Symbolic regression - Using a function as input/starting point

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Jun 27, 2021, 7:50:35 AM6/27/21
to HeuristicLab
Good morning everyone.
I have been searching for quite a long time now but I haven't found a solution yet:
Is there a way one can use a given function as an input of a symbolic regression problem?

For instance, if I had obtained a solution from a previous run of symbolic regression, and I wanted to further improve the function by starting another symbolic regression run from that solution, how could I do that?

Thanks in advance!

Gabriel Kronberger

Jun 28, 2021, 2:30:27 AM6/28/21
to noreply-spamdigest via HeuristicLab
Hello Vincenzo,

one option that would be easy to try is to calculate the residuals (y - f(x)) from the first pass and then use the residuals as the target. This way you could try to build up an additive model y = f1(x)  + f2(x) + ... .  This is essentially the boosting idea (see e.g. [1])

The GP literature describes some ideas how a population might be seeded using information gathered from earlier runs. However, to test such ideas in HeuristicLab you would need to look into implementing your own tree creation / population initialization routine.

Best, Gabriel

[1] M. Sipper and J. H. Moore, Symbolic-regression boosting, Genetic Programming and Evolvable Machines, 2021.
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Jun 28, 2021, 12:40:11 PM6/28/21
Thank you so much Gabriel

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