The authors describe the combination of fuzzy neural networks with genetic algorithms, producing a flexible and powerful learning paradigm, called evolutive learning. Evolutive learning combines as complementary tools both inductive learning through synaptic weight adjustment and deductive learning through the modification of the network topology to obtain the automatic adaptation of system knowledge to the problem domain environment. Algorithms for the development of an evolutive learning machine are presented. A fuzzy criterion based on entropy is proposed to select the architecture for a fuzzy neural network best suited to a specific problem domain. >