the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ‘new’ and ‘comment’ mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This model firstly proposes a five-tuple to represent three types of topics: independent, competitive and mutually exclusive. Furthermore, it adopts mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content.A series of experiments tested on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other while mutually exclusive topics significantly suppress the diffusion processes of each other; 2) lower mutation probability leads to decreasing of final information amount. The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks. the existing information diffusion models focus on analyzing the spatial distribution of certain pieces of messages in social networks. However, these conventional models ignored another important characteristic of diffusion: gradually changing of message contents due to the ’new’ and ’comment’ mechanisms. A novel genetic-algorithm-based information evolution model is proposed to reproduce both the diffusion and development process of information in social networks. This, taking mutation operator and forms new crossover and mutation rules to simulate four typical interactions between individuals, which bring the advantage of reproducing the information evolution process in both popularity and content. A series of scientific tests on public datasets demonstrate that: 1) independent and competitive topics of information rarely affect each other whil The experimental results show that our evolution model is more reasonable and feasible in demonstrating the evolution of information in social networks.