Mutations are random disturbances to ensure that all regions of the solution space are accessible during the search. A point in the solution space should in principle be reachable from any other point thanks to mutations (and its combination with crossover). However, in conventional GAs, mutations should not be too strong in order not to scatter promising features out of their optimal regions as long as the search improves. Mutations are therefore usually seen as rather local changes to exploit vicinities of current solutions, whereas larger moves (explorations) are driven by crossovers. Dealing with a real-valued search space, an instinctive choice for mutations is the addition of Gaussian noise 79 that mutates an individual Θ i like Θi = Θ i + P(pm, K) • σ(N1(0, 1), ..., NK (0, 1)) (3) where • denotes the element-wise multiplication between vectors. P(pm, K) is a vector of size K filled with 0 or 1 that selects genes to be mutated with probability pm. For pm = 1, all genes are mutated, while pm = 0 turns off the mutation. Selected genes are consequently modified with independent samples from the standard normal distribution N (0, 1) scaled with the parameter σ that controls the mutation strength, along with pm.