Schema maps to planes in hypercube...
plane 0 * * ...
2 planes 0 * * ... and * * 1 ...
becomes edge 0 * 1 ....
under two-point crossover this schema has two bits that are "close together" hence has less disruption. two-point cross can be visualize as "ring". From this point of view one-point is more disruptive. (one-point becomes having one location fixed at the joint of the ring).
The ability of GA to search effectively comes from its "Implicit parallelism", i.e. one schema searches many hyperplanes. GA focuses the search effort to the area of high prob. to find solution. It spends more sampling near the plane where the highly fit individual located. GA searches without "gradient" information.
j = 0
partsum = 0
r = rand * sumfitness // sumfitnes is the sum
of population fitness
repeat
j = j+1
partsum = partsum + pop[j].fit
until (partsum > r ) or ( j = popsize )
return j