Evolvable hardware (EHW) uses simulated evolution to generate an electronic circuit with specific characteristics on reconfigurable hardware, such as field programmable gate arrays (FPGAs). Adaptation, fault resistance and utilisation of the physical properties of the hardware are key properties provided by EHW, and as such, EHW is ideally suited to applications embedded in uncertain environments, where the circuit function cannot be known a priori by the designer. EHW has not scaled well with increases in problem complexity, largely due to the use of direct encodings, in which the chromosome directly represents the device's configuration. This book investigates how biological growth mechanisms can be modelled and adapted to EHW for generating large and complex electronic circuits on FPGAs, without requiring architectural details to be abstracted away. Experiments were conducted which show that this morphogenetic approach to EHW scales well to increases in circuit size and complexity. An analysis of the informational requirements for EHW is also provided. This book should be of interest to researchers in adaptive hardware systems, AI, artificial life and autonomous robotics.