For large-scale optimization problems, such as those involving matrices with dimension in the order of hundreds of thousands, traditional approaches like exhaustive search or standard MILP algorithms are impractical due to their computational cost. Instead, here we use genetic algorithms, a type of randomized algorithms to efficiently search a solution that is close to the optimal one. By conducting selection, mutation, and combination operations inspired by natural selection, the solutions evolve to an optimal or near optimal state in an efficient way. Specifically, we use the Pareto optimization with recombination for subset selection (PORSS) proposed by Qian et al. . The pseudo-code for the algorithm is presented in Section S5 in the SI and the full code is available at the GitHub repository provided at the end of this paper. The algorithm accepts three primary inputs: a detection matrix D, a sensor budget k, and an objective function g 1 which is the detection coverage in this case.