However, the mutation rate was independent of the size of the population. However, for multimodal functions an self adaptation of the mutation rate could be useful. 5.1 Real valued mutation Figure 1 shows possible mutations for an real valued individual in two dimensions. The size of the mutation step is usually difficult to choose. The optimal step size depends on the problem considered and may even vary during the optimization process.
Small steps are often successful, but sometimes bigger steps are quicker.
This mutation algorithm is able to generate most points in the hypercube defined by the variables of the individual and range of the mutation. For every individual the variable value to change is chosen uniform at random. Table 1 shows an example of a binary mutation for an individual with 11 variables, variable 4 is mutated. The Genetic and Evolutionary Algorithm Toolbox is not public domain .