Looking for Prototypes by Genetic Programming

Abstract

Abstract. In this paper we propose a new genetic programming based approach for prototype generation in Pattern Recognition problems. Prototypes consist of mathematical expressions and are encoded as derivation trees. The devised system is able to cope with classification problems in which the number of prototypes is not a priori known. The approach has been tested on several problems and the results compared with those obtained by other genetic programming based approaches previously proposed.

Dynamic population variation in genetic programming

“…Finally, we investigate the difference of the mean of high performing individuals between two consecutive generations as defined by (9). We consider the 90% mark as the cut-off for the high performing (HP) individuals.…”

“…genetic programming (GP), which is an automatic domain-independent method, has addressed such questions with a good measure of success. It has been applied in various branches of engineering and sciences including biomedical science [17,19,21,35,44,47,53], classification tasks [14,27,31,49,50,54,55,57], navigation tasks [1,4,36], image processing and pattern recognition [9,10,22,28,57], neural networks [1,7,36,39] and robotics [23,32,33,40], and in many other various applications and disciplines [8,12,18,20,34,40,41,45,46,51,56], to name but a few. However, one of the main drawbacks of GP has been the often large amount of computational effort required to solve complex problems.…”