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GA/ICA Workshop Carla Benatti 3/15/2012 C. Benatti, 3/15/2012, Slide 1 Proposed Thesis Project • Tuning a Beam Line LB source, L-line at ReA3 – Model/design of system provides nominal values for tune – Operators adjust each element individually to optimize tune – Slow process, room for improvement • Tuning Algorithm and Optimizer – Develop new, fast, tuning algorithm – Using neural networks, genetic algorithms possibly – Model Independent Analysis COSY Envelope tracking calculation • Benchmark code at ReA3 – Design experiment to test optimizer – Compare results with tuning “by hand” – User friendly application, possibly GUI LB004 LB006 L051 L054 L057 L061 C. Benatti, 3/15/2012, Slide 2 Artificial Neural Network (ANN) • Neural Network Summary – – – – – • Attempts to simulate the functionality of the brain in a mathematical model Ideal for modeling complex relationships between inputs and outputs as a “black box” solver Ability to learn, discern patterns, model nonlinear data Reliability of prediction Many different models already developed for finding local and global minimum for optimization Neural Network Programming – – – – Neuron receives weighted input If above threshold, generates output through nonlinear function Connecting single neurons together creates a neural network Learning, training: get ANN to give a desired output, supervised or unsupervised learning (GA example) Perceptron x1 w1 x2 w2 y = Output w = Weights x = Inputs b = Threshold φ = Non-linear Function y wN Neuron N y = φ(∑wi xi _ b) xN i =1 Multilayer Perceptron Hidden layer(s) Input layer 1 Output layer 1 x1 2 2 x2 3 k xN wN m • • • Basic ANN example Hierarchical structure Feed-forward network Neuron C. Benatti, 3/15/2012, Slide 3 Genetic Algorithms • • • • Machine learning technique Effective tool to deal with complex problems by evolving creative and competitive solutions Genetic Algorithms search for the optimal set of weights, thresholds for neurons Crossover is the most used search operator in Genetic Programming Create Chromosomes of Initial Population Express Chromosomes (0.3, -0.8, -0.2, 0.6, 0.1, -0.1, 0.4, 0.5) Evaluate Fitness Terminate Iterate or Terminate? End Iterate Elitism Keep Best Programs Genetic Modification Examples Select Programs Replication Parents (0.3, -0.8, -0.2, 0.6, 0.1, -0.1, 0.4, 0.5) (0.7, 0.4, -0.9, 0.3, -0.2, 0.5, -0.4, 0.1) Crossover (0.7, 0.4, -0.9, 0.6, 0.1, -0.1, 0.4, 0.5) Reproduction Mutation Genetic Modification (0.7, 0.4, -0.9, 0.6, 0.1, -0.3, 0.4, 0.5) Prepare New Chromosomes of Next Generation http://www.ai-junkie.com/ann/evolved/nnt7.html C. Benatti, 3/15/2012, Slide 4 SmartSweepers Tutorial Code • NeuralNet.m • NeuralNet_CalculateOutput.m • Genetic_Algorithm.m Best Fitness Average Fitness http://www.ai-junkie.com/ann/ C. Benatti, 3/15/2012, Slide 5 http://www.ai-junkie.com/index.html • Good source for first time learning about genetic algorithms and neural networks • Explains concepts in “plain English” • Goes through some coding examples to play with crossover/mutation parameters C. Benatti, 3/15/2012, Slide 6