The manual design of adaptive controllers for robotic systems that face unpredictable environmental changes is often challenging. There is thus a growing interest in the development of automatic design tools to assist control engineers. One of the most common approaches in this domain is the evolutionary synthesis of Artificial Neural Networks (ANNs), which allows the automatic design of control systems with little or no human intervention. The performance of evolved neural controllers, however, critically depends on the choice of a suitable genetic representation, i.e., a description of the ANN on which the evolutionary search can operate effectively. In this thesis, I propose an alternative to existing representations for neural controllers based on an extension of Analog Genetic Encoding (AGE), an abstraction of the regulatory networks that control the expression of genes in biological organisms. The proposed approach unites three characteristics that have separately been considered advantageous, but have so far not been aggregated into a single representation: (1) it can be used to effectively synthesize the topology, weights, and numerical parameters of arbitrary networks; (2) it permits the use of heterogeneous ANN models that feature multiple types of signals; and (3) it facilitates the evolution of modular controllers. The results of a series of experiments demonstrate the advantages of these characteristics: (i) neural architectures synthesized with the proposed method display a performance competitive to the best hand-designed networks and networks synthesized with other representations; (ii) in tasks that feature an unpredictable changing environment, controllers with a heterogeneous ANN model outperform controllers with conventional, homogeneous ANN models; and (iii) in tasks that feature multiple modular sub-problems, the proposed representation allows the automatic decomposition of the problem, leading to a substantial improvement in the performance of the evolved controllers. In summary, the proposed representation in combination with a heterogeneous ANN model and an evolutionary algorithm represents a promising method for the automatic synthesis of adaptive control systems for robots.