The generation of complex entities with advantageous behaviours beyond our manual design capability requires long-term incremental evolution with continuing emergence. This thesis presents the argument that artificial selection models, such as traditional genetic algorithms, are fundamentally inadequate for this goal. Existing natural selection systems are evaluated, revealing both significant achievements and pitfalls. Thus, some requirements for the perpetuation of evolutionary emergence are established. An (artificial) environment containing simple virtual autonomous organisms with neural controllers has been created to satisfy these requirements and to aid in the development of an accompanying theory of evolutionary emergence. Resulting behaviours are reported alongside their neural correlates. In one example, the collective behaviour of one species provides a selective force which is overcome by another species, demonstrating the incremental evolutionary emergence of advantageous behaviours via naturally-arising coevolution. Further behavioural or neural analysis is infeasible in this environment, so evolutionary statistical methods are employed and extended in order to classify the evolutionary dynamics. This qualitative analysis indicates that evolution is unbounded in the system. As well as validating the theory behind it, work with the system has provided some useful lessons and directions towards the evolution of increasingly complex advantageous behaviours.