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Evolutionary Algorithm
Usage of the evolutionary algorithm is defined by the type Evolution. The
basic configuration of the algorithm is done in config in the namespace of
http://ailab.ifi.uzh.ch/testframework/2008/algorithm/evolution/. Allowed attributes and their values are listed in table 4.2.
Attribute
changeprobability
distribution
generations
population
selection
parents
elitism
parameterscaling
Value
double
Explanation
Probability that a parameter changes
from one to another generation
gaussian Distribution of the changes to the parent’s values
integer
Number of generations.
integer
Size of one generation.
leaders, Parent selection algorithm.
roulette
integer
Number of parents (if ”leaders” is used)
on, off
Elitism guarantees that the best individual survives
double
Stretches the gaussian curve
Table 4.2: Settings for the evolutionary algorithm
Simulated Annealing
Usage of the simulated annealing algorithm is defined by the type Simulated
Annealing. The basic configuration of the algorithm is done in config in
the namespace http://ailab.ifi.uzh.ch/testframework/2008/algorithm/
simulatedannealing/. Allowed attributes and their values are listed in table
4.3.
Attribute
inittemperature
terminatetemperature
Value
double
double
bestfitness
cooling
double
double
boltzmannconstant
double
parameterscaling
double
Explanation
Init temperature
Temparature at which the algorithm
terminates
Maximal fitness value
Cooling constant (recommended eg.
0.95).
default = 1, a lower value increases the
probability to accept less good parameters
default = 1, increases probability for big
changes
Table 4.3: Settings for the simulated annealing algorithm
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