Better hardware and algorithms have made plasma-physics particle-in-cell codes much faster. Instead of running individual simulations, it's now common to explore the space of physical parameters with large sets of simulations. However, predefined regularly spaced parameter scans can be inefficient and expensive. Instead, we use an adaptive algorithm that learns from previous simulations and determines the most promising parameters to try next. We illustrate this method on the problem of electron injection in laser-wakefield acceleration. Using hundreds of GPU-powered simulations with the code FBPIC on the Titan cluster at ORNL, the algorithm quickly focuses on the most relevant regions of the explored parameter space.