Inductive learning of mutation step-size in evolutionary parameter optimization

The problem of setting the mutation step-size for real-coded evolutionary algorithms has received different answers: exogenous rules like the 1/5 rule, or endogenous factor like the self-adaptation of the step-size in the Gaussian mutation of modern Evolution Strategies. On the other hand, in the bitstring framework, the control of both crossover and mutation by means of Inductive Leaning has proven beneficial to evolution, mostly by recognizing — and forbidding — past errors (i.e. crossover or mutations leading to offspring that will not survive next selection step). This Inductive Learning-based control is transposed to the control of mutation step-size in evolutionary parameter optimization, and the resulting algorithm is experimentally compared to the self-adaptive step-size of Evolution Strategies.