Opened 6 years ago

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

Reported by: Owned by: gkronber gkronber low HeuristicLab 3.3.x Backlog Problems.DataAnalysis 3.3.2 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.

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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