Opened 6 years ago

Last modified 4 years ago

#1386 new enhancement

Mutation of constant factors should not simply add a normally distributed value but a value sampled from a mixture of Gaussians

Reported by: gkronber Owned by: gkronber
Priority: low Milestone: HeuristicLab 3.3.x Backlog
Component: Problems.DataAnalysis Version: 3.3.2
Keywords: Cc: maffenze

Description

Now mutation of constants and variable weights is:

f = f + x
x ~ N(0,sigma),

where sigma is a parameter supplied by the user.

This should be changed as suggested by maffenze to the following more complex adaption routine that is arguably more robust.

f = f + y
y ~ N(0,s)
s ~ U(0, s_max),

where s_max is a parameter supplied by the used. For variables s_max should automatically set to:

 s_max = s_user * Var(x_i),

Where s_user is a user supplied parameter and Var(x_i) is the variance of variable x_i over the observations in the training set.

Change History (2)

comment:1 Changed 6 years ago by swagner

  • Milestone changed from HeuristicLab 3.3.3 to HeuristicLab x.x.x

comment:2 Changed 4 years ago by gkronber

  • Priority changed from medium to low
  • Summary changed from Mutation of constant factors should not simply add a random normally distributed variable but a random variable that is a mixture of normally distributed variables to Mutation of constant factors should not simply add a normally distributed value but a value sampled from a mixture of Gaussians
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