  The $(1+(\lambda,\lambda))$ genetic algorithm is one of the few algorithms
for which a super-constant speed-up through the use of crossover could be
proven. So far, this algorithm has been used with parameters based also on
intuitive considerations. In this work, we rigorously regard the whole
parameter space and show that the asymptotic time complexity proven by Doerr
and Doerr (GECCO 2015) for the intuitive choice is best possible among all
settings for population size, mutation probability, and crossover bias.
