Backpropagation works by doing a forward pass, comparing the network's output with the desired output, then sending a signal backwards using calculus that allows the network to compute simultaneously for all connections whether a small change would help or hurt, then adjusting all weights in parallel by small amounts—a far more efficient method than the mutation/evolutionary approach of perturbing one weight at a time.