Summary: In knowledge discovery, genetic algorithms have been used for classification, model selection and other optimization tasks. However, behavior and performance of genetic algorithm are directly affected by the values of their input parameters, while poor parameter settings usually lead to several problems such as the premature convergence. Adaptive techniques have been suggested for adjusting the parameters in the process of running the genetic algorithms. None of these techniques have yet shown a significant overall improvement, since most of them remain domain-specific. In this paper, we attempt to improve the performance of genetic algorithms by providing a new feature – Lifetime. We use a Fuzzy Logic Controller to adapt the crossover probabilities and mutation probability as a function of the chromosomes age. This approach should enhance the exploration and exploitation capabilities of the algorithm, while reducing its rate of premature convergence. We have evaluated the proposed methodology on some benchmark problems by comparing its performance to the basic and adaptive genetic algorithms. The simulation results demonstrate a clear advantage of the proposed method over other adaptive techniques at the aspect of overcoming premature convergence problem.