Lazy learning is a general learning principle in which models are constructed from a database of cases on an ‘as needed’ basis. Methods include instance-based approaches like
nearest neighbour and case based reasoning. Another learning paradigm with a lot of commonality is competitive learning, in which populations of units or high-order modules compete for attention to adapt or respond (produce system output). This work reviews these two general learning paradigms and proposes the clonal selection approach as possessing aspects of laziness whilst being strongly competitive.