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

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Each classifier bids for use at time t. Bid used as a probability (non-linear) of being a winner - assures lower bids get some chances B(C,t) = bR(C)s(C,t) where b is a constant
s(C,t+1) = s(C,t) -B(C,t) - Loss of Strength for each consumer (price paid to be used, prediction of how good it will pay off) {C'} = {suppliers} - Each of the suppliers shares an equal portion of strength increase proportional to the amount of the Bid of each of the consumers in the next time step s(C',t+1) = s(C',t) + B(C i ,t)/|C'| You pay suppliers amount of bid, receive bid amounts from consumers. If consumers profitable (higher bids than you bid) your strength increases. Final rules in chain receive actual payoffs and these eventually propagate iteratively. Consistently winning rules give good payoffs which increases strength of rule chain, while low payoffs do opposite. CS 478 - Evolutionary Algorithms 29
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 and 26: Much current work and extensions Nu
Page 27: Each classifier has an associated s

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

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