针对基本遗传算法中的早熟问题和因种内竞争而带来的“封闭竞争”问题,提出了一种改进的遗传算法。该算法增加了群体多样性,引入了相似度概念,并应用于种间竞争。其收敛性分析表明:与基本遗传算法相比,该算法收敛于全局最优解的概率为1,具有更好的收敛性。并且仿真分析表明:该改进遗传算法与基本遗传算法相比,在BTC译码上改善了约0.4dB的净编码增益。因而所提出的改进遗传算法在译码性能上具有更好的优越性,更适用于FEC译码技术。 Aiming at the problem of precocity in basic genetic algorithm and the issue of “closed competition” brought by intra-species competition, an improved genetic algorithm is proposed. The algorithm increases the population diversity, introduces the concept of similarity, and applies to interspecific competition. The convergence analysis shows that compared with the basic genetic algorithm, the algorithm converges to the global optimal solution with a probability of 1 and has better convergence. And the simulation results show that this improved genetic algorithm improves the net coding gain of about 0.4dB in BTC decoding compared with the basic genetic algorithm. Therefore, the improved genetic algorithm proposed in the decoding performance has better superiority, more suitable for FEC decoding technology.