

The XOR GP Tree

- When Koza applied this to XOR, this tree result work 100% of the time
- Translated to a 2-2-1 network
- Easy problem = easy tree
- What about a more complex problem?

Koza’s Full Adder Con’t

- This full adder tree performed at 100% as well
- Over 102 runs of popsize = 500, a 100% solution was found 86% of the time after 51 generations
- Koza’s idea seemed to have promise and as Koza’s ideas typically do, GP for ANN snowballed

Taking a Developmental Approach

- Frederic Gruau used developmental GP performing operations on nodes of a ANN to create new ones
- This lead to highly connected graphs and there were few ways to change the individual edges
- Sean Luke and Lee Spector took Gruau’s idea but applied it to edges instead of nodes
- This lead to less connected graphs that were able to change edges and nodes easier
- Unfortunately Gruau’s report was inaccessible behind a pay wall and Luke and Spector only released a preliminary report so no data on how well these really performed was available

Ideas Come Full Circle

- D. Rivero et al. upon reading at the Gruau and Luke and Spector papers thought they had a better way
- Have a language where there is a function to represent a neuron, a function to represent an input neuron, and a function to act as the tree root that takes in all the output neurons and lists them
- Sounds familiar, doesn’t it?

Koza’s GPANN v2

- Koza’s paper was never referenced in Rivero et al, 2005
- Likely they came upon the idea organically just like Kozadid
- While Koza’s tests for his GPANN could be considered toyish, it was tested much more rigorously this time
- While not explicitly stated their activation function was likely more complex than the linear one Koza used
- Attempted 4 different problems from the UCI database

Their Results

- D. Rivero at al. didn’t compare themselves to Gruau, Luke, and Spector but did compare their ANN to the results found by Cantú-Paz and Kamath in a paper that compared many different ANN with evolutionary algorithms
- Their method on these problems performed at least as good and better in most cases
- All other algorithms had individual evaluation separate from the design process
- The combination of these by Rivero et al. led to shorter training time and much less computational power used

References

- Koza, John R., and James P. Rice. "Genetic generation of both the weights and architecture for a neural network." In Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on, vol. 2, pp. 397-404. IEEE, 1991.
- F. Gruau, “Genetic micro programming of neural networks”, in Kinnear, Jr., K. E., editor, Advances in Genetic Programming, chapter 24, MIT Press, 1994. pp. 495–518,
- Luke, Sean, and Lee Spector. "Evolving graphs and networks with edge encoding: Preliminary report." In Late Breaking Papers at the Genetic Programming 1996 Conference, pp. 117-124. Stanford, CA: Stanford University, 1996.
- Rivero, Daniel, Julián Dorado, Juan R. Rabuñal, Alejandro Pazos, and Javier Pereira. "Artificial neural network development by means of genetic programming with graph codification." Transactions on Engineering, Computing and Technology 16 (2006): 209-214.

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