SIGA: a novel self-adaptive immune genetic algorithm This paper proposes a novel self-adaptive genetic algorithm SIGA (Self-adaptive Immune Genetic Algorithm) based on immunity to overcome the shortage of traditional genetic algorithms that the converging speed is slow and the solution is a local optimum. The algorithm improves the genetic operators and proposes self-adaptive crossover and mutation operators in case of keeping individual diversity and avoiding prematurity. An immune selection algorithm based on selection probability of similarity and vector distance in order to keep individual diversity and improve the level of fitness is proposed. The results of the experiments indicate that SIGA can improve the converging speed by three to ninety times, enhance the precision which reaches to $10^{-3}$, and avoid prematurity to some extent ones compared with traditional genetic algorithms and immune algorithms.