  Among the evolutionary methods, one that is quite prominent is Genetic
Programming, and, in recent years, a variant called Geometric Semantic Genetic
Programming (GSGP) has shown to be successfully applicable to many real-world
problems. Due to a peculiarity in its implementation, GSGP needs to store all
the evolutionary history, i.e., all populations from the first one. We exploit
this stored information to define a multi-generational selection scheme that is
able to use individuals from older populations. We show that a limited ability
to use "old" generations is actually useful for the search process, thus
showing a zero-cost way of improving the performances of GSGP.
