Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming for Multiple Class Object Detection
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
New crossover operators in linear genetic programming for multiclass object classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Parallel linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
Local search in parallel linear genetic programming for multiclass classification
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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Multi-class object classification is an important field of research in computer vision. In this paper basic linear genetic programming is modified to be more suitable for multi-class classification and its performance is then compared to tree-based genetic programming. The directed acyclic graph nature of linear genetic programming is exploited. The existing fitness function is modified to more accurately approximate the true feature space. The results show that the new linear genetic programming approach outperforms the basic tree-based genetic programming approach on all the tasks investigated here and that the new fitness function leads to better and more consistent results. The genetic programs evolved by the new linear genetic programming system are also more comprehensible than those evolved by the tree-based system.