This thesis argues that natural complex systems can provide an inspiring example for creating software which incorporates emergent, self-organizing and adaptive properties. The advantages of complex sys- tems are their natural resilience, redundancy and adaptivity. A generalization of neural networks and boolean networks called computational networks is presented as a model for complex systems. It is argued that this model satisfies the required properties for modeling complex systems. Furthermore, it is asserted that a computational network, being a network of mathematical functions, is appropriate for solving classification problems. For the design of computational networks an evolutionary design algorithm is constructed. Additionally, four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. An impor- tant component is a novel generative representation which can reuse substructures of computational networks. Experiments with this component have shown that it facilitates a higher level of complexity in the solution space, improving the computational network performance for more complex problems. Other components steer the evolutionary process towards a desired solution, either by introducing spe- cial stages during evolution, or by smoothing the fitness landscape. The experiments show that complex systems can be evolutionary designed to act as a classifier. The resulting computational network has a better performance on the Iris dataset compared to every classifier in the Weka classifier collection. Furthermore, an experiment was conducted using the TIMIT read speech dataset, the classifier was evo- lutionary designed using only 13 MFCC features, and a very small train set. Although the performance is not good enough to be of any practical use, the results are adequate given the limitations of the train data.