The (1+ λ ) evolutionary algorithm with self-adjusting mutation rate
Benjamin Doerr, Christian Gießen, Carsten Witt, Jing Yang · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
We propose a new way to self-adjust the mutation rate in population-based evolutionary algorithms. Roughly speaking, it consists of creating half the offspring with a mutation rate that is twice the current mutation rate and the other half with half the current rate. The mutation rate is then updated to the rate used in that subpopulation which contains the best offspring.