Escaping large deceptive basins of attraction with heavy-tailed mutation operators
Tobias Friedrich, Francesco Quinzan, Markus Wagner · Proceedings of the Genetic and Evolutionary Computation Conference · 2018
In many evolutionary algorithms (EAs), a parameter that needs to be tuned is that of the mutation rate, which determines the probability for each decision variable to be mutated. Typically, this rate is set to 1/n for the duration of the optimization, where n is the number of decision variables. This setting has the appeal that the expected number of mutated variables per iteration is one.