||According to [Banzhaf et al. 2006], bio-inspired optimization algorithms could be improved by incorporating knowledge from molecular and evolutionary biology. A promising source of advances in optimization is one of the important phenomena in evolutionary biology : the dynamic evolution of the genome structure. Several studies showed for instance that an evolvable genome structure allows evolution to modify the effects that evolution operators (e.g., mutations) have on individuals, a phenomenon known as evolution of evolution [Hindré et al. 2012]. My thesis takes place within the european project EvoEvo (FP7 funding, http ://evoevo.eu/, ICT-610427) and aims to take advantage of evolution of evolution mechanisms to achieve data mining tasks on dynamic data sets.