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Evolution of Learning Algorithms in Artificial Neural Networks: A Comprehensive Overview

This paper explores the advancements in learning algorithms for artificial neural networks, particularly focusing on Genetic Algorithms and Local Binary Neural Networks (LBNNs). It outlines a method for evolving neural networks through fitness evaluations and breeding high-performing candidates. By simplifying weights and functions into binary formats, LBNNs offer ease of encoding and integration into genetic algorithms. The paper further discusses training rules based on boolean functions, the efficiency of Hebb’s rule, and the structure of LBNNs as state machines tailored for boolean computations.

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Evolution of Learning Algorithms in Artificial Neural Networks: A Comprehensive Overview

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  1. The Evolution of Learning Algorithms for Artificial Neural Networks Published 1992 in Complex Systems byJonathan Baxter Michael Tauraso

  2. 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 • Breed the high-fitness ones to make a new population. • Repeat.

  3. 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

  4. An LBNN

  5. 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.

  6. Training Rules • Boolean functions of two variables • 16 possible varieties • Analog of Hebb’s rule given by:f(ai(t),aj(t)) = ai(t) aj(t)

  7. Training Goal • Learn the 4 boolean functions of one variable • Identity, Inverse, Always 1, Always 0 • Who wants to learn the boolean functions of one variable anyway?

  8. Fitness Determination • Start with an LBNN from the sample population • Clamp the output node to train for a particular boolean function. • Fitness is how well the network performs at calculating that boolean function after training.

  9. A Successful LBNN

  10. Findings • Hebb’s rule is the most efficient learning rule. • LBNNs can be thought of as state machines

  11. LBNNs as State Machines • Boolean functions are encoded as transitions between fixed points in the NN • Other transitions seek to push the network toward the appropriate fixed point.

  12. State Machine for an LBNN

  13. Questions ?

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