C. Benatti, 3/15/2012, Slide 1

GA/ICA Workshop

Carla Benatti3/15/2012

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

L051 L054 L057 L061LB006LB004

LB source, L-line at ReA3

COSY Envelope tracking calculation

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)

x1

x2

xN

w1

w2

wN

y

∑1=

_ )(=N

iii bxwφy

y = Outputw = Weightsx = Inputsb = Thresholdφ = Non-linear Function

Neuron

Input layerHidden layer(s)

Output layerx1

x2

xN

11

22

m

k

NeuronwN

3

Multilayer Perceptron

• Basic ANN example

• Hierarchical structure

• Feed-forward network

Perceptron

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

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

C. Benatti, 3/15/2012, Slide 5

SmartSweepers Tutorial Code

• NeuralNet.m• NeuralNet_CalculateOutput.m• Genetic_Algorithm.m

http://www.ai-junkie.com/ann/

smart sweepers.exe

Best Fitness

Average Fitness

smart sweepers.exe

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