CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...

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Reinforcement Learning - sparse payoff Contains rules which can be executed and given a fitness (Credit Assignment) - Booker uses bucket brigade scheme GA used to discover improved rules Classifier made up of input side (conjunction of features allowing don’t cares), and an output message (includes internal state message and output information) Simple representation aids in more flexible adaptation schemes CS 478 - Evolutionary Algorithms 26
Each classifier has an associated strength. When matched the strength is used to competitively bid to be able to put message on list during next time step. Highest bidders put messages. Keeps a message list with values from both input and previously matched rules - matched rules set outputs and put messages on list - allows internal chaining of rules - all messages changed each time step. Output message conflict resolved through competition (i.e. strengths of classifiers proposing a particular output are summed, highest used) CS 478 - Evolutionary Algorithms 27
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 9 and 10: Individuals are represented so that
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 and 24: Use a Genetic Algorithm to learn th
Page 25: Much current work and extensions Nu
Page 29: s(C,t+1) = s(C,t) -B(C,t) - Loss of

CS 478 - Evolutionary Algorithms 1 - Neural Networks and Machine ...

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