Abstract
We propose and study new search operators and a novel
node representation that can make GP fitness landscapes
smoother. Together with a tree evaluation method known
as sub-machine-code GP and the use of demes, these make
up a recipe for solving very large parity problems
using GP. We tested this recipe on parity problems with
up to 22 input variables, solving them with a very high
success probability.
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