Genetic Programming with Gradient Descent Search for Multiclass Object Classification

This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems. Gradient descent search is introduced to the GP mechanism and is embedded into the genetic beam search, which allows the evolutionary learning process to globally follow the beam search and locally follow the gradient descent search. Two different methods, an online gradient descent scheme and an offline gradient descent scheme, are developed and compared with the basic GP method on three image data sets with object classification problems of increasing difficulty. The results show that both the online and the offline gradient descent GP methods outperform the basic GP method in both classification accuracy and training time and that the online scheme achieved better performance than the offline scheme.