Fitness First.

With side effect free terminals and functions it is possible to evaluate the fitness of genetic programming trees from their parents without creating them. This allows selection before forming the next generation. Thus avoiding unfit runt Genetic Algorithm individuals, which will themselves have no children. In highly diverse GA populations with strong selection, more than 50% of children need not be created. Even with two parent crossover, in converged populations, $$e^{-2}$$ = 13.5% can be saved. Eliminating bachelors and spinsters and extracting the smaller genetic material of each mating before crossover, reduces storage in an N multi-threaded implementation for a population M to $$\le $$ 0.63M+N, compared to the usual M+2N. Memory efficient crossover achieves 692 billion GP operations per second, 692 giga GPops, at runtime on a 16 core 3.8 GHz desktop.