In this thesis, we present new and fundamental work on Evolvable Hardware motivated by the insight that Evolvable Hardware needs to compensate for events with different change rates. To solve the challenge of different adaptation speeds, we propose a unified adaptation approach based on multi-objective evolution, evolving and propagating candidate solutions that are diverse in objectives that may experience radical changes.
Focusing on algorithmic aspects, we enable Cartesian Genetic Programming (CGP) model, which we are using to encode Boolean circuits, for multi-objective optimization by introducing a meaningful recombination operator. We improve the scalability of CGP by objectives scaling, periodization of local- and global-search algorithms, and the automatic acquisition and reuse of subfunctions using age- and cone-based techniques. We validate our methods on the applications of adaptation of hardware classifiers to resource changes, recognition of muscular signals for prosthesis control and optimization of processor caches.