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

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Representation based typically on a list of discrete tokens, often bits (Genome) - can be extended to graphs, lists, realvalued vectors, etc. Select m parents probabilistically based on fitness Create m (or 2m) new children using genetic operators (emphasis on crossover) and assign them a fitness - singlepoint, multi-point, and uniform crossover Replace m weakest candidates in the population with the new children (or can always delete parents) CS 478 - Evolutionary Algorithms 18
Representation that best fits problem domain All n parents mutated (no crossover) to create n new children - total of 2n candidates Only n most fit candidates are kept Mutation schemes fit representation, varies for each variable, amount of mutation (typically higher probability for smaller mutation), and can be adaptive (i.e. can decrease amount of mutation for candidates with higher fitness and based on time - form of simulated annealing) CS 478 - Evolutionary Algorithms 19
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: There exist mathematical proofs tha
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

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

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