CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...
CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ... CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...
Use a Genetic Algorithm to learn the weights of an MLP. Used to be a lab. You could represent each weight with m (e.g. 10) bits (Binary or Gray encoding), remember the bias weights Could also represent Neural Network Weights as real values - In this case use Gaussian style mutation Walk through an example comparing both representations CS 478 - Evolutionary Algorithms 24
Much current work and extensions Numerous application attempts. Can plug into many algorithms requiring search. Has built-in heuristic. Could augment with domain heuristics. If no better way, can always try evolutionary algorithms, with pretty good results ("Lazy man’s solution" to any problem) Many different options and combinations of approaches, parameters, etc. Swarm Intelligence – Particle Swarm Optimization, Ant colonies, Artificial bees, Robot flocking, etc. More work needed regarding adaptivity of – population size – selection mechanisms – operators – representation CS 478 - Evolutionary Algorithms 25
- Page 1 and 2: CS 478 - Evolutionary Algorithms 1
- Page 3 and 4: Populate our search space with init
- Page 5 and 6: Procedure EA t = 0; Initialize Popu
- Page 7 and 8: CS 478 - Evolutionary Algorithms 7
- Page 9 and 10: Individuals are represented so that
- Page 11 and 12: CS 478 - Evolutionary Algorithms 11
- Page 13 and 14: In general want the fittest parents
- Page 15 and 16: Population size - Larger gives more
- Page 17 and 18: There exist mathematical proofs tha
- Page 19 and 20: Representation that best fits probl
- Page 21 and 22: Evolves more complex structures - p
- Page 23: Use a Genetic Algorithm to learn th
- Page 27 and 28: Each classifier has an associated s
- Page 29: s(C,t+1) = s(C,t) -B(C,t) - Loss of
Much current work <strong>and</strong> extensions<br />
Numerous application attempts. Can plug into many algorithms requiring<br />
search. Has built-in heuristic. Could augment with domain heuristics.<br />
If no better way, can always try evolutionary algorithms, with pretty good<br />
results ("Lazy man’s solution" to any problem)<br />
Many different options <strong>and</strong> combinations of approaches, parameters, etc.<br />
Swarm Intelligence – Particle Swarm Optimization, Ant colonies,<br />
Artificial bees, Robot flocking, etc.<br />
More work needed regarding adaptivity of<br />
– population size<br />
– selection mechanisms<br />
– operators<br />
– representation<br />
<strong>CS</strong> <strong>478</strong> - <strong>Evolutionary</strong> <strong>Algorithms</strong> 25
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