Evolutionary learning has been developing rapidly in the last decade. It is a powerful and general learning approach which has been used successfully in both symbolic systems, e.g., rule-based systems; and subsymbolic systems, e.g., artificial neural networks. However, most evolutionary learning systems have paid little attention to the fact that they are population-based learning. The common practice is to select the best individual in the last generation as the final learned system. Such practice in essence treats these learning systems as optimisation ones. This paper emphasises the difference between a learning system and an optimisation one, and shows that such difference requires a different approach to population-based learning and that the current practice of selecting the best individual as the learned system is not the best choice. The paper then argues that a population contains more information than the best individual and thus should be used as the final learned system. Two examples are presented in this paper to show that even some simple methods which make full use of a population can improve the performance of a learned system greatly. The first example is in the subsymbolic domain of artificial neural networks. The second example is in the symbolic domain of rule-based systems.