Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
Genetic Programming for Feature Discovery and Image Discrimination
Proceedings of the 5th International Conference on Genetic Algorithms
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
Information Retrieval
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
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This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems. In this approach, pixel statistics are used to form the feature terminals and a random generator produces numeric terminals. The four arithmetic operators and a conditional operator form the function set and the classification accuracy is used as the fitness function. In particular, 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. This method is compared with the basic GP method on four image data sets with object classification problems of increasing difficulty. The results show that the new method outperformed the basic GP method on all cases in both classification accuracy and training time, suggesting that the GP method with the gradient descent search is more effective and more efficient than without on object classification problems.