  A general approach to optimizing fast processes using a gender genetic
algorithm is described. Its difference from the more traditional genetic
algorithm it contains division the artificial population into two sexes. Male
subpopulations undergo large mutations and more strong selection compared to
female individuals from another subset. This separation allows combining the
rapid adaptability of the entire population to changes due to the variation of
the male subpopulation with fixation of adaptability in the female part. The
advantage of the effect of additional individual learning in the form of
Boldwin effect in finding optimal solutions is observed in comparison with the
usual gender genetic algorithm. As a promising application of the gender
genetic algorithm with the Boldwin effect, the dynamics of extinguishing
natural fires is pointed.
