Abstract
This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering both standard DE and modern DE based algorithms, recently proposed in the literature. Available numerical results show that the proposed approach seems to be very promising for some fitness landscapes and still competitive with modern algorithms in other cases. In most cases analyzed the proposed self-adaptation is beneficial in terms of algorithmic performance and can be considered a useful tool for enhancing the performance of a DE scheme.