Genetic algorithms (GAs) usually suffer from the so-called genetic-drift effect, which reduces the genetic variability within the evolving population making the algorithm converge toward a local minimum of the objective function. We have developed an innovative method to attenuate such a genetic-drift effect that we named the drift-avoidance GA (DAGA). The implemented method combines some principles of niched GAs (NGAs), catastrophic GAs, crowding GAs, and the Monte Carlo algorithm (MCA) with the aim of maintaining an optimal genetic diversity within the evolving population, thus avoiding premature convergence. The DAGA performance is first tested on different analytic objective functions often used to test optimization algorithms. In this case, the implemented DAGA approach is compared with standard GAs, catastrophic GAs, crowding GAs, NGAs, and MCA. Then, the DAGA and the NGAs approaches are compared on two well-known nonlinear geophysical optimization problems characterized by objective functions with complex topologies: residual statics corrections and 2D acoustic full-waveform inversion. To draw general conclusions, we limit the attention to synthetic seismic optimizations. Our tests prove that the DAGA approach grants the convergence in case of objective functions with very complex topologies, where other GA implementations (such as standard GAs or NGAs) fail to converge. Differently, in case of simpler topologies, DAGA achieves similar performances with the other GA implementations considered. The DAGA approach may have a slightly higher or lower computational cost than standard GA or NGA methods, depending on its convergence speed, that is, on its ability to reduce the number of forward modelings with respect to the other methods.