Crossover controlling the number of crossed genes (CCG) significantly improves the search performance of multi-objective evolutionary algorithms (MOEAs) in many-objective optimization problems (MaOPs). CCG controls the number of crossed genes by using a static parameter α. To achieve high search performance by using the static CCG, we have to find out an appropriate parameter α^* by conducting many experiments. To avoid time consuming parameter tuning and find out an appropriate α^* in a single run of the algorithm, in this work we propose a self-adaptive CCG which dynamically controls the parameter α during the solutions search in a single run of the algorithm. Through experiments using many-objective 0/1 knapsack problems, we show that the values of α controlled by the self-adaptive CCG is converged to an appropriate value even when the self-adaptation is started from any initial values. Also, we show the self-adaptive CCG achieves 80~90% with a single run of the algorithm for the maximum search performance obtained by the static CCG using an optimal α^*.