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 ...
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23.11.2014
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Crossover variations - multi-point, uniform, averaging, etc. Mutation - Random changes in features, adaptive, different for each feature, etc. Others - many schemes mimicking natural genetics: dominance, selective mating, inversion, reordering, speciation, knowledge-based, etc. CS 478 - Evolutionary Algorithms 10
CS 478 - Evolutionary Algorithms 11
- 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: 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 and 28: Each classifier has an associated s
- Page 29: s(C,t+1) = s(C,t) -B(C,t) - Loss of
<strong>CS</strong> <strong>478</strong> - <strong>Evolutionary</strong> <strong>Algorithms</strong> 11
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