this tightens the screws some )( on the prior findings and show they generalize nicely. prior analysis seems very solid but there is always the shadow of question of bias in the generation algorithms which start from small seed sizes and build larger ones out of smaller ones. an entirely different strategy for generating sample seeds is used here based on a genetic algorithm. the idea is to start with a fixed bit width and alter the bits in it just like “genes”. fitness is based on glide length (or equivalently ‘h2’ for a constant seed width). it starts with 20 random parents of given length. there is 1 mutation operator and 2 crossover operators. 1 crossover operator selects adjacent/ contiguous bits from parents at a random cutoff/ crossover point (left to right) and the other just selects bits randomly from parents not wrt adjacent/ contiguous position.
fit24 is again used for the linear regression fit. these runs are for 50, 80, 100 bit sizes and ~200 points generated for each. 50K iterations.
because of declining # of points for higher widths, this is circumstantial evidence that as widths increase long glides (relative to width, ie ‘h2’) are (1) increasingly hard to find and/ or (2) rare. these two aspects interrelate but are not nec exactly equivalent. hardness of finding seeds with certain qualities ie computational expense does not nec mean theyre rare. an example might be RSA algorithm. finding twin primes is somewhat computationally expensive (although still in P) but theyre not so rare. technically/ theoretically rareness and computational expense are related through probabilistic search aka “randomized algorithms”.