Failed disruption propagation in integer genetic programming

We inject a random value into the evaluation of highly evolved deep integer GP trees 9 743 720 times and find 99.7% of test outputs are unchanged. Suggesting crossover and mutation's impact are dissipated and seldom propagate outside the program. Indeed only errors near the root node have impact and disruption falls exponentially with depth at between e-depth/3 and e-depth/5 for recursive Fibonacci GP trees, allowing five to seven levels of nesting between the runtime perturbation and an optimal test oracle for it to detect most errors. Information theory explains this locally flat fitness landscape is due to FDP. Overflow is not important and instead, integer GP, like deep symbolic regression floating point GP and software in general, is not fragile, is robust, is not chaotic and suffers little from Lorenz' butterfly