Аннотация:This study compares several modifications of a gender genetic algorithm (GGA). Aside the difference between the genders in the probability of mutation, we introduce two additional modifications: different implementations of selection and different laws of dependence of the probability of mutation on gene number within a chromosome. We use four test optimization problems in spaces of various dimensions to compare conventional GA, conventional GGA, and GGA with the additional modifications implemented separately or together. It is demonstrated that the proposed additional modifications outperform conventional GA and conventional GGA in the achieved value of the fitness function, especially in high-dimensional spaces. With increase in the problem dimension, they degrade more slowly. Also, the new modifications prevent premature convergence of the algorithm.