Genetic algorithm is a popular optimization method for solving binary optimization problems. However, its efficiency highly depends on the parameters of the algorithm. In this study the success history adaptation (SHA) mechanism is applied to genetic algorithm to improve its performance. The SHA method was originally proposed for another class of evolutionary algorithms, namely differential evolution (DE). The application of DE’s adaptation mechanisms for genetic algorithm allowed significant improvement of GA performance when solving different types of problems including binary optimization problems and continuous optimization problems. For comparison, in this study, a self-configured genetic algorithm is implemented, in which the adaptive mechanisms for probabilities of choosing one of three selection, three crossover and three mutation types are implemented. The comparison was performed on the set of functions, presented at the Congress on Evolutionary Computation for numerical optimization in 2017. The results demonstrate that the developed SHAGA algorithm outperforms the self-configuring GA on binary problems and the continuous version of SHAGA is competetive against other methods, which proves the importance of the presented modification.