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Stat methodes for Susy
search
Daniel August Stricker-Shaver
Institut für Experimentelle Kernphysik, Uni Karlsruhe
24/05/2007
Genetic algorithm

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
Genetic algorithm
1
2
3
4
5
6
Initialization
Evaluation
Selection
Recombination
Mutation
building new Genaration with 4
and 5 and going to step 2 until
termination condition is reached
1. Initialization


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
2. Evaluation

Every individual solution is measured
by a fitness function and will get a
value
3. Selection

Individual solutions are selected
where fitter solutions are typically
more likely to be selected
4. Recombination

The genomes of diffrenr individuals
are getting mixed and a new
generation will be produced
5. Mutation

Randomly changing some parts of
some individuals of the new
generation
6.

building new Genaration with 4
and 5 and going to step 2 until
termination condition is reached
termination condition



A solution is found
The fixed number of generations is
reached
The highest ranking solution‘s fitness
is reached
Genetic algorithm


Advantage:
GA are the fastest evolutionary
algorithms
Disadvantages:
You never know if is the Optimium of
the fitness function
Genetic algorithm
Example: finding the Minimum
Start with maybe 50 individual and a radom for every Genom from -50 to 50
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)
end: termination condition has been reached
Results:
(4,4,4,4,4) or ( − 21, − 21, − 21, − 21, − 21)
TMVA

Cut optimation
fitness function:
quality of a retangular cut is given by
good background rejection combiened
with signal efficiency
Decision trees
What are boosted
decision trees?
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