1. C. Benatti, 3/15/2012, Slide 1 GA/ICA Workshop Carla Benatti 3/15/2012

2. C. Benatti, 3/15/2012, Slide 2 Proposed Thesis Project Tuning a Beam Line – 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 Benchmark code at ReA3 – Design experiment to test optimizer – Compare results with tuning “by hand” – User friendly application, possibly GUI L051L054L057L061LB006LB004 LB source, L-line at ReA3 COSY Envelope tracking calculation

3. C. Benatti, 3/15/2012, Slide 3 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) x1x1 x2x2 xNxN w1w1 w2w2 wNwN y y = Output w = Weights x = Inputs b = Threshold φ = Non-linear Function Neuron Input layer Hidden layer(s) Output layer x1x1 x2x2 xNxN 1 1 2 2 m k Neuron wNwN 3 Multilayer Perceptron Basic ANN example Hierarchical structure Feed-forward network Perceptron

4. C. Benatti, 3/15/2012, Slide 4 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 PopulationExpress ChromosomesEvaluate Fitness Iterate or Terminate? Keep Best ProgramsSelect ProgramsReplicationGenetic ModificationPrepare New Chromosomes of Next Generation Iterate Terminate End Reproduction http://www.ai-junkie.com/ann/evolved/nnt7.html (0.3, -0.8, -0.2, 0.6, 0.1, -0.1, 0.4, 0.5) Elitism (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) (0.7, 0.4, -0.9, 0.6, 0.1, -0.1, 0.4, 0.5) Parents Crossover Mutation (0.7, 0.4, -0.9, 0.6, 0.1, -0.3, 0.4, 0.5) Genetic Modification Examples

5. C. Benatti, 3/15/2012, Slide 5 SmartSweepers Tutorial Code NeuralNet.m NeuralNet_CalculateOutput.m Genetic_Algorithm.m http://www.ai-junkie.com/ann/ Best Fitness Average Fitness

6. C. Benatti, 3/15/2012, Slide 6 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