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|
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