the evolution of learning algorithms for artificial neural networks
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The Evolution of Learning Algorithms for Artificial Neural Networks. Published 1992 in Complex Systems by Jonathan Baxter Michael Tauraso. Genetic Algorithm on NNs. Start with a population of neural networks. Find the fitness of each for a particular task Weed out the low-fitness ones - PowerPoint PPT PresentationTRANSCRIPT
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The Evolution of Learning Algorithms for Artificial
Neural Networks
Published 1992 in Complex Systems by Jonathan Baxter
Michael Tauraso
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Genetic Algorithm on NNsStart with a population of neural networks.Find the fitness of each for a particular taskWeed out the low-fitness onesBreed the high-fitness ones to make a new
population.
Repeat.
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Local Binary Neural Networks(LBNNs)
All weights, inputs, and outputs are binary.Learning rule is a localized boolean function of
two variables.This vastly simplifies everything.LBNNs are easy to encode into binary strings.LBNNs are easy to write into genetic
algorithms
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An LBNN
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Rules for LBNNs Weights are +1, -1, or 0 Nodes: ai(t+1) =sign( ∑ aj(t)wji(t) )
Weights: wij(t+1) = f(ai(t), aj(t))
Weights are classified as fixed or learnable. 0 weights are fixed.
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Training RulesBoolean functions of two variables16 possible varietiesAnalog of Hebb’s rule given by:
f(ai(t),aj(t)) = ai(t) aj(t)
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Training GoalLearn the 4 boolean functions of one variable Identity, Inverse, Always 1, Always 0Who wants to learn the boolean functions of one
variable anyway?
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Fitness DeterminationStart with an LBNN from the sample
populationClamp the output node to train for a
particular boolean function.Fitness is how well the network performs at
calculating that boolean function after training.
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A Successful LBNN
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FindingsHebb’s rule is the most efficient learning
rule.LBNNs can be thought of as state
machines
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LBNNs as State MachinesBoolean functions are encoded as transitions
between fixed points in the NNOther transitions seek to push the network
toward the appropriate fixed point.
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State Machine for an LBNN
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Questions
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