A Real-Time Evolutionary Object Recognition System
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
IEEE Transactions on Evolutionary Computation
New crossover operators in linear genetic programming for multiclass object classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving object detectors with a GPU accelerated vision system
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Genetic Programming and Evolvable Machines
Expert Systems with Applications: An International Journal
Parallel linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Towards automated learning of object detectors
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
Modeling global temperature changes with genetic programming
Computers & Mathematics with Applications
Pattern-guided genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A feature construction method for general object recognition
Pattern Recognition
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In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. The training coevolves feature extraction procedures, each being a sequence of elementary image processing and computer vision operations applied to input images. Extensive experimental results show that the approach attains competitive performance for three-dimensional object recognition in real synthetic aperture radar imagery.