Recent research has demonstrated that no search algorithm is better than any other one when performance is averaged over all possible discrete problems. Hybridization (incorporation of problem-knowledge) is required to produce adequate problem-specific algorithms. This work explores the power of hybridization in the context of evolutionary algorithms. For this purpose, a framework for describing adaptive systems is presented. It is shown that, when hybridized, adaptive techniques are computationally complete systems with Turing capabilities. Moreover, evolutionary algorithms can be regarded as a kind of nondeterministic Turing machines.