1. Stat methodes for Susy search Daniel August Stricker-Shaver Institut für Experimentelle Kernphysik, Uni Karlsruhe 10/05/2007

2. Genetic algorithm 1 Initialization 2 Evaluation 3 Selection 4 Recombination 5 Mutation 6 building new Genaration with 4 and 5 and going to step 2 until termination condition is reached

3. Genetic algorithm Advantage: Advantage: GA are the fastest evolutionary algorithms Disadvantage: Disadvantage: You never know if is the Optimium of the fitness function You never know if is the Optimium of the fitness function

4. Example Genetic algorithm Recombination: G0 = (18, − 3,5,9,8) and G1 = (14,13,33,2,15) => Gc = (18, − 3,33,2,15) Mutation with a posibility of maybe 1% for every change of generation and position m = (1,0,-1) of position =( a, b,c,d,e) Results: (4,4,4,4,4) or ( − 21, − 21, − 21, − 21, − 21) Start with maybe 50 individual and a radom for every Genom rom -50 to 50 end: termination condition has been reached

5. Decision trees What are boosted decision trees?

6. discriminating function non-linear separable non-linear separable linear separable linear separable

7. Fisher discriminants N dim projection on an axis in the hyperspace N dim projection on an axis in the hyperspace Push as far as possible from each other Push as far as possible from each other Event of same class confined in a close vicinity Event of same class confined in a close vicinity

8. Chi^2-Test The chi^2-distribution, with n degrees of freedom.

9. Artificial Neural Networks Artificial Neural Networks Weights, threshold value, propagation function, activation function, output function, online/offline lerning Weights, threshold value, propagation function, activation function, output function, online/offline lerning