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One idea stolen from Linear Optimisation is duality
Applying this to Symbolic Regression, one can image a setup where at generation G:
best individuals in are picked, for further offsping (traditional definition of genetic algos)
worst indiduals are picked, and most common subepxressions are extracted. While those badly performing expressions are used to form a next generation (maximizing the loss), the most common subepxressions are blacklisted from the mating process of the best performing ones.
The text was updated successfully, but these errors were encountered:
One idea stolen from Linear Optimisation is duality
Applying this to Symbolic Regression, one can image a setup where at generation G:
The text was updated successfully, but these errors were encountered: