1. Stat methodes for Susy search Daniel August Stricker-Shaver Institut für Experimentelle Kernphysik, Uni Karlsruhe 24/05/2007

2. Genetic algorithm Is a algorithm that finds a solition for a non analytic problem Is a algorithm that finds a solition for a non analytic problem How do they do this? They chance possible solutions and combin them with each other until they solf the problem satisfying They chance possible solutions and combin them with each other until they solf the problem satisfying

3. 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

4. 1. Initialization Many individual solutions are randomly generated to form a population Many individual solutions are randomly generated to form a population It depends on the nature of the problem. Typically it contain serveral 100 or 1000 of possible solutions It depends on the nature of the problem. Typically it contain serveral 100 or 1000 of possible solutions

5. 2. Evaluation Every individual solution is measured by a fitness function and will get a value Every individual solution is measured by a fitness function and will get a value Individual solutions are selected where fitter solutions are typically more likely to be selected Individual solutions are selected where fitter solutions are typically more likely to be selected 3. Selection

6. 4. Recombination The genomes of diffrenr individuals are getting mixed and a new generation will be produced The genomes of diffrenr individuals are getting mixed and a new generation will be produced Randomly changing some parts of some individuals of the new generation Randomly changing some parts of some individuals of the new generation building new Genaration with 4 and 5 and going to step 2 until termination condition is reached 5. Mutation 6.

7. termination condition A solution is found A solution is found The fixed number of generations is reached The fixed number of generations is reached The highest ranking solution‘s fitness is reached The highest ranking solution‘s fitness is reached

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

9. Example: finding the Minimum 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 from -50 to 50 end: termination condition has been reached

10. TMVA Cut optimation Cut optimation fitness function: fitness function: quality of a retangular cut is given by good background rejection combiened quality of a retangular cut is given by good background rejection combiened with signal efficiency with signal efficiency

11. Decision trees What are boosted decision trees?