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. 1. INTRODUCTION The even-n-parity functions have long been recognised as difficult for Genetic Programming (GP) to induce if no bias favourable to their induction is introduced in the function set, the input representation, or in any other part of the algorithm. For this reason they are very interesting and have been widely used as benchmark tests [1, 4, 5, 6, 7, 23, 24, 26]. For an even-parity function of n Boolean inputs, the task is to evolve a function that returns 1 if an even number of the inputs evaluate to 1, 0 otherwise. The task seems to be difficult for at least two reasons...
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