The aim of this thesis is to develop a comprehensive set of components for an evolutionary tool intended to serve as a foundation for a fuzzer or optimizer, providing all the necessary tools to generate and evolve solutions to a problem presented by the user, by studying self-adaptation and developing a self-adaptive evolutionary algorithm. This algorithm differs from a non-self-adapting vanilla evolutionary algorithm for the ability to allow individuals to die of old age, the existence of an elitist subset of individuals with high fitness, the option to tweak the reward given to operators, and the ability to behave differently according to the current state of the evolution.